受賞記念講演

10 月 23 日（月）/ October 23 (Mon)
瑞雲・平安 17:30 ～ 19:30

若手奨励賞
01

FMO データを用いた機械学習モデルの開発
Development of machine learning models using FMO data
加藤 幸一郎Koichiro Kato
九州大学

大学院工学研究院

応用化学部門Department of Applied Chemistry, Graduate School of Engineering, Kyushu University

02

AI で広がる分子設計の可能性
AI Expands Potential of Molecular Design
大上 雅史

Masahiro Ohue

東京工業大学Tokyo Institute of Technology

Oxford Journals - Japanese Society for Bioinformatics Prize 202203

生体分子設計のインフォマティクス

Informatics for biomolecule design
齋藤 裕

Yutaka Saito
産業技術総合研究所人工知能研究センター

Artiﬁcial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST)― 1―

ワークショップ

WS01

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10 月 23 日（月）/ October 23 (Mon)
福寿 13:00 ～ 17:00

計算科学的手法による毒性予測手法開発に関する最新動向と今後について

Latest Trends and Future Development of Toxicity Prediction Methods Using
Computational Science
主催：奈良先端科学技術大学院大学・データ駆動型サイエンス創造センター
協賛：計算 ADMET 研究会
モデレーター：船津 公人（奈良先端科学技術大学院大学）、庄野 文章（奈良先端科学技術大学院大学）、
植沢 芳広（明治薬科大学）
Kimito Funatsu (Nara institute of science and technology),
Fumiaki Shono (Nara institute of science and technology),
Yoshihiro Uesawa (Meiji Pharamaceutical University)

ご挨拶
Message

内野 絵里香Erika Uchino
経済産業省 製造産業局 化学物質管理課 化学物質リスク評価企画官

Director, Chemical Safety Oﬃce, Director for Chemical Risk assessment, Chemical Management Policy Division,
Manufacturing Industries Bureau, Ministry of Economy and Industry

WS01-01

本ワークショップの開催目的と意義について

The purpose and significance of this workshop
船津 公人

Kimito Funatsu

奈良先端科学技術大学院大学

Nara Institute of Science and Technology

WS01-02

有害性評価支援システム統合プラットフォーム（HESS）及び OECD QSAR Toolbox について

Hazard Evaluation Support System Integrated Platform (HESS) and OECD QSAR Toolbox
櫻谷 祐企

Yuki Sakuratani( 独）製品評価技術基盤機構
National Institute of Technology and Evaluation

WS01-03

Alvascience ソフトウェアスイートを用いた毒性・生物活性予測
Toxicity and biological activity prediction using Alvascience software suite
田坂 友彦

Tomohiko Tasaka

株式会社アフィニティサイエンスAﬃnity Science Corp.

WS01-04

毒性・安全性評価を支える in sillico ソリューションの紹介

Introduction of in sillico solutions to support toxicity and safety assessment
今西 浩之

Hiroyuki Imanishi

インフォコム株式会社

INFOCOM CORPORATION― 2―

WS01-05

Accurately and rapidly assesses the toxicity of chemicals by TOPKAT based solely on their 2D
molecular structure.
Anand Krishnamurthy
バイオビア , ダッソーシステムズ株式会社
BIOVIA, Dassault Systemes

WS01-06

Data Driven Federated Learning: Application to toxicity prediction.
Thierry Hanser
Lhasa Limited

WS01-07

説明可能 AI 技術による ADMET 予測の試み

Attempts to predict ADMET using explainable AI technology
吉田 琢哉

Takuya Yoshida

富士通株式会社

Fujitsu Limited

ソーシャルソリューション事業本部

Healthy Living ライフサイエンス事業部― 3―チュートリアルTS01

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10 月 23 日（月）/ October 23 (Mon)
307 13:00 ～ 17:00

ケモインフォマティクス入門

Introduction to Chemoinformatics
モデレーター：大川 和史（旭化成ファーマ株式会社）、芹沢 貴之（第一三共株式会社）、
高橋 一敏（味の素株式会社）、宮野 奈津美（帝人ファーマ株式会社）
Kazufumi Ohkawa (Asahi Kasei Pharma), Takayuki Serizawa (DAIICHI SANKYO COMPANY, LIMITED),
Kazutoshi Takahashi (AJINOMOTO CO., INC.), Natsumi Miyano (TEIJIN PHARMA LIMITED.)

TS02

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10 月 23 日（月）/ October 23 (Mon)401 13:00 ～ 17:00

第 32 回 FMO 研究会 FMODB 実践チュートリアル- 生体高分子の認識機構の FMO 法による解析：FMO 計算初心者から中級者に向けて

32nd FMO seminar, FMO Database tutorial
モデレーター：高谷 大輔（大阪大学大学院薬学研究科）、渡邉 千鶴（国立研究開発法人理化学研究所）、
加藤 幸一郎（九州大学）
Daisuke Takaya (Graduate School of Pharmaceutical Science, Osaka University ),Chiduru Watanabe (RIKEN), Koichiro Kato (Kyushu University)

TS02-01

FMODB の紹介とデータ活用方法
Introduction to FMODB and how to use the data
渡邉 千鶴

Chiduru Watanabe

国立研究開発法人理化学研究所

RIKEN

TS02-02

FMODB を活用したリガンド – タンパク質間の動的平均相互作用解析
Dynamically Averaged Interaction Energy Analysis between Ligand and Protein using FMODB
松本 大夢

Hiromu Matsumoto

九州大学

Kyushu University

― 4―

TS03

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10 月 23 日（月）/ October 23 (Mon)
研修室 13:00 ～ 17:00

リアルワールド医療データソン

RWD (real-world data) Medical Datathon
モデレーター：茂櫛 薫（cBioinformatics 株式会社 / 東京医科歯科大学統合教育機構）、
荻島 創一（東北大学未来型医療創成センター / 東北メディカル・メガバンク機構）
Kaoru Mogushi (cBioinformatics Inc. / Institute of Education, Tokyo Medical and Dental University)
Soichi Ogishima (INGEM / Tohoku Medical Megabank Organization, Tohoku University)― 5―

大会長講演CL

10 月 24 日（火）/ October 24 (Tue)大ホール 10:10 ～ 10:40

大会長講演

Conference Chairperson LectureCL

東北メディカル・メガバンク計画と個別化ヘルスケアの推進

Tohoku Medical Megabank project and Promotion of Personalized Healthcare
山本 雅之

Masayuki Yamamoto

東北大学東北メディカル・メガバンク機構

Tohoku University Tohoku Medical Megabank Organization― 6―

プレナリー講演PL01

10 月 24 日（火）/ October 24 (Tue)大ホール 10:40 ～ 12:00

大規模データが切り拓く次世代ヘルスケア

Large-scale data for next-generation healthcare
座長：山本 雅之（東北大学東北メディカル・メガバンク機構）
Masayuki Yamamoto (Tohoku Medical Megabank Organization)

PL01-01

データシェアリングによる医療課題の克服と展望：新型コロナからの教訓

Tackling with medical R&D gap by data sharing: A lesson from COVID-19
末松 誠

Makoto Suematsu
慶應義塾大学 / 実験動物中央研究所

Keio University/Central Institute for Experimental Animals

PL01-02

デジタルトラストでつなぐ日本と医療の未来 - 次の世代へと医療のバトンを渡すために

Connecting Japan and the Future of Medicine with Digital Trust - Passing the Baton of
Healthcare to the Next Generation
堤 浩幸

Hiroyuki Tsutsumi
富士通

Fujitsu

PL01-03

モデルナ x デジタルの最前線

Moderna x Digital Frontiers
鈴木 蘭美

Rami Suzuki

モデルナ・ジャパン

Moderna Japan

― 7―

招待講演

IL01

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10 月 24 日（火）/ October 24 (Tue)大ホール 13:30 ～ 15:00

ポスト AI 時代の創薬のための構造計算生物学

Structural Computational Biology for Drug Discovery in the Post-AI Era
座長：井上 豪（大阪大学 大学院薬学研究科）、
木下 賢吾（東北大学情報科学研究科 / 東北メディカル・メガバンク機構）
Tsuyoshi Inoue (Graduate School of Pharmaceutical Science, Osaka University),
Kengo Kinoshita (Tohoku University Tohoku Medical Megabank Organization)

IL01-01

AMED-BINDS によるアカデミア創薬に関する連携支援体制の構築
Establishment of a collaborative support system for drug discovery in academia by AMEDBINDS
井上 豪

Tsuyoshi Inoue
大阪大学 大学院薬学研究科

Graduate School of Pharmaceutical Science, Osaka University

IL01-02

AlphaFold を振り返る
Looking back at AlphaFold桜庭 俊

Shun Sakuraba
量子科学技術研究開発機構

National Institutes for Quantum Science and Technology

IL01-03

機械学習を実際に創薬への適用による効率化と克服すべき課題について

Efficiency Improvements in Drug Discovery through Machine Learning and the Challenges toOvercome
関嶋 政和

Masakazu Sekijima

東京工業大学Tokyo Institute of Technology

IL01-04

AI 時代の分子シミュレーション
Molecular Simulation in the AI Era
池口 満徳

Mitsunori Ikeguchi

横浜市立大学生命医科学研究科 / 理化学研究所計算科学研究センター

Yokohama City University/RIKEN Center for Computational ScienceIL01-05

ポスト AI 時代の構造生物学と創薬研究の今後の展望

Prospects for Structural Biology and Drug Development in the Post-AI Era
木下 賢吾

Kengo Kinoshita

東北大学情報科学研究科 / 東北メディカル・メガバンク機構

Tohoku University Tohoku Medical Megabank Organization― 8―

シンポジウムS01

10 月 24 日（火）/ October 24 (Tue)大ホール 17:10 ～ 18:40

若手研究者による AMED/BINDS インシリコ解析ユニットにおける創薬支援・高度化研究

Research activities of young researchers for supporting drug discovery byin-silico unit of BINDS (AMED)
モデレーター：広川 貴次（国立大学法人筑波大学）
Takatsugu Hirokawa (University of Tsukuba)

S01-01

マルコフ状態遷移モデルを用いた上皮成長因子受容体キナーゼの活性化メカニズムの研究

A study of activation mechanism of epidermal growth factor receptor kinase using Markov state
model
井上 雅郎

Masao Inoue

横浜市大・院生命医科学

Grad. Sch. Med. Life Sci., Yokohama City Univ.

S01-02

NSD2 変異体によるヌクレオソーム H3K36 メチル化異常亢進のメカニズム
Understanding the Mechanism of NSD2 mutation induced H3K36 Hypermethylation
Kumar Amarjeet
量子科学技術研究開発機構 量子生命科学研究所

Insitute for Quantum Life Science, Institutes for Quantum Science and Technology

S01-03

FMO 法に基づく創薬支援・情報基盤技術の開発
Development of drug discovery support and information basic technology based on the FMO
method渡邉 千鶴

Chiduru Watanabe

国立研究開発法人 理化学研究所

RIKEN

S01-04AlphaFold2 を活用した分子設計
Molecular Design with AlphaFold2
大上 雅史

Masahito Ohue

東京工業大学Tokyo Institute of Technology― 9―ランチョンセミナー

LS01

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10 月 24 日（火）/ October 24 (Tue)
研修室 12:15 ～ 13:15

CLARITY PV：医薬品安全性監視のためのトランスレーショナル・プラットフォーム

CLARITY PV: Translational platform for drug safety surveillance
協賛：株式会社モルシス
モデレーター：池上 貴史（株式会社モルシス）、東田 欣也（株式会社モルシス）、狩野 敦（株式会社モルシス）
Takashi Ikegami (MOLSIS Inc.), Kinya Toda (MOLSIS Inc.), Atsushi Kanou (MOLSIS Inc.)

LS01-01

医薬品の安全性シグナルの早期予測 :オフターゲット薬理予測から市販後データの機械学習モデルまで

Early Anticipation of Drug Safety Signals:
from Off-Target Pharmacology Prediction to Machine Learning Models of Post-Marketing Data
Jordi Mestres
ケモターゲッツ合同会社

Chemotargets S.L.

LS02

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10 月 24 日（火）/ October 24 (Tue)401 12:15 ～ 13:15

Schrödinger の提供する総合創薬支援サービスの概要：その技術的背景を中心にOverview of Schrödinger's Comprehensive Drug Discovery Support Services:
Focused on the Technical Background
協賛：シュレーディンガー株式会社
モデレーター：市原 収（シュレーディンガー株式会社）Osamu Ichihara (Schrödinger KK)― 10 ―

LS03

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10 月 24 日（火）/ October 24 (Tue)407 12:15 ～ 13:15

Dotmatics Luma：
データ統合、分析、AI を１つのプラットフォームで実現する革新的ソリューション

Dotmatics Luma：
Innovative solutions for data integration, analytics, and AI on a single platform
協賛：ドットマティクス株式会社
モデレーター：北村 博幸（ドットマティクス株式会社）
Hiroyuki Kitamura (dotmatics K.K.)

LS03-01

福山 隆

Takashi Fukuyama
ドットマティクス株式会社

dotmatics K.K.

LS03-02

Zsolt Lepp
Zsolt Lepp
ドットマティクス株式会社

dotmatics K.K.

― 11 ―

スポンサードセッション

SS01

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10 月 24 日（火）/ October 24 (Tue)
桃源 13:30 ～ 15:00

ゲノム科学領域における最新の AWS 活用〜基礎生物学研究所のゲノム解析プラットフォーム〜

Latest Applications of AWS in Genome Science: The Genomic Analysis Platform
at the National Institute for Basic Biology
協賛：アマゾン ウェブ サービス ジャパン合同会社
モデレーター：片岡 勇人（アマゾン ウェブ サービス ジャパン合同会社）
Yuto Kataoka (Amazon Web Services Japan G.K.)

SS01-1

ゲノム科学領域でご利用が進む最新の AWS サービス及び活用事例のご紹介

Exploring the Latest AWS Solutions and Use Cases in Genome Science
鳥羽 祐輔

Yusuke Toba

アマゾン ウェブ サービス ジャパン合同会社

Amazon Web Services Japan G.K.

SS01-2ゲノム科学におけるクラウド活用：独占型スパコン構築からデータ共有まで

Cloud Utilization in Genome Science: From Building Own Supercomputers to
Sharing Data
重信 秀治

Shuji Shigenobu

自然科学研究機構 基礎生物学研究所

National Institute for Basic Biology, National Institutes of Natural Sciences

SS02

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10 月 24 日（火）/ October 24 (Tue)
福寿 13:30 ～ 15:00

創薬研究における大規模データ／リアルワールドデータ (RWD) の活用と分析ソリューション

Large-scale data/real-world data (RWD) utilization and analysis solutions for
drug discovery research
協賛：クラリベイト・アナリティクス・ジャパン株式会社
モデレーター：クラリベイト・アナリティクス・ジャパン株式会社
Clarivate

SS02-1

Klein Cecilia
クラリベイト・アナリティクス・ジャパン株式会社

Clarivate― 12 ―

SS03

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10 月 24 日（火）/ October 24 (Tue)
平安 13:30 ～ 15:00

Orion® と数十億規模のリガンドベース、および構造ベース仮想スクリーニングを可能にする技術について

Orion® and Billion Scale Ligand and Structure Based Virtual Screening
協賛：OpenEye, Cadence Molecular Sciences
モデレーター：近田 千春（OpenEye, Cadence Molecular Sciences）
Chiharu Konda (OpenEye, Cadence Molecular Sciences)SS03-1

Orion Small Molecule Discovery Suite についてOrion Small Molecule Discovery Suite Overview
近田 千春

Chiharu Konda

OpenEye, Cadence Molecular SciencesSS03-2

数十億規模のリガンドベース、および構造ベース仮想スクリーニングを可能にする技術について

Billion Scale Ligand and Structure Based Virtual Screening
Mark McGann
OpenEye, Cadence Molecular Sciences

SS04

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10 月 24 日（火）/ October 24 (Tue)
桃源 17:10 ～ 18:40

Iktos における AI 創薬とコラボレーション事例紹介

AI-driven drug discovery at Iktos and successful collaboration cases
協賛：イクトス株式会社
モデレーター：藤 秀義（イクトス株式会社）
Hideyoshi Fuji (Iktos SA)

SS04-1

創薬を加速する Iktos のジェネレーティブ AI 技術の紹介

Iktos Generative AI Technology to Accelerate Drug Design and Discovery
藤 秀義

Hideyoshi Fuji
イクトス株式会社

Iktos SA

SS04-2Iktos 社との共同研究を通した「AI 創薬」の取り組み
Practical efforts on “AI drug discovery” through research collaboration with Iktos
熊澤 啓子

Keiko Kumazawa

帝人ファーマ株式会社

Teijin Pharma Ltd.

― 13 ―

SS04-3

AI 創薬プラットフォーム「Makya」を活用した低分子創薬
Small molecule drug discovery utilizing the AI drug discovery platform 'Makya'
鈴木 亮

Ryo Suzuki
小野薬品工業株式会社

Ono Pharmaceutical Co., Ltd.

SS05

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10 月 24 日（火）/ October 24 (Tue)
福寿 17:10 ～ 18:40

量子コンピューティング技術の発展と、創薬・医療分野における期待

Developments in quantum computing technology and expectations in
the fields of drug discovery, medical, and healthcare
協賛：クオンティニュアム株式会社
モデレーター：平岩 美央里（クオンティニュアム株式会社）
Miori Hiraiwa (Quantinuum K. K.)

SS06

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10 月 24 日（火）/ October 24 (Tue)
平安 17:10 ～ 18:40

最新鋭クライオ電子顕微鏡による新規創薬へのアプローチとその最前線

Approach to novel drug discovery with the cutting-edge cryo-EM
協賛：サーモフィッシャーサイエンティフィック
モデレーター：前田 晋太朗（サーモフィッシャーサイエンティフィック）
Shintaro Maeda (Thermo Fisher Scientific)

SS06-1

前田 晋太朗

Shintaro Maeda
サーモフィッシャーサイエンティフィック

Thermo Fisher Scientiﬁc― 14 ―

スポンサード企画

SW01

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10 月 24 日（火）/ October 24 (Tue)402 13:30 ～ 15:00『創薬支援ソフト myPresto5』活用セミナー

Utilization seminar of a software package 'myPresto5' for drug discovery
主催：一般社団法人バイオ産業情報化コンソーシアム
モデレーター：一般社団法人バイオ産業情報化コンソーシアム
Japan Biological Informatics Consortium

SW01-1

中村 寛則

Hironori Nakamura
株式会社バイオモデリングリサーチ

Biomodeling Research Co., Ltd.

― 15 ―

フォーカストセッション

FS01

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10 月 24 日（火）/ October 24 (Tue)
研修室 13:30 ～ 15:00

計算 ADMET 研究会：毒性・副作用関連研究の新展開

New Frontiers in Toxicity and Adverse-Effect Related Research
モデレーター：植沢 芳広（明治薬科大学）、永堀 博久（住友化学株式会社）
Yoshihiro Uesawa (Meiji Pharmaceutical University), Hirohisa Nagahori (Sumitomo Chemical Co.,Ltd.)

FS01-01

計算 ADMET 研究会（旧・計算毒性学研究会）主査就任の挨拶と今後の展望

Welcome Address by the Newly Appointed Head of the Special Interest Group on Computational
ADMET and Future Prospects
植沢 芳広

Yoshihiro Uesawa

明治薬科大学

Meiji Pharmaceutical University

FS01-02

化学構造から有害事象へ：解釈可能なマルチタスク深層学習による有害事象の発現予測

From Chemical Structure to Adverse Events: Multi-task Models for Adverse Event Occurrencewith Interpretable Deep Learning
小川 慶子

Keiko Ogawa

立命館大学

Ritsumeikan University

FS01-03

機械学習モデルの薬物動態及び毒性分野での創薬利用

Machine learning model for drug discovery in DMPK and TOX
半田 耕一

Koichi Handa

帝人ファーマ（株）動態・安全性研究部

Toxicology & DMPK Research Department, TEIJIN PHARMA LIMITED

FS01-04

分子記述子および分子画像を組み合わせた薬物動態パラメータ予測モデルの構築

Construction of prediction models for pharmacokinetic parameters by combining molecular
images and molecular descriptors
侭田 秀章

Hideaki Mamada

日本たばこ産業株式会社

Japan Tobacco Inc.

― 16 ―

FS02

10 月 24 日（火）/ October 24 (Tue)307 13:30 ～ 15:00

オミックスの原理研究会

Workshop on the Omics Principle
モデレーター：美宅 成樹（名古屋大学名誉教授）、広川 貴次（筑波大学医学医療系 生命医科学域）、
荻島 創一（東北大学高等研究機構未来型医療創成センター / 東北メディカル・メガバンク機構）
Shigeki Mitaku (Nagoya University, Emeritus Professor), Takatsugu Hirokawa (University of Tsukuba),
Soichi Ogishima (Tohoku Medical Megabank Organization/INGEM, Tohoku University)

FS02-01

生命の相図を用いた SARS-CoV-2 ゲノム配列の解析

Analysis of the SARS-CoV-2 genome sequence using the phase diagram of life
美宅 成樹

Shigeki Mitaku

名古屋大学名誉教授

Nagoya University, Emeritus Professor

FS02-02

生体システムにおける化学情報処理過程の理解

Understanding chemical information processing in biological systems
小林 徹也

Tetsuya Kobayashi

東京大学生産技術研究所

Institute of Industrial Science, the University of Tokyo

FS03



10 月 24 日（火）/ October 24 (Tue)
研修室 17:10 ～ 18:40

生成 AI の研究における適用可能性と今後の研究スタイルの全面的な変化

Applicability in generative AI research and great changes in future researchstylesモデレーター：湯田 浩太郎（株式会社インシリコデータ）、植沢 芳広（明治薬科大学医療分子解析学研究室）
Kohtaro Yuta (In Silico Data,Ltd.),
Yoshihiro Uesawa (Department of Medical Molecular Informatics, Meiji Pharmaceutical University)

FS03-01

副作用データベースを用いた毒性発現経路の推定と大規模言語モデルの活用

Estimating Toxicity Pathways with Adverse Effects Databases and Large-Scale Language Models
植沢 芳広

Yoshihiro Uesawa

明治薬科大学医療分子解析学研究室

Department of Medical Molecular Informatics, Meiji Pharmaceutical University

FS03-02

生成モデルを中心とした Elix における AI 創薬

AI drug discovery in Elix centered on generative models
結城 伸哉

Shinya Yuki

株式会社 Elix

Elix, Inc.

― 17 ―

FS03-03

LLM ( 大規模言語モデル ) に限界はあるのか？ AGI ( 汎用人工知能 ) の出現はどのように？未来を探る
Is there a limit to LLM (Large Language Model)? How does AGI (Artificial General Intelligenc)
emerge? Exploring the future
生島 高裕

Takahiro Ikushima

株式会社 数理先端技術研究所

Advanced Mathematical Technology Laboratory Co., Ltd.

FS03-04

自律型（オートノマス）研究への生成 AI の適用性

Applicability of generative AI to autonomous research
湯田 浩太郎Kohtaro Yuta
株式会社インシリコデータ

In Silico Data, Ltd.

FS04



10 月 24 日（火）/ October 24 (Tue)307 17:10 ～ 18:40

AI と XR の融合時代における化合物設計とデータ表示の革新

Practical compound design and data display in the era of AI and XR
モデレーター：池田 和由（慶應義塾大学）、江崎 剛史（滋賀大学）、清水 祐吾（理化学研究所）
Kazuyoshi Ikeda (Keio University), Tsuyoshi Esaki (Shiga University), Yugo Shimizu (RIKEN)

FS04-01

創薬現場で役立つ AI、XAI

AI and XAI for drug discovery research
渡邉 博文

Hirofumi Watanabe

株式会社ウィズメーティス

WithMetis Co., Ltd.

FS04-02

複合現実空間で体験する化学反応ネットワークの対話操作

Exploring Chemical Reaction Network Through Mixed-Reality Interaction
石田 祥一

Shoichi Ishida

横浜市立大学

Yokohama City University

FS04-03

VR latest technologies in Theoretical and Computational Chemistry
Simon Bennie
Nanome, Inc.

― 18 ―

口頭発表

O01



10 月 24 日（火）/ October 24 (Tue)
瑞雲 13:30 ～ 15:00

臨床インフォマティクス

Clinical Informatics
座長：内野 詠一郎（京都大学）、中津井 雅彦（山口大学）、岡本 里香（京都大学）

O01-01

Robustness comparison of data preprocessing methods for exosome-derived miRNA
microarray data
Yuto TAKEMOTO1, Daisuke ITO2, Masahisa KATSUNO2, Masahiro NAKATOCHI1,
Laura I. FURLONG3
Department of Integrated Health Sciences, Graduate School of Medicine, Nagoya University, 2Department of Neurology,
Graduate School of Medicine, Nagoya University, 3MedBioInformatics Solutions S.L., Barcelona, Spain

1

O01-02

GLDADec: Guided LDA deconvolution enabled us to identify cell type proportions
Iori AZUMA, Tadahaya MIZUNO, Hiroyuki KUSUHARA
Laboratory of Molecular Pharmacokinetics, Graduate School of Pharmaceutical Sciences, The University of Tokyo

O01-03Development of the classification model for groups with relatively lower hippocampal
volume using blood amino acid levels
Kazuki FUJIWARA1, Wataru SATO1, Kenji NAGAO1, Akira IMAIZUMI1, Ikuko N. MOTOIKE2,
Mitsuharu SATO2, Kengo KINOSHITA2, Seizo KOSHIBA2, Masayuki YAMAMOTO2
1
Research Institute for Bioscience Products & Fine Chemicals, Ajinomoto Co., Inc., 2Tohoku Medical Megabank
Organization, Tohoku University

O01-05

Body mass index stratification improves polygenic prediction of type 2 diabetes in transbiobank analysis
Takafumi OJIMA1, 2, Shinich NAMBA1, Ken SUZUKI1, 3, Kenich YAMAMOTO1, 4, 5,
Kyuto SONEHARA1, 6, Akira NARITA7,
the Tohoku Medical Megabank Project Study Group .7, the Biobank Japan Project .8,
Yoichiro KAMATANI9, Gen TAMIYA2, 7, 10, Masayuki YAMAMOTO2, 7,
Toshimasa YAMAUCHI3, Takashi KADOWAKI3, 11, Yukinori OKADA1, 5, 6, 12
Department of Statistical Genetics, Osaka University Graduate School of Medicine, 2Graduate School of Medicine,
Tohoku University, 3Department of Diabetes and Metabolic Diseases, Graduate School of Medicine, the University ofTokyo, 4Department of Pediatrics, Osaka University Graduate School of Medicine, 5Laboratory of Statistical Immunology,
Immunology Frontier Research Center (WPI-IFReC), Osaka University, 6Department of Genome Informatics, Graduate
School of Medicine, the University of Tokyo, 7Tohoku Medical Megabank Organization, Tohoku University, 8Institute ofMedical Science, the University of Tokyo, 9Laboratory of Complex Trait Genomics, Graduate School of Frontier Sciences,
The University of Tokyo, 10Center for Advanced Intelligence Project, RIKEN, 11Toranomon Hospital, 12Laboratory for
Systems Genetics, RIKEN Center for Integrative Medical Sciences
1

O01-06

An innovative attempt to assist REC procedure for the medical research involving samples
and information held by national network
Hiroko TERUI-KOHBATA1, Fuji NAGAMI2, Kinuko OHNEDA2, Takayuki MORISAKI3,
Yuichi GOTO4, Eisei NOIRI5, Hiroyuki NISHIYAMA6, Manabu MUTO7, Mizuki MIRITA8,Soichi OGISHIMA2, Masayuki YOSHIDA1
Bioethics Research Center, Tokyo Medical and Dental University, 2Tohoku Medical Megabank Organization, Tohoku
University, 3The Institute of Medical Science, The University of Tokyo, 4Medical Genome Center, National Center of
Neurology and Psychiatry, 5NCBN Central Biobank, National Center for Global Health and Medicine, 6Tsukuba Human
Tissue Biobank Center, University of Tsukuba Hospital, 7Department of Therapeutic Oncology, Graduate School of
Medicine, Kyoto University, 8Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama
University
1― 19 ―

O02



10 月 24 日（火）/ October 24 (Tue)401 13:30 ～ 15:00

分子ロボティクス / バイオインフォマティクス

Molecular Robotics / Bioinformatics
座長：小宮 健（海洋研究開発機構）、茂櫛 薫（cBioinformatics）、鎌田 真由美（京都大学）

O02-01Development of a Prototype of voice-operated VR Molecular Design Environment
Hirotaka KONDO1, Spence Gregory GUTMANN2, Kuzuya AKINORI1, 2, 3, Konagaya AKIHIKO2
Organization for Research and Development of Innovative Science and Technology, Kansai University, 2Molecular Robotics
Research Institute, Co., Ltd., 3Department of Chemistry and Materials Engineering, Kansai University
1

O02-02

Deep Learning-Based Deconvolution of Confocal Laser-scanning Fluorescence Microscopy
Images for Enhanced Visualization of Giant Vesicles
Soichiro HIORI1, 2, Taro TOYOTA1, 2, 3, Akihiko KONAGAYA2
Department of Basic Science, Graduate School of Arts and Science, The University of Tokyo, 2Molecular Robotics
Research Institute, Co., Ltd., 3Universal Biology Institute, The University of Tokyo1

O02-03Large-scale VR Molecular Rendering for Co-creation Environment
Yuhui ZHANG1, 2, Gregory GUTMANN2, Akihiko KONAGAYA2, 3, 4
1
4

O02-04

School of Computing, Tokyo Institute of Technology, 2Molecular Robotics Research Institute, Co., Ltd., 3Keisen University,
Advanced Science ELSI Research Laboratory, CBI Research InstituteApplication of Machine Learning Models in Prediction of Disease Therapeutic Target
Molecules Using Gene Expression Profiles
Narumi HATANO1, Mayumi KAMADA1, Ryosuke KOJIMA1, Hiroaki IWATA1,
Yasushi OKUNO1, 2
1
Graduate School of Medicine and Faculty of Medicine Kyoto University, 2RIKEN Center for Computational Science(RCCS)HPC/HPC- and AI-driven Drug Development Platform Division

O02-05

Gene prioritization using a genetics-led approach for target discovery of ALS
Yuki KUNIYOSHI1, Rebecca HARRISON2, Hiroyuki IHA1, Siddharth SETHI2,
Harpreet SAINI2, Daisuke D IKEDA1
1

O02-06

Otsuka Pharmaceutical Co., Ltd., 2Astex Pharmaceuticals

Elucidation of trajectories from healthy state to mibyo and disease onset through large-scalespecific health checkup data
Satoshi NAGAIE, Satoshi MIZUNO, Ryosuke ISHIWATA, Soichi OGISHIMA
Tohoku University Tohoku Medical Megabank Organization― 20 ―

O03



10 月 24 日（火）/ October 24 (Tue)
瑞雲 17:10 ～ 18:40

分子計算・機械学習 / AI による創薬研究の最新動向と展望

The latest trends and prospects in drug discovery research using
molecular calculation, machine learning, and AI
座長：高谷 大輔（大阪大学）、岡田 晃季（第一三共）、河合 隆利（慶應大学）、佐藤 朋広（理化学研究所）

O03-01

De novo inhibitor and activator design from gene expression profiles via deep learning andBayesian optimization
Yuki MATSUKIYO1, Chikashige YAMANAKA1, Yoshihiro YAMANISHI1, 2
Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute ofTechnology, 2Graduate School of Informatics, Nagoya University1

O03-02Development of 53BP1 expression classification model by using Segment-Anything
Model(SAM) and CNN Model in pathological image
Yasuhiko NAKAO1, Maiko TABUCHI1, 2, Kazuhiko NAKAO1, Yuko AKAZAWA2Department of Gastroenterology and Hepatology, Nagasaki University Hospital, 2Department of Histology and Cell
Biology, Nagasaki University Graduate School of Biomedical Sciences1

O03-03

Multi-objective Molecular Structure Generation Using Dynamic Applicability Domains
Adaptation
Tatsuya YOSHIZAWA1, Shoichi ISHIDA1, Tomohiro SATO2, Masateru OHTA3,
Teruki HONMA2, Kei TERAYAMA1, 4, 5
Graduate School of Medical Life Science, Yokohama City University, 2RIKEN Center for Biosystems Dynamics Research,
HPC- and AI-driven Drug Development Platform Division, RIKEN Center for Computational Science, 4RIKEN Center for
Advanced Intelligence Project, 5MDX Research Center for Element Strategy, Tokyo Institute of Technology
13

O03-04Generating Synthesizable Compound Structures with Desired Properties via Deep Learning
ModelsShinnosuke TAKADA1, Kazuma KAITOH1, 2, Koji TSUDA3, Yoshihiro YAMANISHI1, 2
1
Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Instituteof Technology, 2Department of Complex Systems Science, Graduate School of Informatics, Nagoya University, 3Graduate
School of Frontier Sciences, The University of Tokyo

O03-05

Benchmarking Deployed Generative Models on Elix Discovery
Vincent Paul Guillaume RICHARD, Jun Jin CHOONG
Research Department, Elix, Inc.

O03-06

Improving the Practical Applicability of Computer-assisted Chemical Synthesis Planning
Approaches using Substructure Patterns
Haris HASIC1, 2, Takashi ISHIDA1
Department of Computer Science, Tokyo Institute of Technology, 2Elix, Inc.

1

O03-07Development of Auto-FMO protocol and data accumulation in FMODB through 2023
Chiduru WATANABE, Kikuko KAMISAKA, Norihiko TANI, Teruki HONMA
RIKEN Center for Biosystems Dynamics Research― 21 ―

O04

10 月 24 日（火）/ October 24 (Tue)401 17:10 ～ 18:40

計算化学（分子計算）/ 創薬応用 / 量子構造生命科学 /Computer-Aided Drug Design

Computational Chemistry (Molecular Modeling) / Drug Discovery Application /Quantum-Structural Life Science / Computer-Aided Drug Design
座長：田中 成典（神戸大学）、上村 みどり（CBI 研究機構）、升岡 優太（大鵬薬品工業）

O04-01

Exploring the Selectivity and Binding Mechanism of ToP-DNJ Toward Endoplasmic Reticulum
α -Glucosidase II
Mochammad Arﬁn Fardiansyah NASUTION1, 2, 3, Gert-Jan BEKKER1, Suyong RE3,
Chioko NAGAO1, 2, 3, Kenji MIZUGUCHI1, 2, 3
1
Laboratory for Computational Biology, Institute for Protein Research, Osaka University, 2Department of Chemistry,
Graduate School of Science, Osaka University, 3Artiﬁcial Intelligence Center for Health and Biomedical Research, National
Institutes of Biomedical Innovation, Health and Nutrition

O04-02In-silico design of peptide inhibitors containing unnatural amino acidSuyong RE1, Kenji MIZUGUCHI1, 2, Yusaku HONDA3, Daisuke TAKAHASHI3, 4,
Ryo YAZAKI3, Takashi OHSHIMA3
1
Artiﬁcial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Healthand Nutrition, 2Institute for Protein Research, Osaka University, 3Graduate School of Pharmaceutical Sciences, Kyushu
University, 4Faculty of Pharmaceutical Sciences, Sojo University

O04-03

Analysis of the binding profile of quinonoid -form of dihydropteridine to quinonoid dihydropteridine reductase
Haruka KONO, Satoshi HARA, Tadaomi FURUTA, Hiroshi ICHINOSE
School of Life Science and Technology, Tokyo Institute of Technology

O04-04

Study Improves Performance of Pocket Comparisons Using Machine Learning
Risa SHIOZAWA, Masakazu SEKIJIMA, Nobuaki YASUOTokyo Institute of Technology

O04-05Fundamentals of Molecular Dynamics Simulation
Yuto KOMEIJI1, 2, 3, 4
Health and medical research institute, AIST, 2Department of Chemistry, Rikkyo University, 3Center for Biosystems
Dynamics Research, RIKEN, 4Graduate school of pharmaceutical sciences, Osaka University

1

O04-06MAXS reveals a disorder-to-order transition of the intrinsically disordered region in active
MAP2K6
Takashi MATSUMOTO1, Seisuke HASEGAWA2, Takashi SATO1, Takayoshi KINOSHITA 2
Rigaku Corporation, 22Graduate School of Science, Osaka Metropolitan University

1

O04-07

Near atomic-resolution cryoEM structure of PROTAC ternary complex
Jack YAN, Yifan HU, Jiaquan WU
CryoEM department, Wuxi Biortus Biosciences Co. Ltd.

― 22 ―

プレナリー講演PL02

10 月 25 日（水）/ October 25 (Wed)
大ホール 10:00 ～ 11:30

コホート・バイオバンクで挑む次世代ヘルスケア

Next Generation Healthcare Challenged by Cohort Biobanks
座長：武林 亨（慶應義塾大学医学部）
Toru Takebayashi (Keio University)

PL02-01

バイオ戦略におけるデータ利活用の現状と次世代ヘルスケアにおける可能性について

Current Status of Data Utilization in Bio-Strategy and its Potential in Next Generation Healthcare
小林 憲明

Noriaki Kobayashi

内閣府 バイオ戦略有識者

Cabinet Oﬃce, Government of Japan

PL02-02

コホート研究によるエビデンスの構築

Necessity of Cohort Studies to create Evidence
玉腰 暁子

Akiko Tamakoshi

北海道大学医学研究院 社会医学系部門

Hokkaido University Graduate School of Medicine

PL02-03

エビデンスを個別化ヘルスケアにつなげる - 東北メディカル・メガバンクの成果を中心に -Implementation of Evidence to Personalized Health Care - Focusing on the Achievements of
Tohoku Medical Megabank project 寳澤 篤

Atsushi Hozawa
東北大学大学院医学系研究科

Tohoku University Graduate School of Medicine― 23 ―

招待講演

IL02



10 月 25 日（水）/ October 25 (Wed)
大ホール 13:30 ～ 15:00

ビッグデータと創薬

Big Data and AI Drug Discovery
座長：荻島 創一（東北大学未来型医療創成センター / 東北メディカル・メガバンク機構）
Soichi Ogishima (INGEM/Tohoku Medical Megabank Organization, Tohoku University)

IL02-01

ビッグデータ・AI による医学・医療の第 3 次革命と未来の医学

The Third Revolution of Medicine and Medical Treatment by Big Data and AI and Future
Medicine
田中 博

Hiroshi Tanaka
東京大学 / 東京医科歯科大学

The University of Tokyo/Tokyo Medical and Dental University

IL02-02

AI は創薬を変革できるのか
Can AI Revolutionize Drug Discovery?
奥野 恭史

Yasushi Okuno

京都大学大学院医学系研究科

Graduate School of Medicine and Faculty of Medicine Kyoto University

IL02-03

大規模ヒトデータをどう創薬に活かすか：タケダの挑戦と展望

Impact of Biobank Scale Human Data on Drug Discovery: Strategy and Perspective
安藤 達哉

Tatsuya Ando

武田薬品工業株式会社

Takeda Pharmaceutical Company― 24 ―

シンポジウムS02

10 月 25 日（水）/ October 25 (Wed)
大ホール 17:10 ～ 18:40

学術変革領域研究（A）天然物が織り成す化合物潜在空間が拓く生物活性分子デザイン

Latent Chemical Space Based on Diverse Natural Products for
Bio-active Molecular Design
モデレーター：菊地 和也（大阪大学）、鎌田 真由美（京都大学）、大上 雅史（東京工業大学）
Kazuya Kikuchi (Osaka University), Mayumi Kamada (Kyoto University),
Masahito Ohue (Tokyo Institute of Technology)

S02-01

領域挨拶および領域概要と研究計画

Overview of Latent Chemical Space Based on Diverse Natural Products for Bio-active Molecular
Design菊地 和也

Kazuya Kikuchi

大阪大学

Osaka University

S02-02

化学遺伝学評価系に基づく化合物潜在空間の生物活性検証と作用機序解明

Understanding the Biological activity and Mechanism of action of Compounds’ Latent Spaces
using a Chemical genomics approach
出井 晶子

Akiko Idei

国立研究開発法人 理化学研究所

RIKEN

S02-03化合物潜在空間の構築によるバーチャル化合物構造の探索と設計

Deep generative model of constructing chemical latent space for large molecular structureswith 3D complexity
榊原 康文

Yasubumi Sakakibara

慶應義塾大学

Keio University

S02-04

Beyond Rule-of-Five 空間の AI 分子設計
AI-driven molecular design on beyond-rule-of-five chemical space
大上 雅史

Masahito Ohue

東京工業大学Tokyo Institute of Technology

S02-05

アロタケタール類の単純化アナログの設計、立体異性体の系統的合成と PKC 結合活性

Design, synthesis and PKC binding activity of simplified analogs of alotaketals
塚野 千尋

Chihiro Tsukano

京都大学大学院 農学研究科

Kyoto University― 25 ―

ランチョンセミナー

LS04



10 月 25 日（水）/ October 25 (Wed)
研修室 12:00 ～ 13:00

株式会社 Elix ランチョンセミナー

Elix's Luncheon Seminar
協賛：株式会社 Elix
モデレーター：波戸 園美（株式会社 Elix）
Sonomi Hato (Elix, Inc.)

LS04-01

大規模言語モデルの基本から最前線へ ̶ 創薬での応用例を中心に
井上 貴央

Takahiro Inoue株式会社 Elix

Elix, Inc.

LS04-02

科研製薬における Elix Discovery™ の導入及び活用事例
森本 恭平

Kyohei Morimoto

科研製薬株式会社

KAKEN PHARMACEUTICAL CO.,LTD.

LS04-03

Elix Discovery™ の 1 年の歩み ̶ SBDD モジュールの展開と構造発生の新機能
井上 貴央

Takahiro Inoue株式会社 Elix

Elix, Inc.

LS05



10 月 25 日（水）/ October 25 (Wed)401 12:00 ～ 13:00

AI ベースのディスカバリーツール – EmBiology によるバイオロジー研究の促進

Empowering discovery with EmBiology - an AI-based research tool for biologists
協賛：エルゼビア・ジャパン株式会社
モデレーター：伊橋 彩（エルゼビア・ジャパン株式会社）、齋藤 美里（エルゼビア・ジャパン株式会社）
Aya Ihashi (Elsevier Japan K.K.), Misato Saito (Elsevier Japan K.K.)

LS05-01

Sherry Winterエルゼビア B. V.
Elsevier B. V.

― 26 ―

LS06



10 月 25 日（水）/ October 25 (Wed)407 12:00 ～ 13:00

Preferred Networks における AI 創薬への取り組みのご紹介

Introduction of AI Drug Discovery Initiatives at Preferred Networks
協賛：株式会社 Preferred Networks
モデレーター：石谷 隆一郎（株式会社 Preferred Networks）、山岸 純也（株式会社 Preferred Networks）、
武本 瑞貴（株式会社 Preferred Networks）
Ryuichirou Ishitani (Preferred Networks, Inc), Junya Yamagishi (Preferred Networks, Inc),
Mizuki Takemoto (Preferred Networks, Inc)

― 27 ―

スポンサードセッション

SS07



10 月 25 日（水）/ October 25 (Wed)
桃源 13:30 ～ 15:00

創薬の未来へのカギ：クラウドで活用される HPC と生成系 AI

The Key to the Future of Drug Discovery: Utilizing HPC and Generative AI in the Cloud
協賛：アマゾン ウェブ サービス ジャパン合同会社
モデレーター：片岡 勇人（アマゾン ウェブ サービス ジャパン合同会社）
Yuto Kataoka (Amazon Web Services Japan G.K.)

SS07-1AWS の HPC への取り組みと創薬分野における事例
Exploring HPC on AWS and Use Cases in Drug Discovery
佐々木 啓

Kei Sasaki

アマゾン ウェブ サービス ジャパン合同会社

Amazon Web Services Japan G.K.

SS07-2

創薬における機械学習の最新動向 - 生成系 AI がもたらすイノベーション

The Latest Trends in Machine Learning for Drug Discovery: Innovation Unleashed by Generative AI
森下 裕介

Yusuke Morishita

アマゾン ウェブ サービス ジャパン合同会社

Amazon Web Services Japan G.K.

SS07-3

クラウドと生成系 AI を活用した創薬研究：タンパク質構造予測を例にAdvancing Drug Discovery with Cloud and Generative AI: A Case Study in Protein Structure
Prediction
石尾 千晶

Chiaki Ishio

アマゾン ウェブ サービス ジャパン合同会社

Amazon Web Services Japan G.K.

SS08



10 月 25 日（水）/ October 25 (Wed)
福寿 13:30 ～ 15:00

量子コンピューティング、HPC、AI が導く高精度シミュレーション技術の新展開とDMTA 研究コラボレーションプラットフォームによる創薬研究の効率・高速化

Accelerated Drug Discovery Research with Advanced Simulation via Quantum
Computing, HPC, and AI, in Conjunction with the DMTA Research Collaboration
Platform
協賛：富士通株式会社
モデレーター：松本 大輔（富士通株式会社）
Daisuke Matsumoto (Fujitsu Limited)― 28 ―

SS09



10 月 25 日（水）/ October 25 (Wed)
平安 13:30 ～ 15:00

生成 AI 時代の計算創薬を支える GPU 関連技術

GPU technology for generative AI based drug discovery era
協賛：株式会社ジーデップ・アドバンス / エヌビディア合同会社
モデレーター：山田 泰永（エヌビディア合同会社）
Taiei Yamada (NVIDIA Corporation)

SS09-1

AI と HPC で加速する創薬研究 ―ゼウレカでの活用事例と Tokyo-1 の紹介―
牧口 大旭

Hiroki Makiguchi

株式会社ゼウレカ

Xeureka, Inc.

― 29 ―

フォーカストセッション

FS05



10 月 25 日（水）/ October 25 (Wed)
研修室 13:30 ～ 15:00

第 33 回 FMO 研究会：核酸創薬に向けた構造・機能解析と計算によるアプローチ

Structural and functional analysis and computational chemistry approaches tonucleic acid drug discovery
モデレーター：福澤 薫（大阪大学）、本間 光貴（理化学研究所）
Kaori Fukuzawa (Osaka University), Teruki Honma (RIKEN)

FS05-01

創薬標的としての RNA 立体構造モチーフ

RNA structural motifs as drug targets
近藤 次郎

Jiro Kondo

上智大学理工学部物質生命理工学科

Department of Materials and Life Sciences, Sophia University

FS05-02

非二重らせん核酸による疾患関連遺伝子の発現制御

Regulation of expression for disease-related genes by noncanonical nucleic acids
建石 寿枝

Hisae Tateishi-Karimata

甲南大学 先端生命工学研究所

Frontier Institute for Biomolecular Engineering Research (FIBER), Konan University

FS05-03

FMO 法を用いた核酸の相互作用解析
Molecular interaction analysis of nucleic acid using the FMO method
福澤 薫

Kaori Fukuzawa
大阪大学大学院薬学研究科

Graduate School of Pharmaceutical Sciences, Osaka University

FS06



10 月 25 日（水）/ October 25 (Wed)407 13:30 ～ 15:00

先端的計測技術 (1)Advanced Measurement and Analysis (1)モデレーター：多田隈 尚史（上海科技大学）、石田 誠一（国立医薬品食衛生研究所 / 崇城大学生物生命学部）、

藤田 聡史（産業技術総合研究所 生命工学領域 先端フォトニクス・バイオセンシング）
Hisashi Tadakuma (ShanghaiTech), Seiichi Ishida (National Institute of Health Sciences / Department ofApplied Life Science, Sojo University), Satoshi Fujita (OIL Photo BIO-OIL, AIST)

FS06-01

Learning Cytometry の開拓
Developing learning cytometry technologies
太田 禎生

Sadao Ota

東京大学

先端科学技術研究センター

Resarch Center for Advanced Science and Technology, The University of Tokyo― 30 ―

FS06-02

単一細胞核の高解像度空間マルチオミクス解析

High-resolution spatial multi-omics analysis of single nuclei
武井 洋大

Yodai Takei

カリフォルニア工科大 バイオエンジニアリング研究科

Division of Biology and Biological Engineering, California Institute of Technology

FS06-03

スフェロイドの光学観察による創薬の可能性

Possibility of drug discovery by optical observation of spheroids
小島 伸彦

Nobuhiko Kojima

横浜市立大学 生命ナノシステム科学研究科

Graduate School of Nanobioscience, Yokohama City University

FS07



10 月 25 日（水）/ October 25 (Wed)307 13:30 ～ 15:00

分子ロボティクス研究会「人工生体分子で脂質膜間通信を目指す」

Molecular Robotics Research Group: Toward information transmission acrosslipid bilayer using artificial biomolecules
モデレーター：川又 生吹（東北大学）、佐藤 佑介（九州工業大学）、小宮 健（海洋研究開発機構）、
多田隈 尚史（上海科技大学）、豊田 太郎（東京大学）
Ibuki Kawamata (Tohoku University), Yusuke Sato (Kyushu Institute of Technology), Ken Komiya (JAMSTEC),
Hisashi Tadakuma (ShanghaiTech University), Taro Toyota (The University of Tokyo)

FS07-01

分子ロボティクス研究会とセッションの紹介

Introduction of Molecular Robotics Research Group and this session
川又 生吹

Ibuki Kawamata

東北大学

Tohoku University

FS07-02

人工 DNA チャネル内部のイオン輸送に関する分子シミュレーション

Molecular Simulations of Ion Transport Through Artificial DNA Channels
馬渕 拓哉

Takuya Mabuchi

東北大学

Tohoku University

FS07-03

環状ペプチドの膜透過メカニズムを明らかにするための分子動力学シミュレーションプロトコルの開発と応用

Development and application of molecular dynamics simulation protocols to elucidate themembrane permeation mechanism of cyclic peptides
杉田 昌岳

Masatake Sugita

東京工業大学Tokyo Institute of Technology― 31 ―

FS07-04

ナノポアを形成する人工膜輸送体の構築

Construction of artificial membrane transporters with nanopore structure
川野 竜司

Ryuji Kawano

東京農工大学Tokyo University of Agriculture and Technology

FS08



10 月 25 日（水）/ October 25 (Wed)
研修室 17:10 ～ 18:40

創薬におけるプライバシー保護連合学習の最新動向

Recent Developments of Privacy-Preserving Federated Learning in Drug
Discovery
モデレーター：藤 秀義（イクトス）
Hideyoshi Fuji (Iktos SA)

FS08-01

AMED DAIIA の概要と情報セキュリティ技術への期待
Overview of AMED DAIIA and Expectations for Information Security Technology
本間 光貴

Teruki Honma

理研

RIKEN

FS08-02

kMol による効率的かつスケーラブルな活性予測のフレームワーク
Efficient and Scalable Framework for Activity Prediction with kMol
Jun Jin Choong
株式会社 Elix

Elix, Inc.

FS08-03

FLuID, Federated Learning using Information Distillation
Thierry Gilles Hanser
Lhasa Limited

FS08-04

Industry-Scale Orchestrated Federated Learning for Drug Discovery
Martijn Oldenhof
KU Leuven― 32 ―

FS09

10 月 25 日（水）/ October 25 (Wed)407 17:10 ～ 18:40

先端的計測技術 (2)Advanced Measurement and Analysis (2)モデレーター：多田隈 尚史（上海科技大学）、石田 誠一（国立医薬品食衛生研究所 / 崇城大学生物生命学部）、

藤田 聡史（産業技術総合研究所 生命工学領域 先端フォトニクス・バイオセンシング）
Hisashi Tadakuma (ShanghaiTech),
Seiichi Ishida (National Institute of Health Sciences / Department of Applied Life Science, Sojo University),
Satoshi Fujita (OIL Photo BIO-OIL, AIST)

FS09-01

ヒト iPS 細胞由来血液脳関門モデルを用いた輸送解析および MPS 構築への応用

Transport analysis and application to MPS using a human iPS cell-derived blood-brain barrier
model
黒澤 俊樹

Toshiki Kurosawa

帝京大学 薬学部 薬物動態学研究室

Faculty of Pharma-Science, Teikyo University

FS09-02

腫瘍微小環境および器官形成への応用に向けた血管網を有する Microphysiological systems (MPS)

Microphysiological Systems (MPS) With Vascular Network for Tumor Microenvironment andOrganogenesis Applications
横川 隆司

Ryuji Yokokawa

京都大学大学院工学研究科マイクロエンジニアリング専攻

Department of Micro Engineering, Kyoto University

FS09-03

肺オルガノイドの応用と未来

Application of lung organoids for future innovative therapeutics
山本 佑樹

Yuki Yamamoto

HiLung 株式会社
HiLung Inc

FS10



10 月 25 日（水）/ October 25 (Wed)406 17:10 ～ 18:40

生命の起源：翻訳の起源

Origin of Life: Origin of Translation
モデレーター：田中 成典（神戸大学）
Shigenori Tanaka (Kobe University)

FS10-01

はじめにIntroduction
田中 成典

Shigenori Tanaka

神戸大学

Kobe University― 33 ―

FS10-02

翻訳開始過程における開始 tRNA 認識の分子機構：tRNA とリボソームタンパク質の進化を探る

Molecular mechanism of initiation tRNA recognition in translation initiation process: Exploring
the evolution of tRNAs and ribosomal proteins
森 義治

Yoshiharu Mori
神戸大学

Kobe University

FS10-03

翻訳開始因子 eIF4A と RNA の相互作用解析

Analysis of interaction between translation initiation factor eIF4A and RNA半田 佑磨

Yuma Handa

星薬科大学

Hoshi University

FS11



10 月 25 日（水）/ October 25 (Wed)307 17:10 ～ 18:40

医療データ AI 解析実践フォーラム

Medical Data AI Analysis Practical Forum
モデレーター：水野 聖士（東北大学）、小島 諒介（京都大学）、荻島 創一（東北大学）
Satoshi Mizuno (Tohoku University), Ryosuke Kojima (Kyoto University),
Soichi Ogishima (Tohoku University)

FS11-01

大規模グラフニューラルネットワークに基づく多様な医療関連データ解析

Multi-modal medical-related data analysis based on large-scale graph neural networks
小島 諒介

Ryosuke Kojima

京都大学

Kyoto University

FS11-02

中外製薬が進める創薬研究における AI 活用実践

Practical AI Applications in Drug Discovery Research by Chugai Pharmaceutical
徳山 健斗

Kento Tokuyama

中外製薬株式会社

CHUGAI PHARMACEUTICAL

FS11-03

低出生体重の層別化に基づく環境・遺伝要因による予測モデルの開発

The early prediction models of low-birth-weight reveals different influential environmental andgenetic factors for prediction between term and preterm birth group水野 聖士

Satoshi Mizuno

東北大学

Tohoku University

FS11-04

個人レベルでの効果的な治療・疾患予防のための AI 技術の開発

AI technologies for effective treatment and disease prevention at the individual level
中村 和貴

Kazuki Nakamura

協和発酵バイオ株式会社

KYOWA HAKKO BIO CO., Ltd.

― 34 ―

口頭発表

O05



10 月 25 日（水）/ October 25 (Wed)
瑞雲 13:30 ～ 15:00

計算化学（分子計算）/ 創薬応用 / 量子構造生命科学 /Computer-Aided Drug Design

Computational Chemistry (Molecular Modeling) / Drug Discovery Application /Quantum-Structural Life Science / Computer-Aided Drug Design
座長：関嶋 政和（東京工業大学）、池上 貴史（モルシス）、髙岡 雄司（シュレーディンガー）O05-01

Molecular details on the binding of inhibitors to c-Src kinase revealed by molecular
dynamics simulations
Ai SHINOBU1, 2, Suyong RE3, Hiraku OSHIMA2, 4, Yuji SUGITA2, 5, 6
WPI Premium Research Institute for Human Metaverse Medicine, Osaka University, 2Center for Biosystems Dynamics
Research, RIKEN, 3Artiﬁcial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical
Innovation, Health, and Nutrition, 4Graduate School of Science, University of Hyogo, 5Cluster for Pioneering Research,
RIKEN, 6Center for Computational Science, RIKEN

1

O05-02

Structural and Computational Insight into Dynamics and Intermediate State in Activation of
Orexin 2 Receptor
Shun YOKOI1, 2, Ayori MITSUTAKE1
Department of Physics, School of Science and Thechnology, Meiji University, 2Structural Biology Department, School of
Medicine, Stanford University

1

O05-03

Conformation Search of Ternary Complexes for Rational PROTAC Linker Design Using
Enhanced Sampling Method
Ryunosuke YOSHINO1, 2, Genki KUDO3, Takumi HIRAO4, Takatsugu HIROKAWA1, 2
Faculty of Medicine, University of Tsukuba, 2Transborder Medical Research Center, University of Tsukuba, 3Physics
Department, Graduate School of Pure and Applied Sciences, University of Tsukuba, 4Master s Program in Medical
Sciences, Graduate School of Comprehensive Human Sciences, University of Tsukuba
1

O05-04

Conformational studies of oligo(N -methylalanine) and application of the oligomer fordesigning a ligand against a cancer-related protein MDM2
Jumpei MORIMOTO1, Marin YOKOMINE1, Yota SHIRATORI2, Takumi UEDA3,
Koji UMEZAWA4, 5, Koichiro MIYANISHI6, 7, Wataru MIZUKAMI7, Koh TAKEUCHI3,
Shinsuke SANDO1, 2
Department of Chemistry and Biotechnology, Graduate School of Engineering, The University of Tokyo, 2Department of
Bioengineering, Graduate School of Engineering, The University of Tokyo, 3Graduate School of Pharmaceutical Sciences,
The University of Tokyo, 4Department of Biomedical Engineering, Graduate School of Science and Technology, Shinshu
University, 5Institute for Biomedical Sciences, Interdisciplinary Cluster for Cutting Edge Research, Shinshu University,
6
Division of Advanced Electronics and Optical Science, Department of Systems Innovation, Graduate School of Engineering
Science, Osaka University, 7Center for Quantum Information and Quantum Biology, Osaka University
1

O05-05

Uncovering the Mechanism of Molecular Glue Protein Degraders：The high energy watermolecules at the P-P interface play key roles in the recruitment of RBM39 to DCAF15
Osamu ICHIHARA
Application Science, Schrödinger KK

O05-06Development of hit-to-lead molecular optimization by multi-objective Monte Carlo Tree
SearchTakamasa SUZUKI1, Nobuaki YASUO2, Sekijima MASAKAZU1
Department of Computer Science, Tokyo Institute of Technology, 2Academy for Convergence of Materials and Informatics,
Tokyo Institute of Technology

1― 35 ―

O06



10 月 25 日（水）/ October 25 (Wed)401 13:30 ～ 15:00

分子認識と分子計算 / 創薬応用

Moleuclar recognition and Molecular modeling / Drug Discovery Application
座長：渡邉 博文（ウィズメーティス）、石川 岳志（鹿児島大学）、永堀 博久（住友化学）

O06-01

Changes in the conformational landscape of Src kinase upon substrate recognition
Song-Ho CHONG1, Yuji SUGITA2
Global Center for Natural Resources Science, Faculty of Life Sciences, Kumamoto University, 2Center for Pioneering
Research, RIKEN
1

O06-02Development of a machine learning model for predicting protein-ligand interactions basedon FMO data
Ryosuke KITA1, Chiduru WATANABE2, Masateru OHTA3, Naoki TANIMURA4,
Koji OKUWAKI5, Mitsunori IKEGUCHI3, 6, Kaori FUKUZAWA7, Teruki HONMA2,
Tsuyohiko FUJIGAYA1, Koichiro KATO1
Department of Applied Chemistry, Kyushu University, 2RIKEN Center for Biosystems Dynamics Research, 3RIKEN Centerfor Computational Science, 4Mizuho Research & Technologies, 5Engineering Technology Division, JSOL Corporation,
6
Graduate School of Medical Life Science, Yokohama City University, 7Graduate School of Pharmaceutical Sciences, Osaka
University1

O06-03

Utilizing the FMO Database for Transfer Learning in Constructing Machine Learning Force
Fields for Biomolecular Systems.
Hiromu MATSUMOTO1, Chiduru WATANABE2, Masateru OHTA3, Naoki TANIMURA4,
Koji OKUWAKI5, Mitsunori IKEGUCHI3, 6, Kaori FUKUZAWA7, Teruki HONMA2,
Tsuyohiko FUJIGAYA1, Koichiro KATO1
Department of Applied Chemistry, Graduate School of Engineering, Kyushu University, 2RIKEN Center for Biosystems
Dynamics Research, 3RIKEN Center for Computational Science, 4Mizuho Research & Technologies, Ltd., 5Engineering
Technology Division, JSOL Corporation, 6Graduate School of Medical Life Science, Yokohama City University, 7Graduate
School of Pharmaceutical Sciences, Osaka University1

O06-04

Understanding the RNA Sequence Specificity of Translation Initiation Factor Inhibitors
through Dynamical FMO Analysis
Yuma HANDA1, 2, Hironori SAITO3, 4, Koji OKUWAKI1, Takayuki FURUISHI1,
Etsuo YONEMOCHI1, Koichito KATO5, Chiduru WATANABE6, Teruki HONMA6,
Takuhiro ITO7, Shintaro IWASAKI3, 4, Kaori FUKUZAWA1, 2
1
Department of Physical Chemistry, Hoshi University, 2Quantum life science and bioinformatics, Osaka University, 3RNA
Systems Biochemistry Laboratory, RIKEN Cluster for Pioneering Research (CPR), 4The University of Tokyo, 5Department
of Applied Chemistry, Graduate School of Engineering, Kyushu University, 6Laboratory for Structure-Based Molecular
Design, RIKEN Center for Biosystems Dynamics Research (BDR), 7Laboratory for Translation Structural Biology, RIKEN
Center for Biosystems Dynamics Research (BDR)

O06-05

Free Energy Analysis of FtsXECL1 Domain Motion by Divide-and-Conquer MD simulation
Hiromitsu SHIMOYAMA
Noguchi InstituteO06-06

Transformer Encoder-based Generative Adversarial Network for Design ofPolypharmacological Drugs
Kasumi YASUDA1, Chen LI1, 2, Kazuma KAITOH1, 2, Yoshihiro YAMANISHI1, 2
1
Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute ofTechnology, 2Graduate School of Informatics, Nagoya University― 36 ―

O07



10 月 25 日（水）/ October 25 (Wed)
瑞雲 17:10 ～ 18:40

データサイエンス / ADME・毒性

Data science / ADMET
座長：清水 祐吾（理化学研究所）、渡邉 怜子（大阪大学）、瀧本 征佑（日本たばこ産業）

O07-01

In silico and in vitro studies for identification of UDP-glucuronosyltransferase isoforms inacotiamide metabolism
Kazuyoshi YOSHII, Ryoko TODA
Ethical drug research, Zeria Pharmaceutical Co., Ltd.

O07-02

Scaffold-Retained Transformer GAN for Molecular Generation with Chemical Property
Optimization
Chen LI1, 2, Yoshihiro YAMANISHI1, 2
1
Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, 464-8602, Japan, 2Department of Bioscience andBioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka,
820-8502, Japan

O07-03

Computational prediction of target molecules of drug candidate compounds from cellmorphology images
Shinya ISHIHARA1, Michio IWATA1, Hiromu HAYASHI1, Momoko HAMANO1,
Kazumasa SHIMOFURUTA2, Akihiro KITANI2, Naomasa SUITA2, Yoshihiro YAMANISHI1, 3
Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute ofTechnology, 2Minase Research Institute, Ono Pharmaceutical Co., Ltd, 3Graduate School of Informatics, Nagoya University1

O07-04

Insight into Federated Learning for Compound-Protein Interaction Prediction
Takuto KOYAMA1, Hiroaki IWATA1, Shigeyuki MATSUMOTO1, Ryosuke KOJIMA1,
Takao OTSUKA1, Aki HASEGAWA1, Yasushi OKUNO1, 2
1
Graduate School of Medicine, Kyoto University, 2HPC- and AI-driven Drug Development Platform Division, RIKEN
Center for Computational Science

O07-05

Binding Compound Database Screening using Deep Learning: Strategies for Improved
Candidate Enrichment.
Thomas AUZARD1, David JIMENEZ BARRERO1, Nazim MEDZHIDOV1, Naoki TARUI2
1

O07-06Elix, Inc., 2SEEDSUPPLY Inc.

AdaSplit: an adaptive dataset split method
Nathan Robert LUGG, Thomas PEACOCK, Joel C FORSTER, Ross Henry JOHNSTONE,
Benjamin INGLEDOW, Koki SHIMADA
SyntheticGestalt― 37 ―

O08



10 月 25 日（水）/ October 25 (Wed)401 17:10 ～ 18:40

分子ロボティクス

Molecular Robotics
座長：川又 生吹（東北大学）

O08-01

Experimental Investigation of Cascaded DNA Generation Reaction as A Signal Amplification
CircuitKen KOMIYA1, Chizuru NODA1, Masayuki YAMAMURA2
1
Institute for Extra-cutting-edge Science and Technology Avant-garde Research, Japan Agency for Marine-Earth Science &Technology, 2School of Computing, Tokyo Institute of Technology

O08-02

Efficient DNA-based structure automated exploration with machine-learning models
Aoi TAKEGUCHI, Aubelt-Kato NATHANAEL
Department of Advanced Sciences, Ochanomizu University

O08-03

Lipid Based Artificial Multicellular Systems for Compartmentalized and Stimuli-Responsive
Drug Delivery
Shin-ichiro M. NOMURA1, Tsuyoshi INABA1, Richard James ARCHER1, Hamada SHOGO2,
Hideaki T. MATSUBAYASHI3, Ibuki KAWAMATA1, Satoshi MURATA1
1
Department of Robotics, Graduate school of Engineering, Tohoku University, 2School of Computing , Tokyo Institute ofTechnology, 3FRIS, Tohoku University

O08-04

3D Point Cloud Analysis of Microtubule Motility Dynamics
Chen MA1, 3, Masayuki YAMAMURA1, Akira KAKUGO2, 3, Gutmann Spence GREGORY3,
Akihiko KONAGAYA3, 4, 5
1School of Computing, Tokyo Institute of Technology, 2Division of Physics and Astronomy Graduate School of Science,
Kyoto University, 3Molecular Robotics Research Institute, Co., Ltd., 4Keisen University, 5Advanced Science ELSI ResearchLaboratory, CBI Research InstituteO08-05High-Resolution AFM Imaging of DNA Structures: An Approach via Cycle GANs and Virtual
Reality Integration
Xiaoran HU1, 3, Qing LIU2, Gregory GUTMANN3, Masayuki YAMAMURA1,
Akinori KUZUYA2, Akihiko KONAGAYA3, 4, 51
5

O08-06

Tokyo Institute of Technology, 2Kansai University, 3Molecular Robotics Research Institute, Co., Ltd, 4Keisen University,
CBI Research InstituteDNA reaction–diffusion model with polymerization for pattern formation
Keita ABE1, Satoshi MURATA1, Ibuki KAWAMATA1, 2
Graduate School of Engineering, Tohoku University, 2Faculty of Core Research, Ochanomizu University

1― 38 ―

プレナリー講演PL0310 月 26 日（木）/ October 26 (Thu)
大ホール 10:00 ～ 11:30

リスク予測による個別化予防

Personalized Prevention through Risk Prediction
座長：清水 厚志（岩手医科大学）
Atsushi Shimizu (Iwate Medical University)

PL03-01

遺伝統計学による病態解明・個別化医療・ゲノム創薬

Statistical genetics elucidates disease biology, personalized medicine, drug discovery
岡田 随象

Yukinori Okada

大阪大学大学院医学系研究科

Dep. Genome Informatics, The University of Tokyo

PL03-02

機械学習・人工知能によるゲノムリスク予測

ML/AI-based genomic risk prediction
田宮 元

Gen Tamiya
東北大学大学院医学系研究科

Tohoku University Graduate School of Medicine

PL03-03

遠隔診療と Point of Care がもたらすデータ利活用モデル

Telemedicine & Point of Care bringing new Data Utilization Model
坂野 哲平

Teppei Sakano

DeNA ライフサイエンス
DeNA Life Science― 39 ―

パネルディスカッション

Panel Discussion



10 月 26 日（木）/ October 26 (Thu)
大ホール 13:30 ～ 15:00

次世代ヘルスケアの未来

The future of next generation healthcare
座長：長神 風二（東北大学東北メディカル・メガバンク機構）
Fuji Nagami (Tohoku Medical Megabank Organization, Tohoku University)
パネリスト：
吉澤 尚（弁護士 / バイオ戦略有識者）

Nao Yoshizawa (lawyer/Expert Group on Bio-Strategy)
荻島 創一（東北大学未来型医療創成センター / 東北メディカル・メガバンク機構）

Soichi Ogishima (INGEM / Tohoku Medical Megabank Organization, Tohoku University)
安中 良輔（日本製薬工業協会 産業政策委員会 / 第一三共）

Ryosuke Annaka (Japan Pharmaceutical Manufacturers Association/DAIICHI SANKYO COMPANY)
天野 慎介（全国がん患者団体連合会）

Shinsuke Amano (Japan Federation of Cancer Patient Groups)― 40 ―

シンポジウムS03

10 月 26 日（木）/ October 26 (Thu)
福寿 13:30 ～ 15:00

MPS（生体模倣システム）の今後の展開を考える

Future development of Microphysiological Systems (MPS)
座長：石田 誠一（崇城大学大学院）、山崎 大樹（国立医薬品食品衛生研究所）
Seiichi Ishida (Sojo University), Daiju Yamazaki (National Institute of Health Sciences)S03-01

始めに：MPS と感染症研究

Introduction: MPS and Infectious Disease Research
石田 誠一

Seiichi Ishida

崇城大学大学院

Sojo University

S03-02MPS を活用した新興再興感染症の病態解明研究と創薬研究
Elucidation of the pathophysiology of emerging and re-emerging infectious diseases and drug
discovery research using MPS
高山 和雄

Kazuo Takayama

京都大学 iPS 細胞研究所

Center for iPS Cell Research and Application, Kyoto University

S03-03肺の形成・機能・感染における三次元構造の重要性と、Pandemic preparedness への応用

Significance of three-dimensional structure in lung development, function, and infection, andapplication to pandemic preparedness
永元 哲治

Tetsuharu Nagamoto

HiLung 株式会社
HiLung Inc.

― 41 ―

学会企画

SP



10 月 26 日（木）/ October 26 (Thu)
桃源 13:30 ～ 15:00

AMED の事業紹介（医療・介護・健康データ利活用基盤高度化事業，
革新的先端研究開発支援事業，橋渡し研究プログラム）

Introduction of AMED project of "PHR Data Distribution Infrastructure forAdvancement of Medical Care", "Advanced Research & Development Programsfor Medical Innovation", and "Translational Research Program"
モデレーター：塩塚 政孝（国立研究開発法人日本医療研究開発機構）
Masataka Shiozuka (Japan Agency fo Medical Research and Development)

SP-01

医療・介護・健康データ利活用基盤高度化事業（医療高度化に資する PHR データ流通基盤構築事業）について

AMED project of PHR data distribution infrastructure for advancement of medical care
松宮 志麻

Shima Matsumiya

AMED ゲノム・データ基盤事業部ゲノム・データ研究開発課
Division of Health and Clinical Data, Department of Health and Clinical Data, AMED

SP-02

革新的先端研究開発支援事業の概要について

Overview of Advanced Research & Development Programs for Medical Innovation
瀬古 玲

Akira Seko
AMED シーズ開発・研究基盤事業部 革新的先端研究開発課
Division of Innovative Research and Development, Department of Innovation and Clinical Research Center, AMED

SP-03

橋渡し研究プログラム（研究費事業）のご紹介

Introduction of "Translational Research Program (Research and Development Grant Program)"
塩塚 政孝

Masataka Shiozuka

AMED シーズ開発・研究基盤事業部 拠点研究事業課
Division of Translational and Clinical Research Center, Department of Innovation and Clinical Research Center, AMED

― 42 ―

大会企画


10 月 26 日（木）/ October 26 (Thu)
研修室 13:30 ～ 15:00

Meet the legend
モデレーター：高橋 一敏（味の素株式会社）、増田 友秀（東レ株式会社）、渡邉 怜子（大阪大学蛋白質研究所）、
渡邉 博文（株式会社ウィズメーティス）、加藤 幸一郎（九州大学）
Kazutoshi Takahashi (AJINOMOTO CO., INC.), Tomohide Masuda (Toray Industries, Inc.),
Reiko Watanabe (Institute for Protein Research, Osaka University),
Hirofumi Watanabe (WithMetis Co., Ltd.), Koichiro Kato (Kyushu University)

01

一創薬研究者の半世紀

A Half Century of a Drug Discovery Scientist
平山 令明

Noriaki Hirayama

東海大学医学部

Tokai University School of Medicine― 43 ―

ランチョンセミナー

LS07



10 月 26 日（木）/ October 26 (Thu)
研修室 12:00 ～ 13:00

LSKB のフロンティア : BERT（MNLI）文献解析と ChatGPT でわかること

LSKB Frontier: BERT (MNLI) Literature Analysis and Its Adaptation to ChatGPT
協賛：株式会社ワールドフュージョン
モデレーター：川原 弘三（株式会社ワールドフュージョン）
Kozo Kawahara (WorldFusion Co,. Ltd.)

LS07-01

LSKB と BERT（MNLI）による革新的文献解析
Innovative Literature Analysis with LSKB and BERT (MNLI)
緑川 淳

Atsushi Midorikawa
株式会社ワールドフュージョン

WorldFusion Co,. Ltd.

LS08



10 月 26 日（木）/ October 26 (Thu)
401 12:00 ～ 13:00

AI による創薬の変革

Transforming Drug Discovery Through AI
協賛：インシリコ・メディシン
モデレーター：陳 心怡（インシリコ・メディシン）、林 彥竹（インシリコ・メディシン）
Aggie, Hsin-I Chen (Insilico Medicine), Jimmy, Yen-Chu Lin (Insilico Medicine)

LS08-01林 彥竹

Jimmy, Yen-Chu Lin
インシリコ・メディシン

Insilico Medicine― 44 ―

LS09

10 月 26 日（木）/ October 26 (Thu)407 12:00 ～ 13:00

データ活用による医薬品開発の DX 促進

Promoting DX in drug development through data utilization
協賛：パトコア株式会社
モデレーター：宮崎 大祐（パトコア株式会社）、有賀 文昭（パトコア株式会社）
Daisuke Miyazaki (Patcore, Inc.), Fumiaki Aruga (Patcore, Inc.)

LS09-01

データ活用による医薬品開発の新展開

Harnessing the power of data to accelerate drug development
Jaokar Tulika
ExcelraLS09-02

試験依頼システム（仮称）のご紹介 〜化合物試験の計画的・効率的な依頼、Design Hub を添えて〜

Introducing the Assay Request System (tentative name)~Planned and efficient request for compound assay, with Design Hub~蕪木 快

Kai Kaburaki
パトコア株式会社

Patcore, Inc.

― 45 ―

フォーカストセッション

FS12



10 月 26 日（木）/ October 26 (Thu)407 13:30 ～ 15:00

幹細胞と AI を用いた毒性予測の新アプローチ法の紹介

Introduction to a new approach method to toxicity prediction using stem
cells and AI
モデレーター：曽根 秀子（横浜薬科大学）
Hideko Sone (Yokohama University of Pharmacy)

FS12-01

ヒト幹細胞多臓器毒性予測システム「StemPanTox」の特徴とその価値

Features and Its Value of StemPanTox, Human Stem Cell Multi-Organ Toxicity Prediction System
藤渕 航

Wataru Fujibuchi
日本新薬株式会社 / 東京大学大学院医学系研究科
Japan Shinyaku Co., Ltd. / Graduate School of Medicine, The University of Tokyo

FS12-02

再利用可能な実行パイプライン構築版：ステムパントックスαの紹介

Reusable Execution Pipeline Build: Introducing the Stem Pantox α
高瀬 俊郎

Toshiro Takase

日本アイ・ビー・エム株式会社

Japan IBM Corporation

FS12-03

ステムパントックスに用いる化学物質毒性予測アルゴリズムの検討

Study of chemical toxicity prediction algorithm for StemPantox
加藤 毅

Tsuyoshi Kato
群馬大学

Gunma University― 46 ―

抄

録

CL

大会長講演
日時：2023 年 10 月 24 日（火）10：10 － 10：40
会場：大ホール

大会長講演
Conference Chairperson Lecture【 開催趣旨 】次世代ヘルスケアの実現に向けて、ゲノム情報や医療・健康情報の大規模データの利活用が国内外で急速に進んでいる。多因子疾患のゲノム医療の実現には、前向きゲノムコホートによる遺伝要因と環境要因の大規模なデータの利活用による発症メカニズムの解明と罹患リスクの予測モデルの開発が重要である。東北メディカル・メガバンク計画の進展と未来型の医療の実現に向けた取り組みについて講演する。

CL

東北メディカル・メガバンク計画と個別化ヘルスケアの推進
Tohoku Medical Megabank project and Promotion of Personalized Healthcare
山本 雅之

東北大学東北メディカル・メガバンク機構

Masayuki Yamamoto
Tohoku University Tohoku Medical Megabank Organization

ヒトを対象とした縦断型健康調査の重要性が広く理解され始めている。縦断型健康調査は、コホート研究とバイオバン
クを通して実現されるが、特に、これらにゲノム解析を組み合わせたゲノムコホート研究と、その参加者のゲノム・オ
ミックス解析から得られた情報を広く分譲する複合バイオバンク（Integrated Biobank）は、次世代の医療を創り、人々の健康の維持・増進に貢献し、さらに、創薬をはじめとする産業の基盤を創成するものと期待されている。東北メディ
カル・メガバンク（TMM）では、未来型医療を創出する基盤事業として、一般住民を対象とした前向きコホートである地域住民コホートと、妊婦のリクルートと児の出生から始まる三世代コホートを戦略的に組み合わせて実施しており、既に両コホート合わせて 15.7 万人を超える参加者をリクルートしている。また、これらのコホート基盤に立脚する複合バイオバンクの整備も進んでいる。TMM では、解析センターを併設することによって、多くの研究者や企業に必要な基本的な情報を解析データの形で分譲・提供している。TMM 計画の推進を通して、多くの多因子疾患の罹患リスクが予知できるようになることが期待される。私たちは、遠からず、遺伝情報と環境要因情報を活用した未来型の医療モデルが実現できるものと確信している。

― 48 ―

PL01

プレナリー講演
日時：2023 年 10 月 24 日（火）10：40 － 12：00
会場：大ホール

大規模データが切り拓く次世代ヘルスケア
Large-scale data for next-generation healthcare【座長】

山本 雅之（東北大学東北メディカル・メガバンク機構）
Masayuki Yamamoto（Tohoku Medical Megabank Organization）【 開催趣旨 】新型コロナウイルス感染症のパンデミックは、大規模データによる医療の取り組みができていないことを浮き彫りにした。大規模データによる次世代ヘルスケアの実現に向けて、まず、新型コロナウイルス感染症の教訓から、我々が直面している課題を共有し、いかに克服してゆくか議論する。次に、国を挙げた医療 DX が進展するなかで、ICT により未来の医療をいかに実現するか展望する。さらに、医薬品・ワクチン開発と DX の最前線についてご講演いただき、大規模データが切り開く次世代ヘルスケアの実装に向けた議論を深めたい。

PL01-01

データシェアリングによる医療課題の克服と展望：新型コロナからの教訓
Tackling with medical R&D gap by data sharing : A lesson from COVID-19
末松 誠

慶應義塾大学 / 実験動物中央研究所

Makoto Suematsu
Keio University / Central Institute for Experimental Animals

新型コロナウイルス感染症（COVID-19）のパンデミックは図らずも医療研究開発の現場の至るところにあるギャップを露呈させる結果となった。SARS-CoV-2 の変異株の出現は国際協力の下に、鳥インフル用に構築されたデータベース
である GISAID を用いて世界中の個々の研究チームが検出した日とサンプル採取した都市名を登録した。2020 年の第１波は殲滅できたものの、第 2 波第 3 波では不可解な変異株の経時変化を見ることができた。2020 年夏から秋にかけて日本には Main protease 高活性株と低活性株の 2 種類が日本を闊歩していた。Main protease はこのウイルスの病原性に深く関与するのであるが、不思議なことに 2020 年の初夏から秋にかけては低活性株が広く広まっていたが、以降は急速に低下し、高活性株のみが残るようになった。低活性株の消長は公開情報である飛行機旅客数の消長と一致した。
同一の変異株の検出都市を比較すると、どの都市からどの都市へ株が移行したかがデータベースから判明する。ところが主要先進国のうち、変異株の解析結果に都市名と正確な日付がついていなかったのは日本のみの事象のようであった。
初めからデータシェアリングによって広域に真実を把握し、データ管理を特定の研究機関による占有にしないつもりの運用であったからに他ならない。感染症に限らず、希少疾患、臨床画像データ、希少がん症例の情報共有を推進することによって患者はどれだけの恩恵を受けるのか？講演ではヒトの福利に資する研究開発を推進するために必要な視点について議論をしたい。

― 49 ―

PL01-02

デジタルトラストでつなぐ日本と医療の未来 - 次の世代へと医療のバトンを渡すために
Connecting Japan and the Future of Medicine with Digital Trust - Passing the
Baton of Healthcare to the Next Generation
堤 浩幸
富士通

Hiroyuki Tsutsumi
Fujitsu

昨今の技術革新、また COVID や世界情勢の激変の中で、ヘルスケアの重要性はもちろんのこと、それによる社会イノベー
ションへの期待値は一層高まってきています。
この僅か 20 年の間に、スパコンに代表されるコンピュータの性能は飛躍的に向上し、それまでは実現できなかった、
複雑で大量なデータを取り扱うシミュレーションが短時間で実現できるようになり、ゲノム医療や創薬に活かされるよ
うになりました。コンピューティングの進歩はそれだけではありません。小型化され、インターネットの普及と共にい
つでも、どこでも高性能なコンピュータを気軽に利用できる、そういった世の中が当たり前になりました。そうです、
スマートフォンです。スマホの普及により、それまでパソコンを所有していなかった層へも、高性能なコンピュータが行き渡ることとなり、デジタルの民主化が一気に進みました。ヘルスケア業界は、この進歩したコンピューティングをベースにオープン化、標準化を促進し、人々の健康を支えるため、様々なステークホルダーが「繋がり合う」デジタル
エコシステムを拡大、医療と創薬の DX により、健康な生活を長期にわたり維持する、患者の早期回復をサポートする、
さらに医療従事者のワークイノベーションを実現することなど、未来の医療体系を早期に創造することが今まさに求められています。
最終的に、皆が健康で、安心安全、さらに災害に強いソサエティを創ることが私たちの輝ける未来へと繋がります。皆さんとともにこの創造をご一緒できますことを期待しております。

PL01-03

モデルナ x デジタルの最前線
Moderna x Digital Frontiers
鈴木 蘭美

モデルナ・ジャパン

Rami Suzuki
Moderna Japan「mRNA 医薬が細胞の中に入り特定のタンパク質の合成をする指示を出す」。これは、従来の医薬品とは全く違うアプ
ローチです。私たちは「mRNA 医薬で人々に最大の可能性を」というミッションのもと、現在未解決の疾患に向けた治療や予防を開発しております。
今回の講演では、以下のポイントをご紹介します。
モ デルナは長年にわたり AI アルゴリズムとロボットによる自動化に取組んでいます。モデルナのアルゴリズムは、生産または発現などの段階で起こりえる課題を予期することにより事前に回避し、mRNA の配列を最適化しています。
● 
2023 年 5 月に開始した mCHAT ( 社内用の AI 言語処理ツール ) は、7 月末の段階では社員の過半数が活用しており、日々の業務に役立てています。
● 
データの共有にも前向きに取組んでおり、日本においては感染症の流行状況（推計）をリアルタイムで把握できる
ウェブサイトを開設しました。新型コロナウイルス感染症並びに季節性インフルエンザ感染症の流行状況を提供し、
年齢層別、地域別に可視化することができます。
● 
最近弊社はリアル・ワールド・データ（ＲＷＤ）に関する一般の方・医師への意識調査を行いました。RWD を自
身の健康や治療の向上・新しい治療法やより良い治療法の開発・医薬品の効果や治療の成果の確認に活用を望んでいる方々の比率や、実現に向けた課題等が定量化されました。
●― 50 ―

IL01

招待講演
日時：2023 年 10 月 24 日（火）13：30 － 15：00
会場：大ホール

ポスト AI 時代の創薬のための構造計算生物学
Structural Computational Biology for Drug Discovery in the Post-AI Era【座長】

井上 豪（大阪大学 大学院薬学研究科）
木下 賢吾（東北大学情報科学研究科 / 東北メディカル・メガバンク機構）
Tsuyoshi Inoue（Graduate School of Pharmaceutical Science, Osaka University）
Kengo Kinoshita（Tohoku University Tohoku Medical Megabank Organization）【 開催趣旨 】ポスト AI 時代の構造計算生物学をテーマに、まず、わが国の幅広い生命科学関連研究に立脚し、優れた研究
成果を創薬研究などの実用化研究開発に繋げる BINDS によるアカデミア創薬に関する研究支援基盤事業を紹介する。次に、AlphaFold2 に代表される AI による構造予測を取り上げ、ポスト AI 時代に向けて「問題の構造」を議論しながら、機械学習を実際に創薬へ適用し効率化をする際の克服すべき課題についても議論する。最後に、ポスト AI 時代の構造計算生物学を展望する。

IL01-01

AMED-BINDS によるアカデミア創薬に関する連携支援体制の構築
Establishment of a collaborative support system for drug discovery in academia by
AMED-BINDS
井上 豪

大阪大学 大学院薬学研究科

Tsuyoshi Inoue
Graduate School of Pharmaceutical Science, Osaka University「生命科学・創薬研究支援基盤事業（BINDS Phase II）」は、2017 年 4 月から 5 年間実施された「創薬等ライフサイエ
ンス研究支援基盤事業（BINDS Phase I）」を継承する事業として 2022 年 4 月から開始されました。
本事業は、我が国の幅広い生命科学関連研究に立脚し、その中の優れた研究成果を創薬研究などの実用化研究開発に繋げることを目的としています。構造解析に係る大型機器では、クライオ電子顕微鏡、放射光施設、中性子線構造解析施設等を備え、化合物ライブラリーについては、製薬企業拠出ライブラリー、ドラッグ・リポジショニングに資する既存薬ライブラリー、中分子創薬ライブラリー、天然物ライブラリーなど特徴あるライブラリーを整備・提供しています。
創薬研究の臨床への外挿性に資する疾患モデル細胞・動物の提供、生体模倣評価系（スフェロイド、オルガノイド等）の整備、さらに、新規モダリティ探索に資する核酸・ペプチド合成、創薬標的核酸の構造解析、AI 技術を活用したイ
ンシリコスクリーニング、生命現象を追究するオミックス解析、バイオインフォマティクスなど、最先端の生命科学・創薬研究を推進するための高度な研究支援を行います。
本講演では、BINDS（Phase II）における研究支援の概要と実際の支援申請の仕方、また本事業で特に力を入れて取り組んでいる感染症研究や Fast Track PJ などを紹介します。

IL01-02

AlphaFold を振り返る
Looking back at AlphaFold
桜庭 俊

量子科学技術研究開発機構

Shun Sakuraba
National Institutes for Quantum Science and Technology

Deepmind が 2021 年 7 月に発表した AlphaFold [Jumper et al., Nature 596, 583] は極めて多くのタンパク質の構造を正確に予測することを可能にした。2021 年版 AlphaFold (AlphaFold2) は Evoformer と Structure module から構成され、アミノ酸配列とデータベース上の配列の多重アラインメントを入力として構造を予測する。本発表では、AlphaFold2の Structure module を古典的な構造予測と比較しつつ、ポスト AlphaFold 時代に向けて「問題の構造」をどう設計すべきかの議論を行う。

― 51 ―

IL01-03

機械学習を実際に創薬への適用による効率化と克服すべき課題について
Efficiency Improvements in Drug Discovery through Machine Learning and theChallenges to Overcome
関嶋 政和

東京工業大学

Masakazu Sekijima
Tokyo Institute of Technology

我々の研究グループでは、10 年程度前から機械学習を創薬に応用するための基盤技術の開発と、その応用を行ってきた。我々の研究グループの特徴としては、我々自身が東京工業大学において情報理工学院という情報工学の研究を行う中にあって、IT 創薬コンテストを企画して IT 技術で予測したヒット化合物候補を実際にウクライナのエナミン社でアッセイを行ったり、IT 技術での予測を実際に in vitro でのアッセイや結晶構造解析を製薬企業との共同研究で行ってきた経験を機械学習手法に取り込んでいることであると考えている。これまで開発してきた SIVE-Score や VisINet、
MERMAID などは、実際に、開発を行った後に製薬企業やアカデミアとの共同研究で用いてきた。このような研究の中で、機械学習は実際に創薬の効率化が可能であると考えると同時に、in silico 側の研究者として実際に応用する際の問題も実感している。今回、機械学習手法の開発とその応用を行ってきた中で得られた知見について紹介していきたい。

IL01-04

AI 時代の分子シミュレーション
Molecular Simulation in the AI Era
池口 満徳

横浜市立大学生命医科学研究科 / 理化学研究所計算科学研究センター

Mitsunori Ikeguchi
Yokohama City University/RIKEN Center for Computational Science

AlphaFold2 などの AI による立体構造予測法の登場により、蛋白質科学は激変を迎えた。すでに、配列のデータベースには予測構造が掲載され、実験構造がなくても立体構造の概要がわかるようになっている。それでは、分子動力学（MD）
シミュレーションなどの分子シミュレーションは不要になったのだろうか。AI は基本的にデータ駆動の方法であり、一方、分子シミュレーションは物理化学原理をベースにした方法であるので、双方は原理的に相補的である。したがって、今後は AI と分子シミュレーションは密接に連携・融合しながら、進んでいくものと思われる。
では、AI と分子シミュレーションは、どのような連携・融合法がありうるだろうか？通常の分子シミュレーションでは、現時点でマイクロ秒程度の計算が日常的に可能であるが、リガンド結合や大きな立体構造変化などの創薬等で重要な時間スケールは、もう少し長いところにある。そこで、AI によって効果的なサンプリングを行って、時間スケールの壁を越えようとすることができる。また、水の存在も重要である。創薬では、水の役割の重要性の認識が進んでいるが、AlphaFold2 などではまだ水の位置は予測できていない。3D-RISM などの統計力学方法と AI を融合させて、水の位置や結合自由エネルギーを予測しようとする試みが進んでいる。このような研究を紹介する。

IL01-05

ポスト AI 時代の構造生物学と創薬研究の今後の展望
Prospects for Structural Biology and Drug Development in the Post-AI Era
木下 賢吾

東北大学情報科学研究科 / 東北メディカル・メガバンク機構

Kengo Kinoshita
Tohoku University Tohoku Medical Megabank Organization

構造生物学でも AlphaFold2 に代表される AI を活用した様々な手法が用いられるようになってきた。AI を活用した手法は、一昔前までは機械学習手法とかデータ駆動型研究と言われていたアプローチであるが、深層学習の巧妙なモデルの提案とそのモデルをトレーニングするデータ量の増加による飛躍的な精度向上を受け、現在では構造生物学だけでな
く、創薬研究も含む幅広い研究で活用されている。本講演では、BINDS 事業での我々の取り組みや、他の演者の先生方のご講演を踏まえて、創薬などの生命科学研究分野での AI 系のアプローチを概観し、今後の方向性を議論したい。

― 52 ―

PL02

プレナリー講演
日時：2023 年 10 月 25 日（水）10：00 － 11：30
会場：大ホール

コホート・バイオバンクで挑む次世代ヘルスケア
Next Generation Healthcare Challenged by Cohort Biobanks【座長】

武林 亨（慶應義塾大学医学部）
Toru Takebayashi（Keio University）【 開催趣旨 】コホート・バイオバンクの大規模データでいかに次世代ヘルスケアに挑むのか。まず、創薬・医療から、衣（アピアランスケア）・食・住（環境制御）にまでの、より広い産業の参画による次世代ヘルスケアの研究開発について概観する。次に、コホート研究により日々の生活を営む人々の習慣や環境と健康とを実際に結びつけ、いかに健康寿命を延伸する
社会、個人の対策につなげるかについて取り上げる。最後に、個々人のゲノムデータやウェアラブルをはじめとした環境曝露のデータに基づく次世代ヘルスケアの実現に向けて展望する。

PL02-01

バイオ戦略におけるデータ利活用の現状と次世代ヘルスケアにおける可能性について
Current Status of Data Utilization in Bio-Strategy and its Potential in Next
Generation Healthcare
小林 憲明

内閣府 バイオ戦略有識者

Noriaki Kobayashi
Cabinet Office, Government of Japan

バイオ戦略における、ゲノム情報や医療・健康情報の大規模データの利活用の現状と課題及び次世代ヘルスケアの実現に向けた応用面での可能性について、各種プロジェクトの事例を交えながらご紹介します。

PL02-02

コホート研究によるエビデンスの構築
Necessity of Cohort Studies to create Evidence
玉腰 暁子

北海道大学医学研究院 社会医学系部門

Akiko Tamakoshi
Hokkaido University Graduate School of Medicine

日本では環境の整備、生活環境の改善や治療法の開発などにより平均寿命とともに健康寿命（日常生活に制限のない期間）も延びているが、その差は依然として男性で 8.4 年、女性で 12.1 年ある（2019 年）。高齢期に自立を損ねる原因として、介護が必要になった主な原因をみると、脳血管疾患、認知症、高齢による衰弱、関節疾患と続いている。高齢社会を迎えている日本では、これらの疾患に対する有効な治療はもちろん、発生リスクを減らすような取り組みが、社会レベル
でも個人レベルでも必要となる。疾病に影響を与える生活習慣として、喫煙、飲酒、食事、睡眠・休養などが知られている。これらの知見を得るためには、人を対象とした医学研究が必須である。疾病発生メカニズムの解明には分子・細胞・動物レベルでの実験がもちろん不可欠であるが、実際に人で起きている事象に関する情報は、人間の集団を対象として行われる研究（＝疫学研究）から得られてきた。
人々の生活習慣と健康との関連を解明し、予防対策を樹立することを目的に国内でも多くの疫学研究が行われている。
特にコホート研究と言われる追跡型の研究は、多くの人を対象に情報を収集し、時間をかけて追跡する地道な作業をベースとしているが、そこから得られる成果は、日々の生活を営む人々の習慣や環境と健康とを実際に結びつけ、対策にもつながるものである。今回は、コホート研究の方法と成果をいくつか紹介したい。

― 53 ―

PL02-03

エビデンスを個別化ヘルスケアにつなげる - 東北メディカル・メガバンクの成果を中心に Implementation of Evidence to Personalized Health Care
- Focusing on the Achievements of Tohoku Medical Megabank project 寳澤 篤

東北大学大学院医学系研究科

Atsushi Hozawa
Tohoku University Graduate School of Medicine

どのような生活習慣や遺伝子が疾病の発症、要介護の発生、死亡と関連しているかを研究する疫学研究が大きく人々の健康に貢献してきた。ただ、「研究者が分かっている事柄」と「社会で実装されている事柄」にはギャップがあり、社会への橋渡しをすることでもっと豊かな健康社会を構築することが出来る。その橋渡しには産業界の協力が不可欠であり、東北メディカル・メガバンク計画で実施中の産学連携の事例について報告する。また、これまで疫学者が扱っていた調査票ベースの時代がウェアラブル機器による生活習慣把握に急速に変わりつつある。また、全ゲノム解析にかかる費用が低下していることや擬似全ゲノムを解析できるゲノムアレイの開発により、個々人の遺伝情報に基づくヘルスケ
アも近い未来に実現可能になる。このセッションでは多くの医療情報が利活用可能になる世界に向けて、東北メディカ
ル・メガバンク計画で得られた結果、現在実施中の調査などについて紹介する。

― 54 ―

IL02

招待講演
日時：2023 年 10 月 25 日（水）13：30 － 15：00
会場：大ホール

ビッグデータと創薬
Big Data and AI Drug Discovery【座長】

荻島 創一（東北大学未来型医療創成センター / 東北メディカル・メガバンク機構）
Soichi Ogishima（INGEM/Tohoku Medical Megabank Organization, Tohoku University）【 開催趣旨 】ゲノム情報や医療・健康情報の大規模データと AI 創薬による次世代ヘルスケアの研究開発が急速に進展している。
本セッションでは、まず、ビッグデータ医療時代のデータ駆動型の医科学、その成果により診療所の医師がゲノム電子カルテを用いてゲノム医療をする未来について概観する。次に、AI は創薬を変革できるのかー ChatGPT の出現による AI のブレー
クスルーと AI 創薬の現状と今後の方向性について取り上げる。さらに、大規模データをいかに創薬に利活用するのか、製薬会
社の挑戦と展望について議論する。

IL02-01

ビッグデータ・AI による医学・医療の第 3 次革命と未来の医学
The Third Revolution of Medicine and Medical Treatment by Big Data and AI and
Future Medicine
田中 博

東京大学 / 東京医科歯科大学

Hiroshi Tanaka
The University of Tokyo/Tokyo Medical and Dental University

医学・医療の分野も、いまや「ビッグデータ医療時代」に突入し、今後数十年続く「第 3 次革命」
（第 1 革命 : 抗生物質による細菌性感染症の激減、第 2 革命 : 分子生命科学による分子医学）を起こしつつある。従来の医学は「仮説駆動型」医科学で、得られた知識は穴だらけで全体性が欠如している。未来の医学研究は、「データ駆動型」医科学で、全体被覆性のある網羅的データから、人工知能の「教師なし学習」によって知識を自動発見する。また「精密医療の大衆化」を支える国家的な情報知能システムが出現し、一般の診療所医師が精密医療ゲノム電子カルテを使用する環境の中で、最先端の知識を提供され、意思決定を支援され医学の格差をなくすだろう。

IL02-02

AI は創薬を変革できるのか
Can AI Revolutionize Drug Discovery?
奥野 恭史

京都大学大学院医学系研究科

Yasushi Okuno
Graduate School of Medicine and Faculty of Medicine Kyoto University

第 3 次 AI ブームが少し陰りを見せた矢先、ChatGPT の出現により AI は単なるブームから一気にブレイクスルーし、未来を切り開く先導者になったと言っても過言ではない。創薬においても世界中でさまざま AI が開発され、その期待値も大いに高まっている。しかしながら、特に産業界から出される新聞・メディア等の報道には真偽が定かではないものも多く、我々学術界としては、創薬応用での AI の現状の実力、限界を冷静に見極めて、新たな技術開発に挑戦せねばならない。
演者らはこれまでに、創薬プロセスの上流から下流に至る各ステップの AI・シミュレーション技術（創薬標的探索や薬効・毒性等の活性予測やメカニズム解明、さらにはそれらを最適化条件とする化合物デザイン、臨床データからの副作用要因抽出などの計算技術）の開発を行ってきた。例えば、AMED 事業において、製薬企業数社の化合物データを共有し、活性・ADMET を予測し、化学構造の生成する AI の開発を行っている。また、マルチオミクスなどの実験デー
タ、臨床データなどの多種多様なデータを用いて、患者層別化、サンプル分類、分子メカニズムの解明、バイオマーカー
推定、創薬標的分子探索などの AI 技術も開発している。本講演では、演者が取り組む具体的な事例を紹介しつつ、創薬における AI の現状と未来についてお話ししたい。

― 55 ―

IL02-03

大規模ヒトデータをどう創薬に活かすか：タケダの挑戦と展望
Impact of Biobank Scale Human Data on Drug Discovery: Strategy and Perspective
安藤 達哉

武田薬品工業株式会社

Tatsuya Ando
Takeda Pharmaceutical Company― 56 ―

PL03

プレナリー講演
日時：2023 年 10 月 26 日（木）10：00 － 11：30
会場：大ホール

リスク予測による個別化予防
Personalized Prevention through Risk Prediction【座長】

清水 厚志（岩手医科大学）
Atsushi Shimizu（Iwate Medical University）【 開催趣旨 】ゲノム情報や医療・健康情報の大規模データの利活用して、いかにリスク予測による個別化予防を実現するか。まず、
遺伝統計学による疾患感受性遺伝子領域同定、細胞組織特異性に着目した疾患病態の解明からゲノム創薬・ゲノム個別化医療の社会実装まで概観する。次に、人口知能・機械学習手法によるゲノムリスク予測の現状と今後について展望する。さらに、わが国で初めて保険償還された医療従事者間のアプリ、患者向けの PHR や地域包括ケア向け介護・看護アプリなどによる新たな地域医療とデータ利活用の可能性について議論する。

PL03-01

遺伝統計学による病態解明・個別化医療・ゲノム創薬
Statistical genetics elucidates disease biology, personalized medicine, drug
discovery
岡田 随象

大阪大学大学院医学系研究科

Yukinori Okada
Dep. Genome Informatics, The University of Tokyo

次世代シークエンサーに代表されるゲノム配列解読機器の著しい発達により、大容量のゲノム・オミクス情報が出力される時代が到来しました。遺伝統計学は、遺伝情報と形質情報の因果関係を統計学の観点から検討する学問分野です。
メンデルによる遺伝法則の発見、家系例に対する連鎖解析、ヒト集団に対するゲノムワイド関連解析と、疾患感受性遺伝子の同定を軸に発展を遂げてきた遺伝統計学も、時代の潮流にあわせて更なる変革を要求されています。数百万人規模の大規模ヒト疾患ゲノム情報を大容量のオミクスデータと分野横断的に解釈し、社会還元するための学問へのニーズが高まっています。大規模疾患ゲノム解析による疾患感受性遺伝子領域同定、細胞組織特異性に着目した疾患病態の解明、機械学習・深層学習など革新的情報処理技術の適用、ヒト集団の適応進化の解明、ゲノム創薬・ゲノム個別化医療の社会実装など、遺伝統計学が今後取り組むべき課題を本講演ではご紹介したいと思います。特に、最近進歩が著しい
シングルセル解析の現状と実践例についてもご紹介できればと思います。本邦の生命科学研究の更なる発展に必要なのは、若手人材の育成です。特に本邦では遺伝統計学分野の人材不足が指摘されています。「遺伝統計学・夏の学校」の開催など、私達の取り組みも紹介させて頂ければと思います。

PL03-02

機械学習・人工知能によるゲノムリスク予測
ML/AI-based genomic risk prediction
田宮 元

東北大学大学院医学系研究科

Gen Tamiya
Tohoku University Graduate School of Medicine

過去 20 年以上にわたって、大規模なバイオバンク・前向きゲノムコホート研究が各国で整備され、生物医学研究のみ
ならず、生物情報学分野の研究に活発に利用されてきた。これらの前向きゲノムコホートは、糖尿病や精神疾患のような複雑な疾患の発症に効果を持つ環境因子と遺伝子、さらには、その相互作用を同定することを目的としている。このようなゲノムコホートでは、数十万人程度の検体について数百万以上のゲノムデータや生活習慣データや生化学機能
検査データ、画像データや電子カルテデータなどからなる小標本高次元の特徴をもつリアルワールドビッグデータが得られる。このようなデータから、柔軟かつ高速に真の効果因子を抽出することには本質的な統計学的問題（いわゆるp>>n 問題）が存在し、ゲノムコホート解析の実現を阻んできた。そこで、我々は、いくつかの統計的機械学習手法に基づく遺伝統計手法を開発して、実際の大規模ゲノムコホートに適用を行ってきた。本発表では、東北メディカル・メ
ガバンク機構や UK バイオバンクなどの大規模ゲノムコホートデータのデザインやデータの特徴から分かりやすく解説
し、実際の解析例を含めたうえで、今後の人口知能・機械学習手法によるゲノムリスク予測の展望について述べたい。

― 57 ―

PL03-03

遠隔診療と Point of Care がもたらすデータ利活用モデル
Telemedicine & Point of Care bringing new Data Utilization Model
坂野 哲平

DeNA ライフサイエンス

Teppei Sakano
DeNA Life Science

日本で初めて保険償還された医療従事者間用コミュニケーションアプリ Join（医療機器プログラム）を医師対医師の遠隔診療の基盤として地域医療に提供してきかた。主に救急医療現場にといて、非専門医に対して専門医がサポートする際に活用されてきた。昨今では、がん医療においての CancerBoard や訪問診療医と遠隔専門医との間やてんかん専門医とかかりつけ小児科医の間など、用途の幅は広がっている。昨今は、患者向けのパーソナルヘルスレコードや地域包括ケア向け介護・看護アプリなども提供しており、それらを遠隔診療アプリと繋げることで、包括的な医療・介護も実現している。ポータルブルな医療機器と遠隔診療を組み合わせた在宅介護、介護施設、医療過疎地やへき地と繋げて遠隔診療などでも、高度な医療も実現できるようになり、病院外のリアルワールドデータも集まり始めている。これらの新たな地域医療とデータ利活用の可能性について説明する。

― 58 ―

Panel Discussion

パネルディスカッション
日時：2023 年 10 月 26 日（木）13：30 － 15：00
会場：大ホール

次世代ヘルスケアの未来
The future of next generation healthcare【座長】

長神 風二（東北大学東北メディカル・メガバンク機構）
Fuji Nagami（Tohoku Medical Megabank Organization, Tohoku University）【 開催趣旨 】次世代の医療、ヘルスケアのために、ゲノム情報や医療・健康情報のデータの利活用が重要なことは論をまちません。
より大規模なデータを利活用して精緻な研究を行うために、一人一人の生涯にわたるデータについて、昨今は最新のモバイル
ヘルスの情報技術により日常生活についての細部にわたるデータについて収集が企図されたりするようになりました。一方で、
こうして収集された大規模なデータを利活用しきるために越えなくてはならないことには、制度、利活用する技術、マインド、
データを提供するための仕組み、あるいは思い、さまざまなことがあります。これからのヘルスケアを考えて、多様な立場から展望します。

パネリスト
吉澤 尚

弁護士 / バイオ戦略有識者

Nao Yoshizawa
lawyer/Expert Group on Bio-Strategy

荻島 創一
東北大学未来型医療創成センター / 東北メディカル・メガバンク機構

Soichi Ogishima
INGEM/Tohoku Medical Megabank Organization, Tohoku University

安中 良輔
日本製薬工業協会 産業政策委員会 / 第一三共

Ryosuke Annaka
Japan Pharmaceutical Manufacturers Association/DAIICHI SANKYO COMPANY

天野 慎介
全国がん患者団体連合会

Shinsuke Amano
Japan Federation of Cancer Patient Groups― 59 ―

S01

シンポジウム
日時：2023 年 10 月 24 日（火）17：10 － 18：40
会場：大ホール

若手研究者による AMED/BINDS インシリコ解析ユニットにおける創薬支援・高度化研究
Research activities of young researchers for supporting drug
discovery by in-silico unit of BINDS (AMED)【 モデレーター 】
広川 貴次（国立大学法人筑波大学）
Takatsugu Hirokawa（University of Tsukuba）【 開催趣旨 】 令和 4 年 4 月より AMED のプロジェクト BINDS（生命科学・創薬研究支援基盤事業）事業が開始されました。
本プロジェクトでは、優れた研究成果を創薬研究などの実用化研究開発に繋げることを目的に、6 つの研究領域別ユニット（構造活性、発現・機能解析、ヒット化合物創出、モダリティ探索、薬効・安全性評価、インシリコ解析）から構成され、最先端の生命科学・創薬研究を推進するための高度な研究支援を行っています。
本セッションでは、シミュレーション、AI、インシリコスクリーニングやデータ駆動型生命科学等による支援と高度化を行う
インシリコ解析ユニット 8 課題の中から、特に若手研究者より、インシリコユニットの支援・高度化研究の成果を発表していただきます。

S01-01

マルコフ状態遷移モデルを用いた上皮成長因子受容体キナーゼの活性化メカニズムの研究
A study of activation mechanism of epidermal growth factor receptor kinase using
Markov state model
井上 雅郎

横浜市大・院生命医科学

Masao Inoue
Grad. Sch. Med. Life Sci., Yokohama City Univ.

様々な癌との関連が報告されている上皮成長因子受容体（EGFR）のキナーゼドメインは、活性化の際にアシンメトリッ
クダイマーを形成し、レシーバー側のキナーゼが活性化される。この活性化に伴う構造変化のメカニズムを、経路探索
MD（STRING 法）とマルコフ状態遷移モデルを用いて詳細に調べた。その結果、不活性状態から中間状態、活性状態へというレシーバーの段階的な構造変化と、ダイマー界面の構造変化の関係を明らかにできた。

S01-02

NSD2 変異体によるヌクレオソーム H3K36 メチル化異常亢進のメカニズム
Understanding the Mechanism of NSD2 mutation induced H3K36 Hypermethylation
Kumar Amarjeet
量子科学技術研究開発機構 量子生命科学研究所
Insitute for Quantum Life Science, Institutes for Quantum Science and Technology

Aberrant H3K36 hypermethylation due to point mutations in NSD2 (E1099K, T1150A) are linked to various formsof cancers. Understanding the mechanism of H3K36 hypermethylation would facilitate the designing of NSD2specific inhibitors. We studied the nucleosome-bound structures of the NSD2 wild-type and mutants using Molecular
Dynamics simulations. Our study suggests that the open-close dynamics of the autoinhibitory loop in NSD2 isregulated by i) a network of electrostatic interactions (salt-bridges) within the SET domain and between the SET
domain and the nucleosomal DNA and ii) interactions between the hydrophobic residues of the autoinhibitory loop
and the NSD2 SET domain hydrophobic patches. The mutations in NSD2 reorganize the network of electrostaticinteractions and the interactions between the autoinhibitory loop and hydrophobic patches, thereby causing the
autoinhibitory loop to assume open conformation more frequently. An open autoinhibitory loop conformation may
facilitate frequent H3 tail binding and H3K36 hypermethylation.

― 60 ―

S01-03

FMO 法に基づく創薬支援・情報基盤技術の開発
Development of drug discovery support and information basic technology basedon the FMO method
渡邉 千鶴

国立研究開発法人 理化学研究所

Chiduru Watanabe
RIKEN

量子化学計算手法の一種であるフラグメント分子軌道（FMO）法を用いた相互作用解析は、電子の挙動まで考慮した
詳細な相互作用を定量的に評価出来る。そのため創薬ターゲットタンパク質に対する阻害剤や抗体などの分子認識機構の解明において、相互作用解析支援ツールとして利用が広まっている。ここでは、FMO 計算による BINDS 支援事例、
加えて創薬 AI などに活用可能な量子化学計算データ蓄積のための FMO データベース（FMODB）構築に向けた情報基盤技術について紹介する。

S01-04AlphaFold2 を活用した分子設計
Molecular Design with AlphaFold2
大上 雅史

東京工業大学

Masahito Ohue
Tokyo Institute of Technology

AlphaFold2 による高精度なタンパク質立体構造予測が実現し、生命科学・創薬研究は大幅に加速した。単鎖のタンパ
ク質配列からの立体構造のモデリングのみならず、タンパク質複合体予測、ペプチド - タンパク質ドッキング、ペプチ
ド配列スクリーニング、抗体モデリングなど、創薬に関わる様々な問題に AlphaFold2 が応用されている。本講演では、AlphaFold2 を中心としたインシリコ解析技術について、我々の取り組みとともに紹介する。

― 61 ―

S02

シンポジウム
日時：2023 年 10 月 25 日（水）17：10 － 18：40
会場：大ホール

学術変革領域研究（A）天然物が織り成す化合物潜在空間が拓く
生物活性分子デザイン
Latent Chemical Space Based on Diverse Natural Products for
Bio-active Molecular Design【 モデレーター 】
菊地 和也（大阪大学）
鎌田 真由美（京都大学）
大上 雅史（東京工業大学）
Kazuya Kikuchi（Osaka University）
Mayumi Kamada（Kyoto University）
Masahito Ohue（Tokyo Institute of Technology）【 開催趣旨 】2023 年度より、科研費 学術変革領域研究（A）として「天然物が織り成す化合物潜在空間が拓く生物活性分子デザイン」（潜在空間分子設計）がスタートしました。天然物と合成化合物ライブラリーを活用した生物活性分子はケミカル
バイオロジーの発展の駆動力となってきましたが、本領域ではこれらに続く第３のリソースとして化合物潜在空間（Latent
Chemical Space）を提案し、天然物と情報学研究との融合によってデータ駆動型ケミカルバイオロジー研究を推進します。
本セッションでは、当領域の概要と各計画研究班での取り組みについて紹介します。

S02-01

領域挨拶および領域概要と研究計画
Overview of Latent Chemical Space Based on Diverse Natural Products for
Bio-active Molecular Design
菊地 和也
大阪大学

Kazuya Kikuchi
Osaka University

天然物（第１）と合成化合物ライブラリー（第２）という２つの化合物リソースを活用した生物活性分子の発見・同定
は，化学と生物学の融合分野であるケミカルバイオロジー研究推進の駆動力となってきた．本領域では，これらに続く第３のリソースを提案する．この第３のリソースは，天然物の生物活性データを基に深層学習技術によって構築される化合物潜在空間（Latent Chemical Space）からバーチャルに創成され，強固な有機合成技術で実空間に具現化されるものである．この実現に向け，ケミカルバイオロジー，情報科学，有機合成の３班構成による「サイバー生物活性分子デザインラボ」を始動する．この第３のリソースから創出される化合物を起点とし，新しい生命機能解明や医薬・農薬
シーズに結び付く画期的分子を高効率に開発できる生物活性分子デザイン法の新学理構築を目指す．

S02-02

化学遺伝学評価系に基づく化合物潜在空間の生物活性検証と作用機序解明
Understanding the Biological activity and Mechanism of action of Compounds’
Latent Spaces using a Chemical genomics approach
出井 晶子

国立研究開発法人 理化学研究所

Akiko Idei
RIKEN

多種多様な有機化合物の構造に対し，生物活性を予測しうる法則性を見出し，精度の高い予測技術を確立することができれば，生物活性分子デザイン法の開発が可能となる．当班は，化学遺伝学に基づく標的分子の作用機序解明や，大規模な化合物評価による構造活性相関データの取得を通じ，各種化合物に対するマルチモーダルデータを提供することを目的とする．化合物潜在空間構築技術の確立に向けた取り組みについて，研究計画や検討中の事例を紹介する．

― 62 ―

S02-03

化合物潜在空間の構築によるバーチャル化合物構造の探索と設計
Deep generative model of constructing chemical latent space for large molecular
structures with 3D complexity
榊原 康文

慶應義塾大学

Yasubumi Sakakibara
Keio University

化合物潜在空間は，化合物構造を数学的空間に射影したものであり，化合物ライブラリー内の構造的多様性を表現し，新たな化合物構造を生成することができる．天然化合物に見られるような複雑な化合物構造を取り扱う化合物潜在空間を構築できる既存手法は存在しない．本研究では，天然化合物を扱うための変分自己符号化器に基づく新しい深層学習法，NP-VAE を開発し，キラリティを含む大きく複雑な化合物構造を射影する化合物潜在空間を構築した．

S02-04

Beyond Rule-of-Five 空間の AI 分子設計
AI-driven molecular design on beyond-rule-of-five chemical space
大上 雅史

東京工業大学

Masahito Ohue
Tokyo Institute of Technology

天然物ケミカルバイオロジーの発展によって，天然物から革新的な生物活性分子が見出されてきた．これら天然物の多くは経口医薬品に関するリピンスキーの Rule-of-Five 則を満たさず，Rule-of-Five の外の空間，すなわち Beyond Ruleof-Five に位置する．我々の AI 分子生成による化合物設計や AlphaFold による分子設計の事例から，Beyond Rule-ofFive 空間に眠る革新的な化合物の獲得に向けて何ができるか議論できれば幸いである．

S02-05

アロタケタール類の単純化アナログの設計、立体異性体の系統的合成と PKC 結合活性
Design, synthesis and PKC binding activity of simplified analogs of alotaketals
塚野 千尋

京都大学大学院 農学研究科

Chihiro Tsukano
Kyoto University

セリンスレオニン特異的リン酸化酵素であるプロテインキナーゼ C（PKC）は，がんやエイズなどの難治性疾患の治療標的として注目されている．最近，我々は，榊原（慶大理工）らと共同で機械学習モデルを用いた PubChem データベー
スのスクリーニングにより，海綿由来のアロタケタール類が新規 PKC リガンド候補化合物であると予測した．本発表
では，アロタケタールを簡略化した単純化アナログの設計と系統的合成およびそれらの PKC アイソザイム C1 ドメイ
ンに対する結合試験の結果を紹介する．

― 63 ―

S03

シンポジウム
日時：2023 年 10 月 26 日（木）13：30 － 15：00
会場：福寿

MPS（生体模倣システム）の今後の展開を考える
Future development of Microphysiological Systems (MPS)【座長】

石田 誠一（崇城大学大学院）
山崎 大樹（国立医薬品食品衛生研究所）
Seiichi Ishida（Sojo University）
Daiju Yamazaki（National Institute of Health Sciences）【 開催趣旨 】Microphysiological Systems（MPS：生体模倣システム）は、スライドガラスからマルチウェルプレートのサ
イズの培養器に配置された複数の培養コンパートメントに様々な臓器由来細胞を培養し、培地を循環させることで組織や臓器内の血液の流れを模倣し、血流が細胞に及ぼす力学的影響と共に、細胞の栄養素や代謝老廃物、または薬剤などの物質移動の影響を in vitro で再現できる培養装置である。動物試験を主としてきた医薬品開発や食品、化粧品、化学物質の安全性評価における次世代の評価系として、製薬企業をはじめとして、世界のライフサイエンス業界がその研究の動向に注目している。国内では AMED の支援のもと、MPS の社会実装と行政的利活用を目指したプロジェクトが開始されている（AMED-MPS2 プ
ロジェクト、MPS-RS プロジェクト）。
そのようななか、本シンポジウムでは MPS の感染症研究への展開に焦点を当てる。
COVID-19 パンデミックで広く認識されたように、今後は世界規模で感染症との取り組みが求められている。既に、米国では NICEATM（National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological
Methods）らにより MPSCoRe（Microphysiological Systems for COVID Research）working group が組織され、活発な議論がなされてきている。
感染症研究は、予防、発症メカニズムの探求、ワクチンや抗ウイルス薬の開発などヒトを対象とするだけでなく、自然宿主となる動物種での研究など広範囲におよび、ヒトや動物を模倣する事が可能な MPS の活躍が求められている。
そこで、本シンポジウムでは、まず感染症研究への MPS の適用の現状を紹介した後、国内で第一線で活躍されるお二人の研究者より、最新の研究成果についてご報告をお願いし、MPS の感染症研究への展開について議論したい。

S03-01

始めに：MPS と感染症研究
Introduction: MPS and Infectious Disease Research
石田 誠一

崇城大学大学院

Seiichi Ishida
Sojo University

S03-02MPS を活用した新興再興感染症の病態解明研究と創薬研究
Elucidation of the pathophysiology of emerging and re-emerging infectious
diseases and drug discovery research using MPS
高山 和雄

京都大学 iPS 細胞研究所

Kazuo Takayama
Center for iPS Cell Research and Application, Kyoto University

S03-03肺の形成・機能・感染における三次元構造の重要性と、Pandemic preparedness への応用
Significance of three-dimensional structure in lung development, function, andinfection, and application to pandemic preparedness
永元 哲治

HiLung 株式会社

Tetsuharu Nagamoto
HiLung Inc.

― 64 ―

受賞記念講演
日時：2023 年 10 月 23 日（月）17：30 － 19：30
会場：瑞雲・平安

若手奨励賞
Oxford Journals - Japanese Society for Bioinformatics Prize 2022
【 開催趣旨 】CBI 学会では、化学と生物学の境界領域における計算機と情報学の応用（Chemo Bio Informatics）において著しい業績をあげ、Chem Bioinformatics の発展に貢献した方に対して CBI 学会賞、CBI 学会若手奨励賞を授与している。
＜若手奨励賞＞
Ühemo Bio Informatics の領域において独創的で優れた研究を成し遂げ、将来の活躍が期待される 若手の CBI 学会員もしくは学生会員に対して授与される CBI 学会若手奨励賞を、2023 年度は加藤 幸一郎先生（九州大学大学院工学研究院）、大上 雅史
先生（東京工業大学情報理工学院）が受賞されたので、ご講演いただく。
＜ Oxford Journals - Japanese Society for Bioinformatics Prize 2022 ＞
日本バイオインフォマティクス学会 (JSBi) は、バイオインフォマティクスに関わる研究において顕著な成果を収めた若手研究者を讃え、我が国におけるバイオインフォマティクスの発展に寄与することを目的として、Oxford University Press の協賛のもと、Oxford Journals - Japanese Society for Bioinformatics Prize の授与を行っている。本大会では、日本バイオインフォマティクス学会との連携企画として、昨年度、Oxford Journals - Japanese Society for Bioinformatics Prizeを受賞された齋藤 裕先生（産業技術総合研究所人工知能研究センター）にご講演いただく。

01

FMO データを用いた機械学習モデルの開発
Development of machine learning models using FMO data
加藤 幸一郎
九州大学

大学院工学研究院

応用化学部門Koichiro Kato
Department of Applied Chemistry, Graduate School of Engineering, Kyushu University

生体関連分子の立体構造研究や、薬物分子設計における相互作用研究では、タンパク質などを計算対象とした分子シ
ミュレーションの利用が一般的となっている。これらのシミュレーションにおいて分子のエネルギーや各原子に働く力
は、物理化学的描像に基づくポテンシャル関数で表現され、パラメータセットと合わせて古典分子力場と呼ばれる。分
子シミュレーションの精度は使用する古典分子力場によって決定されるため、古典分子力場の更なる精度向上が期待されている。しかしながら、タンパク質の生体高分子と医薬低分子化合物等との相互作用は非常に複雑であり、例えばハロゲン結合等の相互作用は単純なポテンシャル関数形では表現することは困難である。精度向上の意味では、計算対象となる分子系のダイナミクスを全て量子化学（QM）的に計算するという力業も考えられるが、たとえ「富岳」をもってしても計算コストの面で非現実的である。創薬をはじめとした生体関連分子研究にかかる時間・コストの更なる削
減、コロナウィルスの様なパンデミックに対応するための迅速な研究開発など、更なる分子シミュレーション活用が必須になっていく事は明白であるものの、そのためには古典分子力場の計算コストで量子化学（QM）レベルの計算精度の実現に向けた従来力場の枠組みに囚われない大転換、すなわちゲームチェンジが不可欠である。このゲームチェン
ジの鍵になると我々が注目し研究開発に着手しているのが機械学習力場である。これは QM 計算で作成したデータをArtificial Neural Network（ANN）により学習することで、低コストで高精度な力場を構築するというコンセプトの手
法 [1] である。類似手法も合わせて無機結晶材料や低分子化合物へと適用範囲の広がりを見せているが、巨大な生体関連分子で機械学習力場を構築した事例は未だ報告されていない。これは高品質なタンパク系の学習データセットの構築が困難であることが一因であると考えられる。
我々は、この高品質なタンパク系の学習データセットの構築をフラグメント分子軌道法（FMO 法）[2, 3] の活用により克服し、機械学習力場の構築に向けた検討を進めている。FMO 法により生み出される唯一無二のデータを用いた機械学習モデルの構築は、力場開発に留まらず非常に広範なポテンシャルを秘めていると我々は考えている。本講演では、
FMO データを用いた機械学習による原子電荷予測や相互作用予測、さらには機械学習力場構築に向けた状況を紹介する。
[1] Behler, J., Parrinello M., Phys. Rev. Lett. 98, 146401 (2007)[2] Fedorov, D. G., Nagata, T., Kitaura, K., Phys. Chem. Chem. Phys. 14, 7562-7577 (2012).
[3]	Recent Advances of the Fragment Molecular Orbital Method: Enhanced Performance and Applicability. ed. by
Mochizuki Y, Tanaka S, Fukuzawa K, Springer, (2021).

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02

AI で広がる分子設計の可能性
AI Expands Potential of Molecular Design
大上 雅史

東京工業大学

Masahiro Ohue
Tokyo Institute of Technology

深層学習に代表される AI（機械学習）技術の生命科学や化学領域への応用は、近年爆発的な広がりを見せている。
AlphaFold2 の登場は、タンパク質立体構造予測に関係する研究者のみならず、生物学や情報学の研究者が広く注目する一種の「祭り」を引き起こした。当然、AlphaFold2 を活用した研究成果の報告は多く見られるようになり、またハッキングもされてきた。現在では、複合体構造予測（AlphaFold-Multimer）、ペプチドドッキング予測、人工タンパク質設計（AfDesign）、リガンド当てはめ（AlphaFill）など、AlphaFold2 をベースとした予測技術の発展が急速に進んでいる。
本講演では、AlphaFold2 を軸に、情報学・AI が創薬分野へもたらす革新の可能性について、我々のこれまでの研究をふまえて議論したい。我々は、AlphaFold2 を活用した標的結合ペプチドの設計手法 1 や、抗体 CDR 配列設計手法 2などの検討を進めてきた。また、低分子創薬や天然物創薬に資する AI 技術開発も同様に行っており、タンパク質間相互作用を阻害するための低分子設計指針 3 や分子生成 4、グラフ深層ニューラルネットワークに基づく標的活性予測と解釈可能性の追求 5, 6 などの計算手法を開発してきた。大規模な自由エネルギー摂動法計算を実現するための AI 技術の応用や高速化実装も実施している 7。いずれも、いかに「有望そうな」分子を作れるかが焦点となっている。AI による予測はあくまで予測であり、時には嘘も混じるため、使う側もうまく使うことが必要となる。AI 技術とうまく付き合っていくことがこれからの創薬には必須であり、分子設計の可能性をさらに広げられるような AI 技術開発を目指していきたい。
1.	Kosugi T, Ohue M. Solubility-aware protein binding peptide design using AlphaFold. Biomedicines, 10(7): 1626, 2022.
2.	Ueki T, Ohue M. Antibody Complementarity-Determining Region Sequence Design using AlphaFold2 andBinding Affinity Prediction Model. In Proc. PDPTA’23, 2023.
3.	Kosugi T, Ohue M. Quantitative estimate index for early-stage screening of compounds targeting protein-protein
interactions. Int J Mol Sci, 22(20): 10925, 2021.
4.	Ohue M, Kojima Y, Kosugi T. Generating potential protein-protein interaction inhibitor molecules based on
physicochemical properties. Molecules, 28(15), 5652, 2023.
5.	Kengkanna A, Ohue M. Enhancing Model Learning and Interpretation Using Multiple Molecular Graph
Representations for Compound Property and Activity Prediction. In Proc. IEEE CIBCB 2023, 2023.
6.	ugita S, Ohue M. Drug-target affinity prediction using applicability domain based on data density. In Proc. IEEECIBCB 2021, 9562808, 2021.
7.	Furui K, Ohue M. Faster Lead Optimization Mapper Algorithm for Large-Scale Relative Free Energy
Perturbation. In Proc. PDPTA’23, 2023.

03

生体分子設計のインフォマティクス
Informatics for biomolecule design
齋藤 裕

産業技術総合研究所人工知能研究センター

Yutaka Saito
Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST)タンパク質、RNA、DNA は、いずれも配列構造をもつ分子であり、配列を変異させることでその機能を改変できる。
生体分子の設計は配列空間の探索問題であり、機械学習などの情報科学的手法が威力を発揮する。これまでに講演者らは、蛍光タンパク質の波長改変、酵素の活性向上および基質特異性改変、標的結合タンパク質の親和性改善、mRNAの翻訳効率向上など、様々な生体分子の設計問題に取り組んできた。その中で表現学習や言語モデルを用いた手法開発
はもちろん、実験系研究者との共同研究を通して実証まで行ってきた。本講演では、これらの研究事例を紹介するとともに、その中で見えてきた新しい問題についても議論したい。

― 66 ―

大会企画
日時：2023 年 10 月 26 日（木）13：30 － 15：00
会場：研修室Meet the legend【 モデレーター 】
高橋 一敏（味の素株式会社）
増田 友秀（東レ株式会社）
渡邉 怜子（大阪大学蛋白質研究所）
渡邉 博文（株式会社ウィズメーティス）
加藤 幸一郎（九州大学）
Kazutoshi Takahashi（AJINOMOTO CO., INC.）
Tomohide Masuda（Toray Industries, Inc.）
Reiko Watanabe（Institute for Protein Research, Osaka University）
Hirofumi Watanabe（WithMetis Co., Ltd.）
Koichiro Kato（Kyushu University）【 開催趣旨 】日本の創薬研究において、多大な貢献と成功をされたレジェンドを講師としてお招きしご講演頂きます。さらに講師の先生には、CBI 若手の会で募集した質問リストをお渡ししております。その中から、これはと思う質問にご回答頂きつつ、次代を担う若手研究者に向けたメッセージを頂きたいと思います。今年度は東海大学医学部の平山令明先生にご講演頂きます。
平山先生は、東京工業大学大学院を修了された後、Imperial college London 博士研究員や協和発酵工業（株）主任研究員などを務められました。研究分野としては、医薬品の化学構造や、抗生物質など創薬研究にご尽力され多くの特許の発明者と成られました。東海大学に移られてからは、ASE-Dock をはじめとした分子モデリングソフトウェアの開発や、Druglikeness（分子の薬らしさ）に関する先駆的なご研究、最近では、アカデミア創薬への取り組みや HLA と化合物との相互作用など幅広い分
野で活躍されてきました。また、創薬、X 線回折、量子化学などに関して、大学生、大学院生向けの教科書からブルーバック
スにまで及ぶ多数の著書のご執筆をされ後進の育成にも大変熱心に取り組まれております。

01

一創薬研究者の半世紀
A Half Century of a Drug Discovery Scientist
平山 令明

東海大学医学部

Noriaki Hirayama
Tokai University School of Medicine

私は X 線結晶解析者として研究を始めました。比較的若い頃に、約 300 種類程の低分子化合物の X 線解析を行い、分子構造および分子間相互作用の実相を学びました。この仕事を強力に支えてくれたのが CAD-4 という、今は幻の名機になった 4 軸型自動回折計でした。この時代に得た経験は分子の構造に対する「勘」という、非常に有用かつ貴重な財産になりました。その後、種々の計算手法で最適構造が比較的簡単に得られるようになっても、可能な限り実測値である X 線構造にこだわりました。自然科学者は、まず自然の有りの儘の姿を可能な限りたくさん見る必要があり、その経験がその人の洞察力を養うと、私は信じています。
その後、創薬に研究の領域を拡大する中で、分子力学計算、分子軌道法計算、定量的構造活性相関解析およびデータベー
ス構築・解析等を独学しました。そうした研究活動がその後の in silico 研究に発展して行きました。その過程で、私は幸いにも統合計算化学システム MOE という素晴らしいソフトウェアと運命的な出会いをすることになります。MOEの出現によって、研究の速度と幅そして質が一気に向上しました。
東海大学医学部では、多くの優秀で熱意ある共同研究者に恵まれ、数多くの創薬研究に携わることができました。また、患者と真摯に向き合う研究心に富んだ熱意ある医師達との議論は、創薬研究者が何をなすべきかを教えてくれました。
治療の最前線で、薬を正に武器として活用する医師達との意思疎通は非常に大切だと思います。
私が研究に関わった、慢性骨髄性白血病を対象とする PAI- １阻害剤の治験が現在第 III 相に入っています。またこの数
年エネルギーを傾けてきた抗線維症薬の開発研究は非常に興味深い展開を遂げています。一方、私自身の加齢黄斑変性の進行阻止ができる分子を何とか見つけたいと孤軍奮闘もしています。私の創薬研究は現在進行形です。

― 67 ―

SP

学会企画
日時：2023 年 10 月 26 日（木）13：30 － 15：00
会場：桃源

AMED の事業紹介（医療・介護・健康データ利活用基盤高度化事業，
革新的先端研究開発支援事業，橋渡し研究プログラム）
Introduction of AMED project of“PHR Data Distribution Infrastructure
for Advancement of Medical Care”,“Advanced Research & Development
Programs for Medical Innovation”, and“Translational Research Program”【 モデレーター 】
塩塚 政孝（国立研究開発法人日本医療研究開発機構）
Masataka Shiozuka（Japan Agency fo Medical Research and Development）【 開催趣旨 】学会員が医療実用化に向けた研究開発を進めるために，AMED 事業を活用頂けるよう学会趣旨に合った事業を
紹介する。

SP-01

医療・介護・健康データ利活用基盤高度化事業（医療高度化に資する PHR データ流通基盤構築
事業）について
AMED project of PHR data distribution infrastructure for advancement of medical
care
松宮 志麻

AMED ゲノム・データ基盤事業部ゲノム・データ研究開発課

Shima Matsumiya
Division of Health and Clinical Data, Department of Health and Clinical Data, AMED

我が国では、医療・ヘルスケア分野のデータを包括的に利活用するための医療 DX が進められている。これは、医療を効率的に提供したり、創薬等のバリューチェーンを構築したりするなど、国民健康の充実を目指すもので、行政及び関係業界が一体となり「全国医療情報プラットフォームの創設」や「電子カルテ情報の標準化」などを強力に推進している。その結果、医療データをリアルタイムかつ大容量に包括的に収集し分析できる医療データプラットフォームが構築されつつある。
AMED ゲノム・データ研究開発課は、ゲノム医療、個別化医療の実現に向け、ゲノムデータおよび健康医療データの基盤整備ならびに利活用を促進し、ライフステージを俯瞰した疾患の発症・重症化予防、診断、治療等に資する研究開発を進めている。
本会では、当課が所管する「医療・介護・健康データ利活用基盤高度化事業（医療高度化に資する PHR データ流通基盤構築事業）」）を紹介する。昨今のウェアラブル端末やスマートフォン等の普及に伴い PHR データが急速に蓄積されている。このような状況の中で、個人の日々の活動から得られる PHR データを取得・分析し、健康増進等に役立てる
PHR サービスが民間ビジネスとして立ち上がりつつある一方で、医師が効率的かつ適切にそれらの PHR データを確認
し診療に役立てる仕組みは未整備である。そのため本事業では、PHR データの流通基盤のプロトタイプを設計・開発し、多様な PHR 事業者が保管する PHR データを診療に広く活用するべく技術的な仕組みを整備し、上記の「全国医療情報
プラットフォームの創設」との整合性も念頭に、診察内容の一層の精緻化や遠隔診療との相乗効果など医療の質の向上に寄与することを目指している。

SP-02

革新的先端研究開発支援事業の概要について
Overview of Advanced Research & Development Programs for Medical Innovation
瀬古 玲

AMED シーズ開発・研究基盤事業部 革新的先端研究開発課

Akira Seko
Division of Innovative Research and Development, Department of Innovation and Clinical Research Center, AMED

AMED で行われている革新的先端研究開発支援事業（以下、革新先端事業）は、革新的な医薬品や医療機器、医療技術等を創出することを目的に、国が定めた研究開発目標の下、組織の枠を超えた時限的な研究体制を構築し、画期的シーズの創出・育成に向けた先端的研究開発を推進するとともに、有望な成果について研究を加速・深化するプログラムで― 68 ―

す。革新先端事業は、AMED-CREST、PRIME、FORCE、LEAP の４つの研究タイプから構成されています。
AMED-CREST、PRIME においては、毎年度 1 ～ 2 つの研究開発領域が設定され、通常は３カ年度にわたって公募が行われます。研究開発目標の達成に向けて、各研究開発領域の PS および PO が最適となる研究分野のポートフォリオの設計を行い、専門分野の研究者のみならず、様々な異分野からの研究者の参画を促し、革新的な研究成果を創出することを目指します。
一方、FORCE、LEAP は、AMED-CREST、PRIME 等で得られた基礎研究の成果展開を図るためのプログラムであり、支援を通じて AMED の疾患別事業や企業導出につながることを目指します。
本発表では、革新先端事業の概要および上記 4 つのタイプについてお話しします。

SP-03

橋渡し研究プログラム（研究費事業）のご紹介
Introduction of“Translational Research Program (Research and Development Grant
Program)”
塩塚 政孝

AMED シーズ開発・研究基盤事業部 拠点研究事業課

Masataka Shiozuka
Division of Translational and Clinical Research Center, Department of Innovation and Clinical Research Center, AMED

AMED では，アカデミア等の優れた基礎研究の成果を臨床研究・医療実用化へ効率的に橋渡しするために研究費等の支援を行っています。
AMED が実施する橋渡し研究プログラム 異分野融合型研究開発推進支援事業では，文部科学大臣が認定した橋渡し研
究支援機関の中から，異分野融合型研究開発シーズの支援を実施する 5 つの機関（東北大学・慶應義塾・京都大学・大
阪大学・九州大学）を公募により選定しています。これら 5 つの機関では，医歯薬系以外の領域の先端技術・知識を利活用し，研究の早期段階から医療応用に向けて開発するシーズを発掘・選定し，研究開発費を配分するとともに，伴走支援することにより育成を行っています。対象となるシーズは，当該機関内だけでなく機関以外が有する研究シーズについても支援しています。また，医療応用に向けたセミナー・シンポジウム等も実施しています。これらの取組を通して，異分野融合型研究開発シーズの早期支援を強化するとともに，橋渡し研究支援の質の向上を図ります。
橋渡し研究プログラムでは，異分野融合型研究開発推進支援事業の他にも，11 の橋渡し研究支援機関（北海道大学・
東北大学・筑波大学・国立がん研究センター・東京大学・慶應義塾・名古屋大学・京都大学・大阪大学・岡山大学・九州大学）の研究支援基盤を活用し，臨床研究中核病院と円滑な連携をとりつつ，シーズの研究開発を積極的に支援しています。対象とする研究シーズは，機関内外のいずれも対象としており，対象疾患や医薬品・医療機器等の区別なく，基礎から臨床までの研究開発段階に応じた切れ目のない支援メニューを用意している点を大きな特徴としています。
• 異分野融合型研究開発シーズ（橋渡し研究支援機関のうち５機関が公募・採択し研究費を配分）• シーズ A（橋渡し研究支援機関が公募・採択し研究費を配分）• p
 reF・シーズ F・シーズ B・シーズ C（橋渡し研究支援機関が支援シーズを審査後，AMED が公募・採択し研究費を配分）
これらは橋渡し研究支援機関が支援するシーズに対して研究費等の支援を行う，研究者の皆様にとって魅力的な研究支援プログラムです。まずは橋渡し研究支援機関にお気軽にお問い合わせいただき，医療技術の実用化に向けた研究開発実施のためにぜひとも有効活用ください。

― 69 ―

WS01

ワークショップ
日時：2023 年 10 月 23 日（月）13：00 － 17：00
会場：福寿

計算科学的手法による毒性予測手法開発に関する最新動向と今後について
Latest Trends and Future Development of Toxicity Prediction Methods
Using Computational Science
主催：奈良先端科学技術大学院大学・データ駆動型サイエンス創造センター
協賛：計算 ADMET 研究会【 モデレーター 】
船津 公人（奈良先端科学技術大学院大学）
庄野 文章（奈良先端科学技術大学院大学）
植沢 芳広（明治薬科大学）
Kimito Funatsu（Nara institute of science and technology）
Fumiaki Shono（Nara institute of science and technology）
Yoshihiro Uesawa（Meiji Pharamaceutical University）【 開催趣旨 】化学物質によって生じる種々の毒性を、動物実験等に頼ることなく計算科学的手法等により予測することは、動物愛護の観点および昨今のめざましい AI 技術の高度な発展にともない国際的潮流である。一方で創薬分野においても新規の医薬品開発の加速化等の観点からも国際機関をはじめ各国規制当局および業界を含めた研究機関が取り組みを進めている。本ワー
クショップは経産省令和５年度化学物質規制対策（毒性発現予測システムの活用促進に向けた課題等の調査）の一環として実施しており、国内外のすでに利用可能な「毒性」の「インシリコ予測」手法の現状について会場でのデモンストレーションも含め、最新の情報を共有しその方向性を知る機会とする。なお、奈良先端科学技術大学院大学は経産省省エネ型電子デバイス
材料の評価技術の開発事業（機能性材料の社会実装を支える高速・高効率な 安全性評価技術の開発 平成 29 年から令和 4 年）
において 28 日間反復投与毒性予測システム（AI-SHIPS）を構築しているが、本ワークショップでは環境毒性を含め広く毒性全般の予測手法について情報を共有し活発な意見交換を期待する。

ご挨拶
Message

内野 絵里香
経済産業省 製造産業局 化学物質管理課 化学物質リスク評価企画官

Erika Uchino
Director, Chemical Safety Office, Director for Chemical Risk assessment, Chemical Management Policy Division, Manufacturing
Industries Bureau, Ministry of Economy and Industry

WS01-01 本ワークショップの開催目的と意義について
The purpose and significance of this workshop
船津 公人

奈良先端科学技術大学院大学

Kimito Funatsu
Nara Institute of Science and Technology

本ワークショップは経産省令和５年度化学物質規制対策（毒性発現予測システムの活用促進に向けた課題等の調査）の一環として実施しているものであり、国内外のすでに利用可能な「毒性」の「インシリコ予測」手法の現状について会
場でのデモンストレーションも含め、最新の情報を共有しその方向性を知る機会としている。本ワークショップでは環境毒性を含め広く毒性全般の予測手法について情報を共有し活発な意見交換を期待する。

― 70 ―

WS01-02 有害性評価支援システム統合プラットフォーム（HESS）及び OECD QSAR Toolbox について
Hazard Evaluation Support System Integrated Platform (HESS) and OECD QSAR
Toolbox
櫻谷 祐企（独）製品評価技術基盤機構

Yuki Sakuratani
National Institute of Technology and Evaluation

リードアクロスによる反復投与毒性の評価を支援する有害性評価支援システム統合プラットフォーム（HESS）の概要を紹介する。また、OECD QSAR Toolbox の開発・運用動向について述べる。

WS01-03 Alvascience ソフトウェアスイートを用いた毒性・生物活性予測
Toxicity and biological activity prediction using Alvascience software suite
田坂 友彦

株式会社アフィニティサイエンス

Tomohiko Tasaka
Affinity Science Corp.

分 子 記 述 子・ フ ィ ン ガ ー プ リ ン ト 計 算 ソ フ ト ウ ェ ア と し て 定 評 の あ る alvaDesc を は じ め と す る alvaModel，alvaBuilder 等の Alvascience ソフトウェアスイート並びにノンコード AI プラットフォーム Multi-Sigma を用いた毒性・生物活性予測の事例を紹介する．

WS01-04 毒性・安全性評価を支える in sillico ソリューションの紹介
Introduction of in sillico solutions to support toxicity and safety assessment
今西 浩之

インフォコム株式会社

Hiroyuki Imanishi
INFOCOM CORPORATION

医薬品や一般化学物質の研究開発現場では、開発コストの削減やスピードアップ、動物愛護の観点から、（定量的）
構造活性相関（Quantitative Structure-Activity Relationship: QSAR）を含めた in silico 解析を行い、生体に投与したときの活性や毒性を予測することが行われています。統計ベース QSAR が搭載されている代表的なソフトである
MultiCASE 社の CASE Ultra は、変異原性・発がん性・心毒性・皮膚毒性など様々なエンドポイントの予測モジュー
ルを搭載しています。また、同社は、近年特に製薬業界で注目を集めているニトロソアミン類について、リードアクロスを用いた AI 設定をサポートすることができる QSAR Flex を上市しました。本講演では、これらの製品の成り立ち
から利用方法に至るまで紹介します。

WS01-05 Accurately and rapidly assesses the toxicity of chemicals by TOPKAT based solely
on their 2D molecular structure
Anand Krishnamurthy
BIOVIA, Dassault Systemes

TOPKAT accurately and rapidly assesses the toxicity of chemicals based solely on their 2D molecular structure.
TOPKAT uses a range of robust, cross-validated, Quantitative Structure-Toxicity Relationship (QSTR) models forassessing specific toxicological endpoints.
The computational toxicology in TOPKAT is useful for:1. Rapidly assessing a broad range of toxicity of an organic compound solely from its 2D molecular structure
2.	Examining structure-toxicity relationships as a function of substructure and potential changes in structure andrelating these to mechanism of action
3. Ranking compounds for experimental testing or further development
4.

Designing new molecules― 71 ―

WS01-06 Data Driven Federated Learning : Application to toxicity prediction
Thierry Hanser
Lhasa Limited

Federated Learning (FL) allows to share knowledge across multiple organisations whilst preserving information
privacy. In recent years this emerging approach has been applied in Molecular Discovery [1] enabling the accessto knowledge previously locked in private corporate silos. In this presentation we will describe a new data-driven
Federated Learning paradigm called FLuID (Federated Learning using Information Distillation) which presents
several advantages over current popular model-driven methods. We will present an overview of the approach andhow it can be applied to improve toxicity prediction.
[1] Hanser T. Federated learning for molecular discovery. Current Opinion in Structural Biology. 2023 Apr 1;79:102545.

WS01-07 説明可能 AI 技術による ADMET 予測の試み
Attempts to predict ADMET using explainable AI technology
吉田 琢哉

富士通株式会社

ソーシャルソリューション事業本部

Healthy Living ライフサイエンス事業部

Takuya Yoshida
Fujitsu Limited

当社では、独自の説明可能な AI 技術の組合問題を高速に計算可能な Wide Learning 技術を開発している。当技術を ADMET 予測へ適用し、ADMET に起因する可能性のある要因の組合を計算することで、化合物構造の特徴や
ADMET メカニズムとの関係性を明らかにする試みについて紹介する。

― 72 ―

TS01

チュートリアル
日時：2023 年 10 月 23 日（月）13：00 － 17：00
会場：307

ケモインフォマティクス入門
Introduction to Chemoinformatics【 モデレーター 】
大川 和史（旭化成ファーマ株式会社）
芹沢 貴之（第一三共株式会社）
高橋 一敏（味の素株式会社）
宮野 奈津美（帝人ファーマ株式会社）
Kazufumi Ohkawa（Asahi Kasei Pharma）
Takayuki Serizawa（DAIICHI SANKYO COMPANY, LIMITED）
Kazutoshi Takahashi（AJINOMOTO CO., INC.）
Natsumi Miyano（TEIJIN PHARMA LIMITED.）【 開催趣旨 】バイオインフォマティクス分野と同様にケモインフォマティクス分野でも多くの Open source software（OSS）
が研究に活用されているが、一方で体系的にまとまった情報は少なく実業務への活用は試行錯誤が必要である。そこで本セク
ションでは、ケモインフォマティクスの OSS ツールとして標準的に利用されている RDKit を用いたケモインフォマティクス
実務者向けのハンズオンセミナーを行う。
具体的には以下の内容をハンズオン形式で実施する。
1．特定標的に関する特許から抽出した化合物データ解析
データの前処理（正規化、脱塩など）ケミカルスペースの可視化、クラスタリング
2．予測モデルの活用予測モデルの構築 ( データ準備 , モデル最適化 , モデル作成 )予測モデルの利用した化合物の検証
3．公共データを利用した Virtual Screening の実施
対象者ケモインフォマティクス中級者
・ Anaconda などを利用した環境構築などがご自身できる方
・ 実務で RDKit を利用している方、したい方
・ Python Programming の経験者

― 73 ―

TS02

チュートリアル
日時：2023 年 10 月 23 日（月）13：00 － 17：00
会場：401

第 32 回 FMO 研究会 FMODB 実践チュートリアル— 生体高分子の認識機構の FMO 法による解析：FMO 計算初心者から中級者に向けて
32nd FMO seminar, FMO Database tutorial【 モデレーター 】
高谷 大輔（大阪大学大学院薬学研究科）
渡邉 千鶴（国立研究開発法人理化学研究所）
加藤 幸一郎（九州大学）
Daisuke Takaya（Graduate School of Pharmaceutical Science, Osaka University）
Chiduru Watanabe（RIKEN）
Koichiro Kato（Kyushu University）【 開催趣旨 】フラグメント分子軌道（FMO）計算結果を収載した FMO データベース（FMODB;（https://drugdesign.riken.
jp/FMODB/）は、2019 年 2 月の一般公開以来データ数を増やし、2023 年 3 月 23 日時点で 16816 構造を公開している。
最近では、富岳等のスパコンを用いた MD スナップショット等の大規模データを用いた FMO 計算が始まっている。また、創薬ターゲットタンパク質の静的な構造に対する解析だけでなく、動的な構造に注目した FMO 解析及びデータ収集の取り組みが広がっている。本セッションではまず FMO 計算を始めようと考えているユーザー向けに FMODB の概要を紹介し、実際に登録されているデータを用いて、創薬研究に用いるための解析方法について紹介する。またすでに FMO 計算を行っている中級者向けに、最新の研究事例を紹介する。この過程において、実際の FMO 計算の解析方法をチュートリアル形式で体験する。
FMODB の詳細を知りたい方、実際に FMO 計算結果を解析してみたい方のご参加をお待ちします。

TS02-01

FMODB の紹介とデータ活用方法
Introduction to FMODB and how to use the data
渡邉 千鶴

国立研究開発法人理化学研究所

Chiduru Watanabe
RIKEN

量子化学計算の一つである FMO 法により得られる相互作用エネルギー（IFIE/PIEDA）はタンパク質 - リガンド間相互作用解析及び創薬研究への応用が期待されている。私たちのグループでは FMO 計算データの蓄積を目的とした
FMODB を開発し、FMODD コンソーシアムのメンバーによる計算、自動化前処理プロトコールで計算された結果等のFMO データを閲覧するための簡便なインターフェイスをホームページから提供している。本セクションでは FMODBが提供する機能や開発状況について紹介する。また、FMODB データの活用事例として FMO 計算の Pre/Post-GUI である BioStation Viewer 等を用いた解析を実施する。

TS02-02

FMODB を活用したリガンド – タンパク質間の動的平均相互作用解析
Dynamically Averaged Interaction Energy Analysis between Ligand and Proteinusing FMODB
松本 大夢
九州大学

Hiromu Matsumoto
Kyushu University

リガンド ‐ タンパク質間の動的平均相互作用解析について、COVID-19 のメインプロテアーゼ（Mpro）を題材にFMODB の web インターフェイスおよび BioStationViewer を用いた解析を行う。関連研究の最新動向を紹介すると共に、一連の MD スナップショット構造に対する FMO 計算で得られた複数の cpf ファイル（IFIE などの各種数値が格納されたファイル）から１つの動的平均 cpf ファイルの作成を行い、動的平均 cpf ファイルを用いた解析を実践する。

― 74 ―

TS03

チュートリアル
日時：2023 年 10 月 23 日（月）13：00 － 17：00
会場：研修室リアルワールド医療データソン
RWD (real-world data) Medical Datathon【 モデレーター 】
茂櫛 薫（cBioinformatics 株式会社 / 東京医科歯科大学統合教育機構）
荻島 創一（東北大学未来型医療創成センター / 東北メディカル・メガバンク機構）
Kaoru Mogushi（cBioinformatics Inc. / Institute of Education, Tokyo Medical and Dental University）
Soichi Ogishima（INGEM / Tohoku Medical Megabank Organization, Tohoku University）【 開催趣旨 】次世代ヘルスケアの実現に向けて、ゲノム情報や医療・健康情報の⼤規模データの利活⽤が国内外で急速に進んでおり、創薬においてはすでにゲノム情報、医療情報の活⽤が強⼒なアプローチとなっている。そこで、いかにゲノム情報、医療・健康情報の基盤を形成し、⼤規模データを、データサイエンス・AI を活⽤して新しい医療・創薬の実現に挑むか ー その戦略を考えるあたり、実際に医療データを解析するデータソンを通じて、最新の⼤規模な医療データの状況、解析技術を共有する。
また、個⼈情報保護についてどうあるべきかについて議論し、今後私たちが向かうべき⽅向性を共有する。
新型コロナウイルス感染症のパンデミックは、医療データのリアルタイムの集計・分析ができない課題が浮き彫りになり、国は医療データの DX に取り組んでいます。もし、パンデミックが始まったときに医療データにアクセスできたら、私たちデー
タサイエンティストはなにができたのかー。時計をパンデミックがはじまった 2020 年に巻き戻して、その当時にリアルタイ
ムに医療データにアクセスできたとしたときに、いかなるエビデンスを創出して、医療に役立てることができたかに挑戦します。
リアルワールド医療データの解析にご興味がある方のご参加をお待ちします。当日は解析用のノート PC をご持参ください。
参加にあたっての留意点
中級者レベル以上の Python もしくは R のプログラミングスキルが必要です。
R であれば Base R もしくは Tidyverse 等、Python であれば Pandas 等によるデータ操作に習熟していることが望ましいです。

― 75 ―

SS01

スポンサードセッション

協賛：アマゾン ウェブ サービス ジャパン合同会社

日時：2023 年 10 月 24 日（火）13：30 － 15：00
会場：桃源

ゲノム科学領域における最新の AWS 活用〜基礎生物学研究所のゲノム解析プラットフォーム〜
Latest Applications of AWS in Genome Science:
The Genomic Analysis Platform at the National Institute for Basic Biology【 モデレーター 】
片岡 勇人（アマゾン ウェブ サービス ジャパン合同会社）
Yuto Kataoka（Amazon Web Services Japan G.K.）【 開催趣旨 】近年のゲノム科学を取り巻く環境は、目まぐるしい技術的進歩に伴い大きく変化しており、特に膨大な量のゲノムデータを解析する際に、計算環境が律速にならないスケーラブルな環境や煩雑なワークフロー構築を自動化する手段としてクラウドを活用されるケースが増えてる。本セミナーでは、基礎生物学研究所の重信先生よりゲノムデータ解析におけるクラウド活用の実際をご紹介頂くとともに、AWS からゲノム科学領域でご活用いただける豊富な AWS サービスおよび活用事例についてご紹介する。AWS の展⽰ブースでは、最新のゲノミクスサービス AWS HealthOmics のハンズオンをご紹介しておりますので、是⾮お⽴ち寄りください。

SS01-1

ゲノム科学領域でご利用が進む最新の AWS サービス及び活用事例のご紹介
Exploring the Latest AWS Solutions and Use Cases in Genome Science
鳥羽 祐輔

アマゾン ウェブ サービス ジャパン合同会社

Yusuke Toba
Amazon Web Services Japan G.K.

次世代のゲノム科学では、全ゲノム解析など大量のデータをセキュアに解析する環境が必要となり、スケーラブルな環境構築やワークフローの自動化にクラウドを活用いただくケースが増えている。AWS ではヘルスケア・ライフサ
イエンス領域への継続した投資の結果、豊富な業界特化型サービスやソリューション、そしてその活用をご支援する
サポート体制を提供している。本セッションでは、オミクスデータの保存、クエリ、分析ができる新サービス AWS
HealthOmics を含め、ゲノム解析用途で利用される AWS の最新サービスをご紹介するとともに、実際にゲノム領域において AWS を活用されているお客様のご活用事例をお伝えする。

SS01-2ゲノム科学におけるクラウド活用：独占型スパコン構築からデータ共有まで
Cloud Utilization in Genome Science: From Building Own Supercomputers to
Sharing Data
重信 秀治

自然科学研究機構 基礎生物学研究所

Shuji Shigenobu
National Institute for Basic Biology, National Institutes of Natural Sciences

近年、次世代シーケンシング（NGS）などの革新的な進歩により、私たち生物学者はゲノム情報や各種オミクスデー
タなどの大規模データを日常的に扱う必要に迫られているが、その対策として、クラウドコンピューティングが世界的にも注目されている。例えば米国では、NCBI は SRA（NGS データアーカイブ）を AWS のクラウド経由で提供するようになり、NHGRI は AnVIL と呼ばれるゲノム解析の統合的クラウドプラットフォームの開発を推進している。私はゲノム科学と進化学の基礎研究に従事しており、クラウドの使い所を模索してきた。本講演では、第一にクラウド上にハイパフォーマンスコンピューティング（HPC）環境を構築し、効率の良い計算を実現する手法を紹介する。AWS
ParallelCluster というサービスを活用することによって、自分専用の理想的なスパコン環境を手に入れることができる。
第二に、クラウドの特性を活かしたデータ共有の戦略について議論する。特に、新しくゲノムを解読した非モデル生物
において、ゲノムブラウザを基盤としたプラットフォーム上でゲノム情報やアノテーションデータを迅速に共有し共同研究を促進する試みについて紹介する。本講演ではこれらの事項について技術と運営（事務手続きや費用の問題を含む）の両面から議論したい。

― 76 ―

SS02

スポンサードセッション

協賛：クラリベイト・アナリティクス・ジャパン株式会社

日時：2023 年 10 月 24 日（火）13：30 － 15：00
会場：福寿

創薬研究における大規模データ／リアルワールドデータ (RWD) の活用と分析ソリューション
Large-scale data/real-world data (RWD) utilization and analysis
solutions for drug discovery research【 モデレーター 】
クラリベイト・アナリティクス・ジャパン株式会社
Clarivate【 開催趣旨 】創薬研究を成功に導くためにはターゲットをとりまく生物学的メカニズムの全容を把握し、適切な仮説を立てることが重要な一歩であると考えられます。近年では次世代シーケンシング技術やハイスループット実験技術が普及し、そこから膨大なデータを容易に得られるようになりました。しかし、それらの膨大なデータを的確に処理して生物学的知見を得ることは容易でないため、その需要は高まっています。また同様に医薬品開発におけるリアルワールドデータ（RWD）の活用は以前から注目されてきましたが、現在は開発期間と臨床試験コストを削減し、技術や規制の観点からの成功確率を高めるために
医薬品開発のライフサイクル全体で活用が促進されています。これらの新しいデータに対して多くの製薬企業では未だ試行錯誤しながら活用法を模索している現状があります。
本セッションでは創薬研究をサポートするための機械学習とバイオインフォマティクスのアプローチについてご説明し、疾患患者由来のデータやシングルセルデータを用いたターゲット探索や疾患の優先順位付け、シングルセルシーケンスから得られたデータを用いたバイオマーカー探索と患者層別化、等の過去の実施プロジェクトのケーススタディーをご紹介いたします。
また、これらのソリューションへの RWD の統合についてもご紹介させていただきます。
【 内容 】・C larivate が提供するバイオインフォマティクスサービスの特徴と創薬研究向け導入事例：
バイオマーカー探索と患者層別化、疾患の優先順位付け、ターゲット同定、リポジショニング、等およびこれらのソリュー
ションへのリアルワールドデータ（RWD）の統合* 本セッションは英語での講演となります。
Clarivate について
Clarivate™ は、イノベーションを加速する信頼性の高い知見や分析を提供する、世界的リーディングカンパニーです。当社の使命は、新しいアイデアから人生を変えるような発明を生む時間を短縮するための、実用的な情報とインサイトを提供する
ことにより、お客様が抱える難題を解決できるよう支援をすることです。学会・政府、ライフサイエンス・医療、専門家向け
サービス及び消費財、生産・技術の分野において提供し、発明やアイデアの発見、保護、そして商業化に関してお客様を支援すべく、深い専門知識に加え信頼性の高いサブスクリプションおよびテクノロジーベースのソリューションを提供しています。
Web of Science, Cortellis, および Derwent Innovation が Clarivate を代表するデータベースとなります。詳細については、
clarivate.com/ja をご覧ください。

SS02-1

Klein Cecilia
クラリベイト・アナリティクス・ジャパン株式会社
Clarivate― 77 ―

Clarivate Life Sciences and Healthcare

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to create a healthier tomorrow
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▪ 製品・ポートフォリオ戦略▪ アセットスクリーニング▪ 参入戦略▪ プライシング・市場アクセス戦略▪ 事業戦略▪ グローバルアクセスアドバ
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医薬品開発を支援するためのクラリベイト独自のコンテンツ

クラリベイト・アナリティクス・ジャパン株式会社
Marketing.jp@clarivate.com/jaClarivate.com/ja― 78 ―

SS03

スポンサードセッション

協賛：OpenEye, Cadence Molecular Sciences

日時：2023 年 10 月 24 日（火）13：30 － 15：00
会場：平安Orion® と数十億規模のリガンドベース、および構造ベース仮想スクリーニングを可能にする技術について
Orion® and Billion Scale Ligand and Structure Based Virtual Screening【 モデレーター 】
近田 千春（OpenEye, Cadence Molecular Sciences）
Chiharu Konda（OpenEye, Cadence Molecular Sciences）【 開催趣旨 】昨今、合成可能な分子の仮想化合物ライブラリーは数十億規模に達します。より新規性が高く、かつ活性の高い分子が見つかる可能性はケミカルスペースの大きさと関連しており、アクセスできるケミカルスペースが大きい程その可能性が高まることが報告されています。今後仮想化合物ライブラリーは益々大きくなっていくことが想定される一方、このような大規模ライブラリーを仮想的にスクリーニングするには膨大な計算資源が必要となる上、技術的にも困難、また高額な計算コ
ストもかかります。
OpenEye の Orion は、この数十億規模のリガンド、および構造ベースの仮想スクリーニングを可能とするクラウドベースのプラットフォームです。リガンドベース（FastROCS Plus）であれば約 1 時間、構造ベース（Gigadock™️）でも 24 時間程
度と超高速にスクリーニングを行うことができます。また、特に計算コストの高い構造ベースのスクリーニングについては、
既存の Gigadock に AI モデルを活用（Gigadock Warp）することでコストを 10 分の 1 まで抑えることが可能となりました。
このセッションでは、まず Orion 全般についてご紹介させて頂き、続いて大規模計算を可能にする技術と Orion でご提供している 3 つの手法（FastROCS Plus, Gigadock, Gigadock Warp）について詳しくご説明いたします。

SS03-1

Orion Small Molecule Discovery Suite についてOrion Small Molecule Discovery Suite Overview
近田 千春

OpenEye, Cadence Molecular Sciences

Chiharu Konda
OpenEye, Cadence Molecular Sciences

Orion は、創薬のための分子設計プラットフォームであり、クラウドのリソースを最大限に活用することで、大規模な計算もコスパよく行うことができます。例えば、数十億分子のライブラリーを使用したリガンドベースおよび構造ベースのバーチャルスクリーニングから始まり、結合様式予測、相対結合自由エネルギー予測、分子動力学シミュレーション、
ケモインフォマティクスなど多岐に渡って活用できる、Small Molecule Discovery Suite についてご紹介いたします。

SS03-2

数十億規模のリガンドベース、および構造ベース仮想スクリーニングを可能にする技術について
Billion Scale Ligand and Structure Based Virtual Screening
Mark McGann

OpenEye, Cadence Molecular Sciences

FastROCS Plus は、3 次元類似性を元に数十億の分子を迅速にスクリーニングできる、Orion のリガンドベースの仮想スクリーニング方法です。Gigadock は、Orion 内で実行される 10 億サイズの仮想ライブラリをドッキングできる構造
ベースのスクリーニング方法です。 Gigadock Warp は Gigadock の亜種で、ドッキングと AI モデルを組み合わせて使用することで、わずかな計算コストで Gigadock とほぼ同一のヒット リストを生成することができます。この講演では、Gigadock、Gigadock Warp、および FastROCS Plus の基礎となる技術とその成果についてご説明いたします。

― 79 ―

Accelerating Drug Discovery ThroughComputational Molecular Design
SCIENCE | SPEED | SCALESCIENCE: Use physics-based design and machine learning to advance
Biomolecular target exploration
Hit identification, hit-to-lead, and lead optimization
Binding affinity and molecular dynamics simulations
Membrane permeability pathway modeling
Quantum chemistry calculations
Pharmaceutical formulations

SPEED: Delivered the way you need withOrion’s web-based cloud native modeling platform
Desktop and Linux applications for local hardware
Cheminformatics and development toolkits
Expert consulting services

SCALE: Perform extreme-scale search and screening on
Billions-sized libraries using ligand (2D/3D) and structure-based (3D) methods
Millions of sequences from Next Generation Sequencing antibody data
Hundreds of thousands known and putative protein-ligand binding sites for off-target effects
Your proprietary libraries using combined physics-based and data-driven approaches

In Virtual Screening against the Enamine REAL ® Database

Want to learn more?
www.openeye.inc

Speak to a specialist today
openeye.inc/contact

Checkout our solutions
openeye.inc/solutions

Trusted Science. Delivered the Way You Need.

©2023 OpenEye, Cadence Molecular Sciences 03-2023― 80 ―

SS04

スポンサードセッション

協賛：イクトス株式会社

日時：2023 年 10 月 24 日（火）17：10 － 18：40
会場：桃源

Iktos における AI 創薬とコラボレーション事例紹介
AI-driven drug discovery at Iktos and successful collaboration cases【 モデレーター 】
藤 秀義（イクトス株式会社）
Hideyoshi Fuji（Iktos SA）【 開催趣旨 】近年、AI 技術の進化により、革新的な変化を目の当たりにする機会が増えました。その中でも、2022 年 11 月に公開された ChatGPT を皮切りに、ジェネレーティブ AI が急速に普及し、ビジネス、芸術、科学など様々な分野での活用が期待されています。本セッションでは、フランスの AI 創薬ベンチャー企業である Iktos 社のジェネレーティブ AI 技術等を紹介した後に、Iktos 社との提携を進めた国内製薬企業より２名の講演者をお招きし、実際の創薬研究におけるジェネレーティブ
AI の活用事例や Iktos 社との共同研究から得られた知見についてご紹介頂きます。本セッションが、ジェネレーティブ AI を活用した創薬研究の最新動向を知り、創薬研究における AI 活用の可能性を探る一助となれば幸いです。

SS04-1

創薬を加速する Iktos のジェネレーティブ AI 技術の紹介
Iktos Generative AI Technology to Accelerate Drug Design and Discovery
藤 秀義

イクトス株式会社

Hideyoshi Fuji
Iktos SA

2022 年 11 月より公開された ChatGPT を皮切りに、ジェネレーティブ AI が飛躍的に普及し、ビジネスや芸術、科学など多くの分野におけるジェネレーティブ AI の活用が期待されています。Iktos 社は、2016 年 10 月に設立されたフランスの AI 創薬ベンチャー企業です。創薬化学・ドラッグデザインに特化した AI 開発を専門としており、ジェネレーティ
ブ AI に基づく独自の革新的な創薬ソリューションを提供しています。Iktos 社では、メディシナルケミストを始めとする創薬に携わる研究者が中心となってジェネレーティブ AI を使いこなすことが重要だと考えており、メディシナル
ケミストが使いやすい SaaS ソフトウェアプラットフォームを提供しています。本講演では、Iktos 社のジェネレーティ
ブ AI を搭載した de novo ドラッグデザインプラットフォームの Makya と、逆合成解析のための独自の AI 技術に基づく合成計画ソフトウェアの Spaya を紹介した後に、最新の Active Learning 手法とドッキング計算を組み合わせて大規
模化合物ライブラリーのバーチャルスクリーニングを数日で実施可能にした DockAI について、ご紹介させて頂きます。
最後に、Iktos 社で進めている化合物合成の自動化プロジェクト Iktos Robotics についても最新情報を紹介し、Iktos 社が目指す創薬プロセスの変革についてお話させて頂きます。

SS04-2Iktos 社との共同研究を通した「AI 創薬」の取り組み
Practical efforts on“AI drug discovery”through research collaboration with Iktos
熊澤 啓子

帝人ファーマ株式会社

Keiko Kumazawa
Teijin Pharma Ltd.

帝人ファーマは 2022 年 4 月より Iktos 社と共同研究を開始し、Iktos 社 AI 技術の自社プロジェクトへの適用と共同での新規 AI 技術開発に着手した。プロジェクトの成功には、専門性の異なるメンバーが信頼関係を築き、チームとして同じ目的に向かって協働することが不可欠であると考える。今回の共同研究では、専門性の違いに加えて、言語や習慣、文化の違い、時差や Web 会議での限られた時間でのコミュニケーションの難しさといった問題にも直面している。それらの問題に対し、試行錯誤を重ねる中で得られた気づきや対応策について紹介する。また、コミュニケーション促進の一環として、Iktos 社への研究員の派遣も実施している。Iktos 社のオフィスで共に働く中で感じた印象についても紹介する。

― 81 ―

SS04-3

AI 創薬プラットフォーム「Makya」を活用した低分子創薬
Small molecule drug discovery utilizing the AI drug discovery platform‘Makya’
鈴木 亮

小野薬品工業株式会社

Ryo Suzuki
Ono Pharmaceutical Co., Ltd.

創薬において革新的な新薬候補を創製するためには、複数の課題を同時に解決する必要がある。その解決策の１つとして AI 創薬が脚光を浴びている。AI 創薬は、人間の経験や知識に基づいた試行錯誤に頼らず、データに基づいたアプロー
チによって迅速かつ効率的に化合物設計を行うことができる。
2022 年 3 月に当社は Iktos 社との AI 創薬技術を活用した創薬提携を発表した。本提携においては、Iktos 社の開発した AI 創薬プラットフォーム「Makya」を活用した低分子化合物の創製を行っている。本講演では Iktos 社との創薬提携の効果について、詳細な分析結果を報告する。

― 82 ―

SS05

スポンサードセッション

協賛：クオンティニュアム株式会社

日時：2023 年 10 月 24 日（火）17：10 － 18：40
会場：福寿

量子コンピューティング技術の発展と、創薬・医療分野における期待
Developments in quantum computing technology and expectationsin the fields of drug discovery, medical, and healthcare【 モデレーター 】
平岩 美央里（クオンティニュアム株式会社）
Miori Hiraiwa（Quantinuum K. K.）【 開催趣旨 】近年、量子コンピューティング技術はグローバルに急速な発展を見せている。『量子コンピュータ』というその名前は、研究者のみならず広く世に知れ渡り、将来的に期待の大きい新たなテクノロジーの一つとして確固たる地位を築いた。
将来的な「誤り耐性型量子コンピュータ」の実現が強く望まれる中、現在、アルゴリズム、ハードウェア双方の最新の課題点
を理解しながら研究開発を進めることに大きな意義が見出されている。一方、量子コンピュータ技術の産業分野における応用とその可能性を追求するためには、量子コンピュータ企業とエンドユーザー企業間で、そのユースケースについて密で継続的な議論が重要である。これまで、さまざまな産業分野において量子コンピューティング企業とエンドユーザー企業とのコラボ
レーションが実現してきた。
本講演では、グローバル視点での量子コンピューティング技術の進展に加え、クオンティニュアムでの研究開発、お客様との共同開発事例をベースに量子コンピューティング関連サービスについて述べる。また、今回は創薬・医療分野におけるその可能性について述べる。

― 83 ―CBI 学会 2023 年⼤会 スポンサードセッション SS05

量⼦コンピューティング技術の発展と、創薬・医療分野における期待クオンティニュアム株式会社
2023年10⽉24⽇（⽕）17:10 – 18:40
クオンティニュアム株式会社 平岩 美央⾥, ph.D, Enterprise Sales Executive
概要︓
近年、量⼦コンピューティング技術はグローバルに急速な発展を⾒せている。『量⼦コンピュータ』というその名前は、研究者のみならず広く世に知れ渡り、将来的に期待の⼤きい新たなテクノロジーの⼀つとして確固たる地位を築いた。将来的な「誤り耐性型量⼦コンピュータ」の実現が強く望まれる中、現在、アルゴリズム、ハードウェア双⽅の最新の課題点を理解しながら研究開発を進めることに⼤きな意義が⾒出されている。⼀⽅、量⼦コンピュータ技術の産業分野における応⽤とその可能性を追求するためには、量⼦コンピュータ企業とエンドユーザー企業間で、そのユースケースについて
密で継続的な議論が重要である。これまで、さまざまな産業分野において量⼦コンピューティング企
業とエンドユーザー企業とのコラボレーションが実現してきた。本講演では、グローバル視点での量⼦コンピューティング技術の進展に加え、クオンティニュアムでの研究開発、お客様との共同開発事
例をベースに量⼦コンピューティング関連サービスについて述べる。また、今回は創薬・医療分野におけるその可能性について述べる。
q クオンティニュアム社について︓
クオンティニュアム社（英名︓Quantinuum）は、最先端のイオントラップ型量⼦コンピュータと、
量⼦コンピュータ⽤ソフトウェアおよびアプリケーションを併せ持つフルスタック量⼦コンピュー
ティング企業です。⽶国、欧州、⽇本を含む7つの拠点で活動する480⼈以上の従業員の多くは科学者・エンジニアで構成されています。
商業・産業界における計算科学の更なる活躍において、クオンティニュアム社は最先端の量⼦コン
ピューティング技術ポートフォリオにより、化学、⾦融、最適化、サイバーセキュリティなど幅広い領域でお客様のアプリケーションの研究を加速します。
q 「H-Series」イオントラップ型量⼦コンピュータ︓
クオンティニュアムは、イオントラップ型量⼦コンピュータH-Seriesの開発および運⽤を⾏っています。H-Seriesは、⾼い忠実度（Fidelity）かつ全結合の量⼦ビット、回路途中での測定（Mid-circuit
measurement）、および量⼦ビットの再利⽤（Qubit reuse）などの機能を備えることで⾼度な回路設計が可能となり、ブレークスルーを狙う最新の量⼦アプリケーション研究をサポートします。
q 「InQuanto （インクァント） 」量⼦コンピュータ⽤量⼦計算化学プラットフォーム︓
InQuanto（インクァント）は、分⼦・材料シミュレーションの新時代を切り開く、エンタープライズ
向けの量⼦計算化学パッケージです。InQuanto （インクァント）の⾃由度の⾼いモジュール式ワー
クフローにおいて、最新のアルゴリズムと⾼度なサブルーチンやエラー補正技術を容易に組み合わせることが可能です。これらの機能は、計算化学者や量⼦アルゴリズム研究開発者の⽅に業界最⾼レベルの結果をお届けします。

お問合せ︓
miori.hiraiwa@quantinuum.com― 84 ―

The leading edge of quantum computational chemistry

InQuanto（インクァント）はQuantinuum（クオンティニュアム）の提供する最先端のPythonベースの量⼦計算化学
プラットフォームです。 InQuantoのコードを使⽤することで、 企業や研究機関に所属する研究者の皆様には、様々な量⼦
バックエンド上で最新の量⼦アルゴリズムを簡単にお試し頂けます。
InQuantoは、研究者が量⼦コンピューティングの可能性を理解し、各分野における複雑な分⼦や物質のシミュレーションの精度を⼤幅に向上させるために設計されています。Quantinuumの提供するソフトウェア開発プラットフォーム TKET（ティケット）上に構成されているため、Quantinuumの誇る最先端イオントラップ型ハードウェアやIBM Quantumハードウェアをはじめ、シミュレータを含む25以上の異なるバックエンドと互換性を持ちます。
InQuanto（インクァント）の特徴

1

ワークフローの改善により、⼀部の状態ベクトル計算を最⼤10倍⾼速化

2

新たなタイプの変分量⼦アルゴリズムを可能にするため、 多参照計算をサポートする様々なAnsatzを再構築

3

虚数発展と実時間発展を実現する⼀般化された変分量⼦ソルバー

4

フラグメント分⼦軌道法・密度⾏列埋め込み理論をはじめとする⼤きな系を扱うための⽅法を実装

5

4粒⼦密度⾏列を測定しVQEエネルギーを補正する新たなQRDM-NEVPT2法

InQuanto
解説ブログ*InQuantoの活⽤事例はぜひ弊社HP（ https://www.quantinuum.com ）をご覧ください― 85 ―

SS06

スポンサードセッション

協賛：サーモフィッシャーサイエンティフィック

日時：2023 年 10 月 24 日（火）17：10 － 18：40
会場：平安

最新鋭クライオ電子顕微鏡による新規創薬へのアプローチとその最前線
Approach to novel drug discovery with the cutting-edge cryo-EM【 モデレーター 】
前田 晋太朗（サーモフィッシャーサイエンティフィック）
Shintaro Maeda（Thermo Fisher Scientific）【 開催趣旨 】クライオ電子顕微鏡は、急速凍結された生体試料（以下試料）に電子線を照射することで、試料を直接可視化することが可能な装置です。また、染色や固定を行わないことでアーティファクトを排除しつつ、試料を確実にとらえることが可能です。クライオ電子顕微鏡を利用した生体試料へのアプリケーションは複数ありますが、単粒子構造解析はＸ線結晶構造
解析、NMR に並ぶ構造解析手法として認知されていています。この技術確立の功績により 2017 年 10 月、スイス・ローザ
ンヌ大学のジャック・デュボシェ教授、米コロンビア大学のヨアヒム・フランク教授、英 MRC 分子生物学研究所のリチャード・
ヘンダーソン教授の 3 氏が、ノーベル化学賞を受賞されたことも有名です。現在、単粒子構造解析はアカデミアのみならず製薬企業等に至る広範囲で利用されており、難結晶性タンパク質構造解析に加え、新薬開発さらにはワクチン開発等において絶大な効果を発揮しています。クライオ電子顕微鏡を利用した創薬は、市販化までの時間を大幅に短縮するだけでなく、成功確率を向上させることが可能となります。人類の記憶にも新しい、猛威を振るった新型コロナウイルスへのワクチン開発に代表されるように、次々と変異するウイルスへ対応したワクチン開発にクライオ電子顕微鏡が大きく貢献したことは周知の事実です。その他にも、クライオ電子顕微鏡は上述した通り、試料を可視化できることから高品質ドラッグデリバリー輸送体の作成へ応用されています。特に、AAV、リポソーム、LNP そしてナノディスクに代表されるドラッグデリバリー輸送体を手軽に確認することでき、統計学的解析と組み合わせることで、ナノメートルスケールで形態分析を可能としています。
以上のように、クライオ電子顕微鏡の利用は、従来の研究を飛躍的に発展させる可能性を有しております。本セッションでは、
クライオ電子顕微鏡についてご興味のある様々な分野の方のご参加をお待ちしており、最新の研究成果の共有から、最新鋭クライオ電子顕微鏡を利用した構造生物学、創薬研究について議論させていただきたいと考えております。

SS06-1

前田 晋太朗
サーモフィッシャーサイエンティフィック

Shintaro Maeda
Thermo Fisher Scientific― 86 ―

SS07

スポンサードセッション

協賛：アマゾン ウェブ サービス ジャパン合同会社

日時：2023 年 10 月 25 日（水）13：30 － 15：00
会場：桃源

創薬の未来へのカギ：クラウドで活用される HPC と生成系 AI
The Key to the Future of Drug Discovery:
Utilizing HPC and Generative AI in the Cloud【 モデレーター 】
片岡 勇人（アマゾン ウェブ サービス ジャパン合同会社）
Yuto Kataoka（Amazon Web Services Japan G.K.）【 開催趣旨 】近年の創薬研究を取り巻く環境は、目まぐるしい技術的進歩に伴い大きく変化しており、例えば、HPC（ハイパフォーマンスコンピューティング）や生成系 AI に関連するサービスがクラウド上で提供されることで、誰でも最新の技術を活用し、創薬プロセスの効率化、高速化を進める取り組みが始まっています。本セミナーでは、HPC に関わる取り組みとして、
クライオ電子顕微鏡やバーチャル富岳に関わる事例や最新サービスを、また、機械学習に関わる取り組みとして、近年注目を集めている生成系 AI を中心に創薬研究領域での最新事例やサービスをご紹介します。

SS07-1AWS の HPC への取り組みと創薬分野における事例
Exploring HPC on AWS and Use Cases in Drug Discovery
佐々木 啓

アマゾン ウェブ サービス ジャパン合同会社

Kei Sasaki
Amazon Web Services Japan G.K.

AWS は HPC 分野において様々な分野での HPC の革新的なプロジェクトに貢献している。本セッションでは、創薬研
究領域において、高エネルギーか早期研究機構におけるクライオ電子顕微鏡から取得した膨大なデータをクラウドリ
ソースで活用する事例と、AWS が独自に開発した低コスト低消費電力の HPC チップを用い、理化学研究所計とともに富岳上で開発された様々な高性能アプリケーションのポーティングに協同で取り組んでいる「バーチャル富岳」プロ
ジェクトについて概要をご紹介します。

SS07-2

創薬における機械学習の最新動向 - 生成系 AI がもたらすイノベーション
The Latest Trends in Machine Learning for Drug Discovery: Innovation Unleashedby Generative AI
森下 裕介

アマゾン ウェブ サービス ジャパン合同会社

Yusuke Morishita
Amazon Web Services Japan G.K.

製薬業界では、研究や臨床、製造、患者サポートに至る幅広い場面で AI/ 機械学習の活用が進んでいます。近年では「生成系 AI」と呼ばれる、テキストなどのコンテンツを非常に高い精度で生成する技術が注目されており、創薬研究においても大きな可能性が期待されています。本セッションでは、特に創薬研究領域の 機械学習 / 生成系 AI の活⽤において、AWS が提供しているサービスやそれにより実現できる取り組みや事例についてご紹介します。

― 87 ―

SS07-3

クラウドと生成系 AI を活用した創薬研究：タンパク質構造予測を例にAdvancing Drug Discovery with Cloud and Generative AI: A Case Study in Protein
Structure Prediction
石尾 千晶

アマゾン ウェブ サービス ジャパン合同会社

Chiaki Ishio
Amazon Web Services Japan G.K.

AlphaFold2 や OpenFold をはじめとしたタンパク質構造予測ソフトウェアは、その登場以来、創薬研究者にとって欠かせないものとなっています。しかし、これらのソフトウェアを実行するための環境構築やハードウェアの管理は、研究者にとって煩雑な作業です。そこで、AWS では、タンパク質構造予測を実行するためのソリューションとして、ウェ
ブフロントエンドと、機械学習や HPC のバックエンドを組み合わせた環境を、簡単に構築できる実装例を複数ご用意しています。本セッションでは、これらの実装例を中心に、AWS を活用することで、どのように研究プロセスの生産性を高めることができるかについてご紹介します。

― 88 ―

SS08

スポンサードセッション

協賛：富士通株式会社

日時：2023 年 10 月 25 日（水）13：30 － 15：00
会場：福寿

量子コンピューティング、HPC、AI が導く高精度シミュレーション技術の新展開と DMTA 研究コラボレーションプラットフォームによる創薬研究の効率・高速化
Accelerated Drug Discovery Research with Advanced Simulation via
Quantum Computing, HPC, and AI, in Conjunction with the DMTA
Research Collaboration Platform【 モデレーター 】
松本 大輔（富士通株式会社）
Daisuke Matsumoto（Fujitsu Limited）【 開催趣旨 】本セッションでは、先ず創薬分野におけるリード探索という重要なプロセスに焦点を当て、量子コンピュー
ティングと高精度シミュレーション技術の組合せによる次世代の探索プロセスについて紹介します。量子コンピューティング（Fujitsu Digital Annealer）は、膨大なフラグメントの組み合わせの中から最適な化合物を特定する能力に優れており、高精度シミュレーション技術は分子の相互作用や特性を詳細にモデル化するための強力なツールとして活用されます。
セッションの前半では Digital Annealer を用いた仮想大規模ケミカルスペースからの効率的なターゲットフォーカスドライブ
ラリー構築について紹介し、高精度シミュレーション技術については戦略パートナ―である Cresset 社から最新の技術動向について合わせ紹介します。
また、リード最適化プロセスにおける DMTA（Design Make Test Analyze）プラットフォームは研究プロセスの可視化やデータの一元管理、研究者（社内、CRO）間でのリアルタイムかつ高品質なコラボレーションを実現することで、候補化合物選定の飛躍的な効率化を促すことから、欧米を中心に多くの企業にて導入が進んでいます。
最新のシミュレーション技術と最適な創薬プラットフォームの融合により、HITL : Human-in-the-loop の高度実現に向けた取り組みと関連する技術についても紹介します。

― 89 ―

SS09

スポンサードセッション

協賛：株式会社ジーデップ・アドバンス / エヌビディア合同会社

日時：2023 年 10 月 25 日（水）13：30 － 15：00
会場：平安

生成 AI 時代の計算創薬を支える GPU 関連技術
GPU technology for generative AI based drug discovery era【 モデレーター 】
山田 泰永（エヌビディア合同会社）
Taiei YAMADA（NVIDIA Corporation）【 開催趣旨 】近年の AI/ 計算創薬には大規模な計算環境が必須であり、これまでも NVIDIA の GPU ハードウェアと関連ソフ
トウェアは「縁の下の力持ち」として、その進展を支えてきました。 さらに直近は ChatGPT や StableDiffusion の登場を契機に、様々な分野で大規模言語型モデルを基盤とした生成 AI の研究開発や実活用が一気に広がり、創薬分野への応用も活発になっています。
本セッションではこのような生成 AI 時代に向けた最新の GPU ハードウェア環境と一般的なソフトウェア環境、そして創薬・
ライフサイエンス分野に特化したソフトウェアや様々な取り組みについてもご紹介し、特に創薬向けの最新の生成 AI モデルを手軽に実行するためのマネージドサービス、およびフレームワークである「BioNemo」を重点的にご紹介します。

SS09-1

AI と HPC で加速する創薬研究―ゼウレカでの活用事例と Tokyo-1 の紹介―

牧口 大旭

株式会社ゼウレカ

Hiroki Makiguchi
Xeureka, Inc.

ゼウレカでは、最先端の AI やシミュレーション等の技術を駆使し、創薬研究における大幅な効率化と精度向上の実現を目指しており、本講演では、AI を活用したタンパク質３次元構造の生成や Billion スケールのバーチャルスクリーニ
ングを活用した Hit Discovery などのサービスを事例を交えてご紹介します。
このような事例にとどまらず、インシリコアプローチによる創薬研究の成果は年々加速度を増してふえていますが、国内の創薬研究の現場においては、多様な創薬課題を解決するために積極的に AI や大規模計算を活用していくといった点においては、欧米に比して遅れている現状があります。ゼウレカでは、近年躍進めざましい LLM を中心とした生成モデルの活用も含め、創薬研究を変革するための大規模な計算リソースの提供や、ノウハウの共有などを目的としたコミュニティ形成などを狙った、NVIDIA DGX H100 にて構築されるスーパーコンピューター「Tokyo-1」を年内にローンチする予定であり、その概要も併せてご紹介します。

― 90 ―

SW01

スポンサード企画
日時：2023 年 10 月 24 日（火）13：30 － 15：00
会場：402『創薬支援ソフト myPresto5』活用セミナー
Utilization seminar of a software package‛myPresto5ʼ for drug discovery
主催：一般社団法人バイオ産業情報化コンソーシアム【 モデレーター 】
一般社団法人バイオ産業情報化コンソーシアム
Japan Biological Informatics Consortium【 開催趣旨 】myPresto は、経済産業省、NEDO 及び AMED からの委託プロジェクトの中で開発された医薬品開発支援の分
子シミュレーションシステムで、無料で利用でき、国内 30 社以上で使われています。本セミナーでは、最新の myPresto についての紹介、操作デモ等を行います。

SW01-1

中村 寛則
株式会社バイオモデリングリサーチ

Hironori K. Nakamura
Biomodeling Research Co., Ltd.

― 91 ―

― 92 ―

― 93 ―

LS01

ランチョンセミナー

協賛：株式会社モルシス

日時：2023 年 10 月 24 日（火）12：15 － 13：15
会場：研修室CLARITY PV：医薬品安全性監視のためのトランスレーショナル・プラットフォーム
CLARITY PV: Translational platform for drug safety surveillance【 モデレーター 】
池上 貴史（株式会社モルシス）
東田 欣也（株式会社モルシス）
狩野 敦（株式会社モルシス）
Takashi Ikegami（MOLSIS Inc.）
Kinya Toda（MOLSIS Inc.）
Atsushi Kanou（MOLSIS Inc.）【 開催趣旨 】 医薬品開発において、in vitro 安全性薬理試験、in vivo 安全性評価、臨床試験および市販後調査など、様々な過程で、毒性・安全性データが得られます。非臨床試験で報告された有害・安全性事象は、その後の臨床試験、更には市販後調査まで、トランスレーショナル・セーフティの対象になります。医薬品開発の各段階で得られる有害事象や毒性・安全性の問題のうち、どれが次の段階に影響するかを同定することは、トランスレーショナル・セーフティを考える上で非常に重要です。
Chemotargets 社製 CLARITY PV は、医薬品の安全性薬理研究から市販後調査までのすべての毒性・安全性データを統合した、
トランスレーショナル・セーフティとファーマコビジランス（PV）のための新しいプラットフォームです。非臨床毒性および臨床安全性に関するデータから市販後レポートに至るまで、医薬品のライフタイム全体にわたる安全性に関するあらゆるデー
タを各分野の専門家が精査し、構造化および統合化して収載しています。従って、医薬品開発の全ての段階における安全性シ
グナルのシームレスな関係付けとトレーサビリティーが可能です。
本ランチョンセミナーでは、CLARITY PV の開発責任者の Jordi Mestres より Chemotargets 社におけるトランスレーショ
ナル・セーフティへの取組みと、医薬品開発における有害・安全性事象の早期予測に CLARITY PV が及ぼす影響について講演します。

LS01-01

医薬品の安全性シグナルの早期予測 : オフターゲット薬理予測から市販後データの機械学習
モデルまでEarly Anticipation of Drug Safety Signals: from Off-Target Pharmacology Prediction
to Machine Learning Models of Post-Marketing Data
Jordi Mestres
ケモターゲッツ合同会社
Chemotargets S.L.

In the era of Artificial Intelligence, Machine Learning and Large Language Models, access to quality data has becomeof upmost importance. The increasing availability of large amounts of quality in vitro affinity data has promoted thedevelopment of state-of-the-art platforms for predictive off-target pharmacology, such as CLARITY®. With these tools,
our ability to guide the design of novel active molecules for protein targets has increased dramatically. However, accessto large amounts of quality safety data is still missing. In this talk, I will introduce our recent efforts in drug safety datacollection and curation, how these quality safety data are made available for AI/ML modeling, and the impact for the
early anticipation of adverse drug events at all stages of the drug discovery and development process.
The talk will review, with some use case examples, the quality safety data feeds that are currently being offered:
ClarityOne: one integrated Spontaneous Reporting System data feed with deduplicated reports, standardized
terminology and COVID-corrected versions of VigiBase, FAERS, VAERS and JADER
ClaritySignals: the most comprehensive list of safety signals for drugs and drug clases but also for targets andtarget families extracted from disproportionate analyses of Spontaneous Reporting Systems but also from Congress
Abstracts
ClarityStrata: pre-calculated subpopulation analyses of safety signals across some of the main patient parameters
available in Spontaneous Reporting Systems (sex, age, weight and geography)
The session will finalize with a life demonstration of the new ClarityVista product (formerly known as ClarityPV),
the GUI interface and analytics platform to browse, search and analyze all safety data and signals in ClarityOne,
ClaritySignals and ClarityStrata to assist its user to get a clear view of drug safety data, from off-target
pharmacology to post-marketing signals.

― 94 ―

トランスレーショナル・セーフティ・プラットフォーム

>Z/dz Ws&/$5,7< 39は、医薬品のライフタイム全体にわたる毒性・安全性データを統合した、トランス
レーショナル・セーフティとファーマコビジランス（39）のための新しいプラットフォームです。
医薬品の安全性薬理学、非臨床毒性試験、臨床安全性試験および市販後調査の全てのレポートをキュレーションすることで得られた情報を、構造化して保存しています。医薬名、分子構造、副作用、適応症およびターゲットから検索して、医薬品の全ての開発段階における安全性シグナルのシームレスな関係付けとトレーサビリティーが可能です。
■主要な安全性情報の一覧表示

医薬品の一般名または製品名、分子構造、副作用、ターゲットタンパク質名で検索できる、
種類以上の医薬品と種類のワクチンに関する統合された安全性データ■トランスレーショナル・セーフティ

医薬品開発の各段階で得られる全ての毒性・安全性情報の関連性を解析することによる、トランスレーショナル・セーフティの変遷の分析および安全性シグナルの発見■ファーマコビジランス

承認されたすべての医薬品に関する、)$(569LJL%DVH-$'(59$(56データベースからの詳細な安全性イベントおよび、ファーマコビジランス関連パラメータの取得■薬物とクラス間でデータを比較

同じ、または異なる薬剤クラスのトランスレーショナル・セーフティの比較
安全性情報の統合
トランスレーショナル・
セーフティ・パス

安全性薬理安全性進化チャート
ターゲットと安全性の
関連性

非臨床毒性

$'0(プロファイル
医薬品添付文書の監視

臨床安全性

市販後シグナルの検出
医薬品クラス有害反応
医薬品の比較解析

市販後レポート

定期的な安全性レポート

7UDQVODWLRQDOSODWIRUPIRUGUXJVDIHW\VXUYHLOODQFH― 95 ―

&/$5,7<39のソリューション
&$/5,7<39は、トランスレーショナル・セーフティの問題点に対して、以下のソリューションを提供します。
■データの統合

LQ YLWURで行われる安全性薬理試験、非臨床毒性試験、および臨
床安全性試験の結果に関するレポート、また市販後調査における継続的なレポートなどに含まれる、医薬品ライフタイム全体
にわたる全ての毒性・安全性データの統合■レポート

定期的な安全性レポートの収集⚫ 最新の市販後レポートへの迅速なアクセス⚫ 臨床安全性事象の市販後調査⚫ 市販後の安全性シグナルの層別認識（地域、年齢、性別など）⚫ $'(  や$'5  のレポート数の出現速度と変遷

安全性シグナル検出■予測

市販後の副作用シグナル（$'(  および$'5  ）を予測することで、以下の支援を行います。
⚫ 重要な毒性マーカーの同定⚫ トランスレーショナル・セーフティ問題の検出⚫ 新薬の市販後安全性動向の予測⚫ 薬と副作用に関する監視対象リストの策定

トランスレーショナル・チャート■解析

以下のユーザーフレンドリーな可視化解析ツールを提供します。
⚫ トランスレーショナル・セーフティ⚫ 比較分析
⚫ 層別分析
⚫ 安全性予測 $GYHUVH'UXJ(YHQW
薬物有害事象 $GYHUVH'UXJ5HDFWLRQ 薬物有害反応

比較分析&KHPRWDUJHWV社 日本代理店

株式会社モルシス

ライフサイエンス部〒 東京都中央区八丁堀ジオ八丁堀

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85/KWWSVPROVLVFRMS

◼ 詳細につきましてはお問い合わせください。

◼ 記載の商品名、サービス名は各社の商標または登録商標です。
◼ 本カタログの内容は予告なく変更される場合があります。

― 96 ―



LS02

ランチョンセミナー

協賛：シュレーディンガー株式会社

日時：2023 年 10 月 24 日（火）12：15 － 13：15
会場：401

Schrödinger の提供する総合創薬支援サービスの概要：その技術的背景を中心にOverview of Schrödinger ’s Comprehensive Drug Discovery Support Services :Focused on the Technical Background【 モデレーター 】
市原 収（シュレーディンガー株式会社）Osamu Ichihara（Schrödinger KK）【 開催趣旨 】Schrödinger が提供する創薬支援サービスは、Modeling Service と呼ばれる小規模な受託計算から、開発候補
化合物の創成を目指した総合的な創薬アライアンスまで多岐にわたりますが、すべてのプロジェクトにおいて我々が自社創薬
プロジェクトで使用する最先端のテクノロジーを大規模に駆使し、長年にわたり培われた計算化学の専門知識を存分に活用します。例えば、中規模の支援サービスである Research Enablement で提供される代表的なサービスには以下のようなものがあります。
Hit Discovery Campaign では、最新のバーチャルスクリーニング技術を駆使して、より多様なヒット化合物を素早く見つけ出すことが可能です。このアプローチにより、有望な候補化合物を効率的に特定し、短期間で開発プロジェクトを前進させることができます。
Target Enablement では、ターゲットの構造情報の不足する、あるいは存在しない創薬プログラムに対して最先端のシミュ
レーション技術を駆使して構造モデルを提供し、構造ベースの薬物設計を実現します。このプロセスにより、厳密な分子レベ
ルでの薬物設計が可能となり、効率的な医薬品設計の実現に寄与します。
ADMET Anti-target FEP-Enablement では、CYP、hERG、PXR などのリスクを構造ベースのアプローチで克服し、安全性や薬物動態の向上を図ります。
Crystal Polymorph Prediction は、薬剤の製剤に関わるリスクを軽減するために重要です。最も安定した多形を特定することで、安定性や溶解性の向上を図り、製品の開発をより確実などなものにします。
本セミナーでは、FEP+ によるアフィニティー予測、AI 技術を駆使した超大規模 VS にとどまらず、量子化学計算による
ADMET プロパティ予測など、Schrödinger の創薬支援サービスの基盤となる技術について幅広く解説、ご紹介します。

― 97 ―

LS03

ランチョンセミナー

協賛：ドットマティクス株式会社

日時：2023 年 10 月 24 日（火）12：15 － 13：15
会場：407

Dotmatics Luma：データ統合、分析、AI を１つのプラットフォームで実現する
革新的ソリューション
Dotmatics Luma： Innovative solutions for data integration, analytics,
and AI on a single platform【 モデレーター 】
北村 博幸（ドットマティクス株式会社）
Hiroyuki Kitamura（dotmatics K.K.）【 開催趣旨 】マルチモダリティ研究の深化に伴い、データを迅速かつ効率的に収集、活用し、より有益なインサイトを導出する事が研究プロセス上の最重要課題として捉えられる事が加速しています。
同様に、非定型データ、機器から出力される大容量の生データ、画像ファイルなど様々な新しいデータフォーマットが発生し、従来の技術基盤では対応が難しいシステム側の課題も浮き彫りになって参りました。
Dotmatics では、この様な背景から 2021 年より、Prism、SnapGene、Geneious をはじめとした各領域に強みを持つア
プリケーションとのインテグレーションを開始しました。
そしてこの度、更に多様な領域、多様なデータセットを一元管理すべく、新たな技術基盤として、データ、分析、AI を 1 つのプラットフォームで実現する Databricks 社技術を採用し、R&D データの統合・解析に特化した革新的なソリューションを開発いたしました。
主なトピックとして下記 3 点がございます。
はじめに、ローコードのデータモデリングおよびクエリを軸に、既存システム、データとシームレスに接続し、大規模データ
セットの効率的な処理とモデリングを可能にいたします。この新しい基盤により、これまで実現の難しかったより多くのデー
タフォーマットへの対応、機器から排出される大規模データの自動収集・処理とデータ統合を行うことが容易になります。
次に、昨今のトピックの中心である、大規模言語生成モデルに基づく AI を搭載し、対話式コマンドによりユーザーはより簡単に、より深く、データ活用を実行できる様になります。
最後に、ユーザインタフェースの大幅改良を実施し、ユーザーインタラクションの効率をさらに向上させる計画となります。
来年初頭の正式リリースへ向け、日本国内向けへ向け初のては初の情報公開となるこちらの講演を是非ご確認ください。

LS03-01

福山 隆
ドットマティクス株式会社

Takashi Fukuyama
dotmatics K.K.

LS03-02

Zsolt Lepp
ドットマティクス株式会社

Zsolt Lepp
dotmatics K.K.

― 98 ―

LS04

ランチョンセミナー

協賛：株式会社 Elix

日時：2023 年 10 月 25 日（水）12：00 － 13：00
会場：研修室株式会社 Elix ランチョンセミナー
Elixʼs Luncheon Seminar【 モデレーター 】
波戸 園美（株式会社 Elix）
Sonomi Hato（Elix, Inc.）【 開催趣旨 】株式会社 Elix は「創薬を再考する」をミッションとした AI 創薬企業で、昨年６月に AI 創薬プラットフォーム「ElixDiscovery™」をローンチいたしました。本セミナーでは、科研製薬株式会社様をお招きし、導入事例をご紹介いただきます。
また最新の AI 創薬の動向や製品アップデートについても発表させていただきます。

LS04-01

大規模言語モデルの基本から最前線へ ― 創薬での応用例を中心に
井上 貴央

株式会社 Elix

Takahiro Inoue
Elix, Inc.

ChatGPT をはじめとする大規模言語モデルは、大規模な深層学習モデルを大規模なデータで訓練したものであり、様々な分野で注目を集めています。本講演では、大規模言語モデルに関する基礎事項や創薬での応用例について、最近の事例を含めて概説いたします。

LS04-02

科研製薬における Elix Discovery™ の導入及び活用事例
森本 恭平

科研製薬株式会社

Kyohei Morimoto
KAKEN PHARMACEUTICAL CO.,LTD.

科研製薬では、Elix Discovery™ の導入及び Elix との協業を通して、データ駆動型の創薬研究プロセスへの変革を進めています。本講演では、Elix Discovery™ の導入背景及びその活用事例についてご紹介いたします。

LS04-03

Elix Discovery™ の 1 年の歩み — SBDD モジュールの展開と構造発生の新機能
井上 貴央

株式会社 Elix

Takahiro Inoue
Elix, Inc.

統合型 AI 創薬プラットフォーム Elix Discovery™ を弊社がローンチしてから約 1 年が経ちました。この期間、お客様からのフィードバックをうけて、創薬に便利な機能の追加や UI/UX の改善に努めてまいりました。本講演では、Elix
Discovery™ による構造発生デモを通して、新機能の SBDD モジュール・人間参加型スコアを中心に製品をご紹介いたします― 99 ―

CBI学会
学会 2023 年大会
ランチョンセミナーのご案内
チョンセミナーのご案内
株式会社 Elix は「創薬を再考する」をミッションとした AI 創薬企業で、昨年６月に AI 創薬プラットフォーム「ElixDiscovery™」をローンチいたしました。本セミナーでは、科研製薬株式会社様をお招きし、導入事例をご紹介いただきます。
また最新の AI 創薬の動向や製品アップデートについても発表させていただきます。

SESSION
1 0 月 2510
2 5 日 水 12 : 0 0 -13 : 0 0

LS-04

会場 4 階研修室

大規模言語モデルの基本から最前線へ ̶ 創薬での応用例を中心に
ChatGPT をはじめとする大規模言語モデルは、大規模な深層学習モデルを大規模なデータで訓練したものであり、様々な分野で注目を集めています。本講演では、大規模言語モデルに関する基礎事項や創薬での応用例について、最近の事例を含めて概説いたします。
登壇者

株式会社 Elix Research Engineer 井上 貴央

科研製薬における Elix Discovery™の導入及び活用事例
科研製薬では、Elix Discovery™の導入及び Elix との協業を通して、データ駆動型の創薬研究プロセスへの変
革を進めています。本講演では、Elix Discovery™の導入背景及びその活用事例についてご紹介いたします。
登壇者

科研製薬株式会社 森本恭平様

Elix Discovery™の 1 年の歩み ̶ SBDD モジュールの展開と構造発生の新機能
統合型 AI 創薬プラットフォーム Elix Discovery™を弊社がローンチしてから約 1 年が経ちました。この期間、お客様からのフィードバックをうけて、創薬に便利な機能の追加や UI/UX の改善に努めてまいりました。本
講演では、Elix Discovery™による構造発生デモを通して、新機能の SBDD モジュール・人間参加型スコアを中心に製品をご紹介いたします。
登壇者

株式会社 Elix Research Engineer 井上 貴央※講演内容が変更となる場合がございます。

１階展示ホールにてブース出展を行っております 。
是非お気 軽にお立ち寄りください 。
出展日時

全日予定

出展場所 22 番ブース

― 100 ―

AI 創薬プラットフォームTM

FEATURE
Elix の特 徴
統合型
AI 創薬プラットフォーム

コンサル・サポート及びノウハウ提供

ケミストファースト
UI/UX

膨大な手間を掛けることなく、

個別具体的な課題に対する、徹創薬化学者から計算化学者まで

AI 創薬に必要な機能のほとんど底的な解決支援と惜しみないノが直感的に利用でき、研究者の知全てを一括で導入可能。

ウハウ提供。

見を最大限に引き出す UI/UX。

主要取引先

製薬企業

製薬企業

製薬企業

製薬企業

製薬企業

他多数の製薬会社様と取引実績がございます。

学術機関

パートナー

京都大学

お気軽にお問い合わせください。お待ちしております。
株式会社 Elix 〒102-0081 東京都千代田区 四番町 8-34 第 2 東郷パークビル 3 階elix-inc.com/jp/ info@elix-inc.com― 101 ―

製薬企業

LS05

ランチョンセミナー

協賛：エルゼビア・ジャパン株式会社

日時：2023 年 10 月 25 日（水）12：00 － 13：00
会場：401AI ベースのディスカバリーツール – EmBiology によるバイオロジー研究の促進
Empowering discovery with EmBiology- an AI-based research tool for biologists【 モデレーター 】
伊橋 彩（エルゼビア・ジャパン株式会社）
齋藤 美里（エルゼビア・ジャパン株式会社）
Aya Ihashi（Elsevier Japan K.K.）
Misato Saito（Elsevier Japan K.K.）【 開催趣旨 】製薬業界は、医薬品の研究開発において、高コスト、長いタイムライン、高失敗率という大きな課題に直面しており、これらの課題のほとんどは、不完全な情報に起因することがあります。
特定の疾患や薬物のメカニズムには、研究者が研究を進めるために必要な生物学の概念と関係性が広範に存在します。上下流の生物学的関係を視覚化する生物学ナレッジグラフアプリケーションである EmBiology は、トランスレーショナルサイエン
ティスト、細胞生物学者、バイオインフォマティシャンなどにより効率的な発見と調査をもたらします。高度なテクノロジー
を用いて、720 万を超えるジャーナル、3,450 万の抄録、43 万の臨床試験から生物学的関係を抽出し、直感的に理解できる方法でまとめることにより、研究者が重要なエビデンスを基にした AI 情報を用いて意思決定することを可能とし、治療法をより早く市場に出すことができます。EmBiology は創薬初期のプロジェクトで特に力を発揮し、「何を探したいのかわからない」、「データの海で迷子になる」際に、バイアスのかからない方法で圧倒的な量のデータを調べることができます。
生物学的な関係性は情報が理解しやすい方法で視覚化され、科学的な質問をベースとして検索をするために使われます。
例えば、研究者が EmBiology で潜在的な創薬標的である「EPAS 1」（内皮性 PAS ドメイン含有タンパク質 1、Hif2a としても知られる）を検索する場合、上流の関係性（ビジュアルの左側）は、潜在的なアンタゴニスト（その発現に負の影響を与える EPAS1 の上流のタンパク質）であるといえます（図参照）。この形式により、高度なデータスキルを必要とせずに、疾患の発症、進行、および薬剤の反応性をより深く理解できます。
EmBiology を用いて可視化され検索可能となった高品質のデータと AI がどうやってさらに効率的で創薬開発のプロセスを成功に導き、新しくもっと良い治療法を患者に迅速にもたらすかをご紹介します。
ぜひランチョンセミナーにご参加ください。

LS05-01

Sherry Winterエルゼビア B. V.

― 102 ―

情報計算化学生物学会2023年大会ランチョンセミナー
AIベースのディスカバリーツール
EmBiologyによるバイオロジー研究の促進
日時:
講演者:

2023年10月25日(水) 12:00-13:00

LS05

Sherry Winter: Director Biology and Biomedical Solutions, Elsevier B. V.
齋藤 美里: ライフサイエンスソリューションズ, エルゼビア・ジャパン

Empowering discovery with EmBiology - an AI-based research tool for biologists― 103 ―

情報計算化学生物学会2023年大会ランチョンセミナー
AIベースのディスカバリーツール
EmBiologyによるバイオロジー研究の促進
The pharmaceutical industry faces significant challenges in drug research and development, withhigh costs, long timelines, and a high failure rate – most of these challenges can be attributed tohaving incomplete information when it is needed most for making what are potentially very
costly decisions.
In any given disease or drug mechanism, there are a vast range of related biology concepts andrelationships that a researcher needs to understand to advance their work. EmBiology – a biology
knowledge graph application that visualizes up and downstream biological relationships –enables more efficient discovery and investigation by translational scientists, cell biologists,bioinformaticians and others. It uses advanced technologies to extract biological relationships
from more than 7.2 million full-text journal articles, 34.5 million abstracts and 430,000 clinical
trials and brings it together in an intuitive way to allow researchers to make AI-informed
decisions based on critical evidence – so they can more successfully bring therapies to market
sooner.
EmBiology is particularly powerful in discovery projects, providing an unbiased way to searchoverwhelming amounts of data, where ‘Sometimes you don’t know what you want to look for’and ‘Information is lost in a sea of data’.
Biological relationships are displayed visually in a way that makes information easy to understandand can be used to focus searches based on the scientific question being asked. For example, if a
researcher performs a search in EmBiology for a potential drug target, ‘EPAS 1’ (Endothelial PAS
domain-containing protein 1, also known as Hif2a), upstream relationships (on the left-hand sideof the visual) could identify potential antagonists – e.g., proteins upstream of EPAS1 thatnegatively affect its expression (See figure). This format enables deeper understanding of diseasedevelopment, progression, and drug responsiveness, without requiring advanced data skills.
Join us to learn how AI and high-quality data, visualised and searchable via EmBiology, can leadto a more efficient and successful drug development process, bringing new and better
treatments to patients faster.
製薬業界は、医薬品の研究開発において、高コスト、長いタイムライン、高失敗率という大きな課題に直面しており、これらの課題のほとんどは、不完全な情報に起因することがあります。
特定の疾患や薬物のメカニズムには、研究者が研究を進めるために必要な生物学の概念と関係性が広範に存在します。上下流の生物学的関係を視覚化する生物学ナレッジグラフアプリケー
ションであるEmBiologyは、トランスレーショナルサイエンティスト、細胞生物学者、バイオイ
ンフォマティシャンなどにより効率的な発見と調査をもたらします。高度なテクノロジーを用いて、720万を超えるジャーナル、3,450万の抄録、43万の臨床試験から生物学的関係を抽出し、直感的に理解できる方法でまとめることにより、研究者が重要なエビデンスを基にしたAI情報を用いて意思決定することを可能とし、治療法をより早く市場に出すことができます。EmBiology
は創薬初期のプロジェクトで特に力を発揮し、「何を探したいのかわからない」、「データの海で迷子になる」際に、バイアスのかからない方法で圧倒的な量のデータを調べることができます。生物学的な関係性は情報が理解しやすい方法で視覚化され、科学的な質問をベースとして検索をするために使われます。
例えば、研究者がEmBiologyで潜在的な創薬標的である「EPAS 1」(内皮性PASドメイン含有タン
パク質1、Hif2aとしても知られる)を検索する場合、上流の関係性(ビジュアルの左側)は、潜在的なアンタゴニスト（その発現に負の影響を与えるEPAS1の上流のタンパク質）であるといえます（図参照）。この形式により、高度なデータスキルを必要とせずに、疾患の発症、進行、および薬剤の反応性をより深く理解できます。
EmBiologyを用いて可視化され検索可能となった高品質のデータとAIがどうやってさらに効率的で創薬開発のプロセスを成功に導き、新しくもっと良い治療法を患者に迅速にもたらすかをご紹介します。
ランチョンセミナーに是非ご参加ください。
― 104 ―

LS06

ランチョンセミナー

協賛：株式会社 Preferred Networks

日時：2023 年 10 月 25 日（水）12：00 － 13：00
会場：407

Preferred Networks における AI 創薬への取り組みのご紹介
Introduction of AI Drug Discovery Initiatives at Preferred Networks【 モデレーター 】
石谷 隆一郎（株式会社 Preferred Networks）
山岸 純也（株式会社 Preferred Networks）
武本 瑞貴（株式会社 Preferred Networks）
Ryuichirou Ishitani（Preferred Networks, Inc）
Junya Yamagishi（Preferred Networks, Inc）
Mizuki Takemoto（Preferred Networks, Inc）
【 開催趣旨 】画像やテキストの認識・生成において飛躍的な発展をもたらした深層学習は、生物学や化学、特に創薬分野においてもその活用が広がってきています。これらの分野では、ゲノム情報、タンパク質や核酸の配列情報・立体構造情報、ペプチド、
低分子化合物など、多種多様なデータが対象となりえますが、それらを有効に活用し、大きな成果が出始めている分野もあります。本セミナーでは現在、Preferred Networks が注力している
１）深層生成モデルによる低分子化合物の自動設計
２）Preferred Potential のドラッグ様化合物における性能評価と応用
３）Preferred Networks における結合自由エネルギー計算パイプラインの開発の 3 つのテーマに関する講演を行います。また、アカデミアや製薬企業との創薬に関する共同研究を含む Preferred
Networks における AI 創薬への取り組みを、最新の動向も含め、ご紹介させていただきますので、是非お立ち寄りください。

― 105 ―

ランチョンセミナー/6

3UHIHUUHG1HWZRUNVにおける$,創薬への取り組みのご紹介
画像やテキストの認識・生成において飛躍的な発展をもたらした深層学習は、生物学や化学、特に創薬分野においてもその活用が広がってきています。これらの分野
では、ゲノム情報、タンパク質や核酸の配列情報・立体構造情報、ペプチド、低分子化合物など、多種多様なデータが対象となりえますが、それらを有効に活用し、大きな成果が出始めている分野もあります。本セミナーでは現在、3UHIHUUHG
1HWZRUNVが注力している

深層生成モデルによる低分子化合物の自動設計



3UHIHUUHG 3RWHQWLDOのドラッグ様化合物における性能評価と応用

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3UHIHUUHG 1HWZRUNVにおける結合自由エネルギー計算パイプラインの開発のつのテーマに関する講演を行います。また、アカデミアや製薬企業との創薬に関する共同研究を含む3UHIHUUHG 1HWZRUNVにおける$,創薬への取り組みを、最新の動向も含め、ご紹介させていただきますので、是非お立ち寄りください。
日時：（水）   
会場：
講演者： 石谷 隆一郎、山岸 純也、武本 瑞貴∃∑⇌∍∞∑⇌⇴∕∞∕√∓ ⇍⇍私たちはプログラミングに強く、技術への情熱を持つ皆様を歓迎します― 106 ―

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莫大な時間・コストがかかる化学・生物実験の削減と創薬の効率化を目指し、深層生成モデルやシミュレーションを活用した分子設計を主軸とした$,創薬技術で創薬研究を支援しています。京都薬科大学様との共同研究では、新型コロナウイルス感染症 &29,' 治療薬の開発を目指し、数十万の化合物を$,により自動設計しました。最終的に合成された化合物の半数以上 化合物 が阻害活性を有することを確認し、リード化合物の取得に成功しました。アカデミアや製薬企業との共同研究は随時進めており、上記以外のケースでも多数のリード化合物取得に至っています。

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創薬に提供可能な自社スパコン
台以上の*38を有する大規模なスーパーコンピュータを自社運用しており、大量の計算資源が必要な計算創薬のワークフローも実行可能です。例えば、タンパク質・リ
ガンド結合自由エネルギー計算のような高精度な計算を行うには多くの計算リソースが必要ですが、自社運用するスパコンを活用することで、多数の化合物やターゲットおよび計算条件を同時に計算することが可能となり、LQ VLOLFR 創薬プロセスの効率化が期待されます。また、データの保管に必要なストレージ等も社内で保有しているため、外部のサービスにデータを送信・保管することなくすべての計算を完結させることができます。

0DWODQWLV™（材料科学）
材料科学分野において、汎用原子レベルシミュレータ0DWODQWLV™を年月より提供しています ※ 。0DWODQWLV70 は、ニューラルネットワークポテンシャル
3UHIHUUHG 3RWHQWLDOを使用し、原子スケールのシミュレーションを量子化学計算')7 と比較して数十万から数千万倍程度高速に計算可能です。さらに種類の元素をサポートし、未知の素材や結晶などに対してシミュレーションできる汎用性を有しています。クラウドサービスとして提供されているためハードウェアやソフトウェアのインストールが不要であること、-XS\WHU/DEによるプログラミン
グで柔軟なカスタマイズが可能であることが特徴となっています。
※現在、0DWODQWLV70は材料分野への研究開発向けに 株 3UHIHUUHG&RPSXWDWLRQDO&KHPLVWU\が販売とサポートを行っております。

会社概要• 現実世界を計算可能にする• 最先端の技術を最短路で実用化する

https://matlantis.com/

基本情報
商号
株式会社3UHIHUUHG1HWZRUNV
設立
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事業内容
深層学習やロボティクスなどの先端技術を応用したソフトウェア・ハード
ウェア・ネットワーク技術の研究・開発・販売

主要子会社
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出資企業
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株式会社東京エレクトロン株式会社

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― 107 ―

LS07

ランチョンセミナー

協賛：株式会社ワールドフュージョン

日時：2023 年 10 月 26 日（木）12：00 － 13：00
会場：研修室LSKB のフロンティア : BERT（MNLI）文献解析と ChatGPT でわかること
LSKB Frontier: BERT (MNLI) Literature Analysis and
Its Adaptation to ChatGPT【 モデレーター 】
川原 弘三（株式会社ワールドフュージョン）
Kozo Kawahara（WorldFusion Co,. Ltd.）【 開催趣旨 】BERT や ChatGPT をフルに活用し、疾患とターゲット間の関係を示すクラス分類を拡充
このセミナーでは、ライフサイエンス分野での必須なデータとの新しいインタラクションを探求します。創薬、大学研究機関など 広く利用されております自社開発の統合ライフサイエンスプラットフォーム、LSKB の新機能を中心に、BERT（MNLI）
を用いた革新的な文献解析技術や PubMed のウィークリーアップデートなど、最新の機能やアプリケーションの紹介を致します。科学のフロンティアを探索し、LSKB がどのように業界の変革を牽引しているかをご理解いただけます。

LS07-01

LSKB と BERT（MNLI）による革新的文献解析
Innovative Literature Analysis with LSKB and BERT (MNLI)
緑川 淳

株式会社ワールドフュージョン

Atsushi Midorikawa
WorldFusion Co,. Ltd.

近年、AI を利用したバイオインフォマティクスとケムインフォマティックスの進化が急速に進行していますが、AI には土台となるトレーニングデータが必須です。
LSKB はこの進化の中心に位置しており、遺伝子、タンパク質、疾患、組織などの強力なオントロジーを中心に、さま
ざまな公共データや AI の活用で、最新の解析技術と相互作用データを提供しています。この講演では、BERT（MNLI）
による最先端の文献解析技術とその応用例を中心に、ターゲット探索の手法や、新しい PubMed 解析データのウィー
クリーアップデートデータの効率的活用方法や、Microbiome の情報展開へのアプローチについても紹介します。また、
これらの Evidence を効果的に確認するための ChatGPT との連携についても詳しく取り上げます。

― 108 ―

LS08

ランチョンセミナー

協賛：インシリコ・メディシン

日時：2023 年 10 月 26 日（木）12：00 － 13：00
会場：401

AI による創薬の変革
Transforming Drug Discovery Through AI【 モデレーター 】
陳 心怡（インシリコ・メディシン）
林 彥竹（インシリコ・メディシン）
Aggie, Hsin-I Chen（Insilico Medicine）
Jimmy, Yen-Chu Lin（Insilico Medicine）【 開催趣旨 】Insilico Medicine “Insilico”),(a generative artificial intelligence (AI)-driven clinical-stage biotechnology
company using itʼs Pharma.AI platforms. In this seminar, Insilico will introduce our Pharma.AI platforms andshare the successful experiences using AI technology to accelerate the drug discovery process.

LS08-01林 彥竹
インシリコ・メディシン

Jimmy, Yen-Chu Lin
Insilico Medicine― 109 ―

LS09

ランチョンセミナー

協賛：パトコア株式会社

日時：2023 年 10 月 26 日（木）12：00 － 13：00
会場：407

データ活用による医薬品開発の DX 促進
Promoting DX in drug development through data utilization【 モデレーター 】
宮崎 大祐（パトコア株式会社）
有賀 文昭（パトコア株式会社）
Daisuke Miyazaki（Patcore, Inc.）
Fumiaki Aruga（Patcore, Inc.）【 開催趣旨 】AI/ML を活用した創薬手法は各所において革命と呼べる変化を起こしつつある一方で、大量に存在するデータを効果的に活用することは多くの課題が残存している。
これらデータの管理・最適化について Excelra 社より先進的なアプリケーションを利用した実用化について具体的に示します。
また、「データ駆動型」および「仮説駆動型」アプローチを統合し、創薬の早期段階における DMTA（Design, Make, Test,
Analyze）サイクルを効率化するための画期的な情報プラットフォームとして Chemaxon 社の「Design HUB」をパトコア
より紹介いたします。

LS09-01

データ活用による医薬品開発の新展開
Harnessing the power of data to accelerate drug development
Jaokar Tulika
Excelra情報・データ時代の進展に伴い、医薬品研究分野は顕著な進歩を達成してきた。特に、高解像度データの生成技術、高速コンピューティング、先進的なアナリティクス、そして AI/ML を活用した予測モデリングは、従来の研究パラダイ
ムをデータ駆動型へと転換させている。化学、生物学、各種オミックス領域などから得られるデータの相関分析により、より総体的な洞察が可能となっている。
しかしながら、これらのデータを効果的に活用するための課題が残存している。具体的には、データの分散、データベースの構築・整理の難しさ、適切な解析パイプラインの欠如などが挙げられる。これらの課題が研究の洞察を得る過程を遅延させる要因となっている。
本講演では、Excelra の提供するデータ製品とソリューションが、これらの課題を解決するための取り組みと成果を中心に述べる。特に、統合されたデータ管理、解析手法の最適化、そして先進的アプリケーションの実用化について詳述する。加えて、GOSTAR データベースの効果と、それが医薬品研究にどのように貢献しているかを具体的に示す。
Excelra は、データ、知識、技術の統合を通じて、創薬・開発領域での新たな可能性を追求している。本講演を通じて、効率的な研究推進のための取り組みと、その実現に向けた方針について考察する。

LS09-02

試験依頼システム（仮称）のご紹介 ～化合物試験の計画的・効率的な依頼、Design Hub を添えて～Introducing the Assay Request System (tentative name) ～ Planned and efficientrequest for compound assay, with Design Hub ～蕪木 快

パトコア株式会社

Kai Kaburaki
Patcore, Inc.

化合物数の増大だけでなく、多様化・複雑化する生物・ADMET・物性などの試験において、試験依頼から完了まで様々な雑多なアクションが必要となっている。
加えて、受託可否・試験差し戻し・ペンディングなど、試験に纏わるさまざまな状況の確認・連絡などが依頼者・受託者の負担となっている。
そのような試験依頼に関連する様々な課題を解決し、Design Hub との連携で効率的な DMTA サイクルの効率化を図る「試験依頼システム（仮称）」を紹介する。
Not only is the number of compounds increasing, but also the diversification and complexity of living organisms,― 110 ―

ADMETs, physical properties, etc.
Assays require various miscellaneous actions from assay request to completion.
In addition, various aspects related to assay, such as acceptability, remand, and pending,the client/trustee is burdened with checking and communicating the situation.
We solve various issues related to such assay requests and collaborate with Design Hub.
“Assay Request System (tentative name)” to improve the efficiency of the DMTA cycle introduce.

― 111 ―

FS01

フォーカストセッション
日時：2023 年 10 月 24 日（火）13：30 － 15：00
会場：研修室

計算 ADMET 研究会：毒性・副作用関連研究の新展開
New Frontiers in Toxicity and Adverse-Effect Related Research【 モデレーター 】
植沢 芳広（明治薬科大学）
永堀 博久（住友化学株式会社）
Yoshihiro Uesawa（Meiji Pharmaceutical University）
Hirohisa Nagahori（Sumitomo Chemical Co.,Ltd.）【 開催趣旨 】医薬品や一般化学物質の毒性・副作用は開発中止や市場撤退と関連する重要な課題である。開発初期における動物実験はコスト、時間、倫理等の問題を有するため、スループット性の高い代替手法の開発が求められている。化学構造等の限られた情報から極めて高速に毒性予測が実施可能な in silico 手法はその重要な選択肢となっている。本フォーカストセッショ
ンが、毒性・副作用に関連する生理現象を in silico 技術を用いて解明・解析・予測する最先端の研究について、多様な分野間で議論を深める場となることを期待したい。

FS01-01

計算 ADMET 研究会（旧・計算毒性学研究会）主査就任の挨拶と今後の展望
Welcome Address by the Newly Appointed Head of the Special Interest Group on
Computational ADMET and Future Prospects
植沢 芳広

明治薬科大学

Yoshihiro Uesawa
Meiji Pharmaceutical University

計算 ADMET 研究会は前主査の湯田浩太郎先生によって 2014 年に計算毒性学研究会として設立されました。当時、日本においては計算毒性学に関する認知度が極めて低い状況でしたが、多くの皆様のご協力のもと、本研究会は活発な議論の場となって参りました。一方、体内動態と毒性に対する包括的な把握は、毒性発現メカニズムに対する理解とそれに基づく効率的な医薬品をはじめとした機能性分子の安全性評価のために重要です。そこで、2023 年 1 月には、毒性とともに化学物質の体内動態に関する知見を包括的に情報共有する場として、計算毒性学研究会は計算 ADMET 研究会へと改称され、現在に至ります。新規化学物質の開発における安全性や環境毒性の評価など、人々の生活に直結する問題と深く関わっていることから、本研究領域は今後ますます重要性を増していくと考えられます。
人工知能技術をはじめとする計算科学の進歩はまさに日進月歩であり、従来は困難であった複雑な体内動態・毒性に関する予測の可能性はますます拡がりを見せています。このような背景から、ADMET 研究会では多様な分野における多くの研究成果を共有していきたいと考えております。広範な研究領域の連携を通じて、本研究会が更に発展していくことを願っております。

FS01-02

化学構造から有害事象へ：解釈可能なマルチタスク深層学習による有害事象の発現予測
From Chemical Structure to Adverse Events: Multi-task Models for Adverse Event
Occurrence with Interpretable Deep Learning
小川 慶子
立命館大学

Keiko Ogawa
Ritsumeikan University

医薬品による有害事象は健康被害や治療の中断に繋がるため、その発現リスクをなるべく早く察知することが求められる。有害事象を起こすリスクが高いアラート構造を事前に予測することができれば、医薬開発の早い段階で不適切な候
補物質を除外することができる。
そこでアラート構造を視覚的に認知できるよう、グラフ畳み込みニューラルネットワーク（GCN）を利用した有害事象の発現予測が有効であると考えた。GCN では化学構造をグラフ構造に変換することで、近傍のみならず遠隔の部分構
造や原子の情報も加味した予測が可能となる。FDA が公開する自発報告データベース FDA Adverse Event Reporting
System（FAERS）から副作用情報を抽出し、医薬品のグラフ構造から複数の有害事象の発現を同時に予測するマルチ― 112 ―

タスクモデルを構築した。本発表では、化学構造からマルチタスクに有害事象の発現を予測する取り組みを紹介する。
くわえて、グラフ構造を利用した可視化により、有害事象の発現に寄与する化学構造の特徴について考察する。

FS01-03

機械学習モデルの薬物動態及び毒性分野での創薬利用
Machine learning model for drug discovery in DMPK and TOX
半田 耕一

帝人ファーマ（株）動態・安全性研究部

Koichi Handa
Toxicology & DMPK Research Department, TEIJIN PHARMA LIMITED

近年、機械学習モデルの創薬利用が進展し、様々な薬物動態（ADME）パラメータの予測に関する論文が発表されている。
しかしながら、創薬プロセスに沿って、各段階での予測モデルの必要性を考えた時、どのようなモデルが創薬研究に有用なのかについて判断することは容易ではない。そこで、本発表ではまず近年発表されている ADME に関わる機械学
習モデルの傾向とその重要性についてまとめて紹介する。これを踏まえた上で我々がこれまでに取り組んできたいくつかの個別の研究課題（動態 : MATE1 阻害予測、毒性 : システイントラッピング予測、生成モデル : REINVENT の実デー
タ検証）について紹介し、それぞれのモデルについて言及するとともに、今後の計算 - 実験生物学融合領域の研究者へ問いかける、”What do you wanna do next?”。

FS01-04

分子記述子および分子画像を組み合わせた薬物動態パラメータ予測モデルの構築
Construction of prediction models for pharmacokinetic parameters by combining
molecular images and molecular descriptors
侭田 秀章

日本たばこ産業株式会社

Hideaki Mamada
Japan Tobacco Inc.

近年、経済的・時間的コストの削減および、動物実験の削減のため、創薬過程において毒性および薬物動態パラメータの予測に QSAR 解析が利用されている。またこれらのパラメータの中には既存手法では予測精度が低く、予測精度向上が課題となるパラメータが存在する。そのためこれらのパラメータ予測では、高精度の予測モデル構築手法の開発が望まれている。DeepSnap-DeepLearning 法は化合物を様々な角度から撮影することで、化合物の画像を取得しこの画像を用いて深層学習により予測モデルを構築する手法である。本手法により核内受容体活性やクリアランスなど毒性および薬物動態に関連する複数のパラメータ予測モデルの報告がされている。本講演では、分類および回帰モデルの構築において DeepSnap-DeepLearing 法および記述子法を組み合わせることで予測精度が向上した新たな知見について紹介する。

― 113 ―

FS02

フォーカストセッション
日時：2023 年 10 月 24 日（火）13：30 － 15：00
会場：307

オミックスの原理研究会
Workshop on the Omics Principle【 モデレーター 】
美宅 成樹（名古屋大学名誉教授）
広川 貴次（筑波大学医学医療系 生命医科学域）
荻島 創一（東北大学高等研究機構未来型医療創成センター / 東北メディカル・メガバンク機構）
Shigeki Mitaku（Nagoya University, Emeritus Professor）
Takatsugu Hirokawa（University of Tsukuba）
Soichi Ogishima（Tohoku Medical Megabank Organization/INGEM, Tohoku University）【 開催趣旨 】ゲノム解析技術の発展により種毎あるいは個人毎の全ゲノムは確実に得られるようになりました。そのことのイ
ンパクトは大きく、個々の遺伝子や変異などについて重要な情報が得られるようになりました。しかし、生物全体がどのように設計されているか？という原理は、依然分からない状態です。さらに広く考えると、生物の分子レベルから個体レベル、さらに生態系まで原理的な問題はまだ十分明らかになっていないと思います。最も基本的なビッグデータである全ゲノムが手に入る現在、私たちは「原理的な問題」が最も喫緊の課題と考えています。今回の FS も、いくつかの課題について議論しますので、さまざまな分野の方々に参加していただきたいと思います。

FS02-01

生命の相図を用いた SARS-CoV-2 ゲノム配列の解析
Analysis of the SARS-CoV-2 genome sequence using the phase diagram of life
美宅 成樹

名古屋大学名誉教授

Shigeki Mitaku
Nagoya University, Emeritus Professor

これまでゲノム配列の解析により、『生命の相図』を求めることができることを、当学会 FS で詳しく議論してきました。
それによれば、相図を記述するパラメータは、コドンの文字位置毎のヌクレオチド組成です。そこで今回は、ヌクレオ
チド組成を決める因子が環境外部因子か細胞内因子かを調べるために、ウィルス（SARS-CoV-2）ゲノムの解析を行いました。また、ウィルスは宿主のゲノム処理系に全面的に依存しているので、宿主を乗り換えた時のウィルスゲノムの解析によって、ゲノム処理系の興味深い性質を明らかにすることができました。

FS02-02

生体システムにおける化学情報処理過程の理解
Understanding chemical information processing in biological systems
小林 徹也

東京大学生産技術研究所

Tetsuya Kobayashi
Institute of Industrial Science, the University of Tokyo

生体は様々な感覚系を利用して外界の情報を感知し、その情報を処理して適応的な振る舞いを実現する。また感覚系を介して他の個体と情報のコミュニケーションをする。人間の情報処理は視覚（光情報）・聴覚（音声・言語情報）が優位であるが、細胞から個体までほとんどの生体システムの基礎は、化学物質による情報感知、情報伝達、そして情報処理である。したがって生体が用いる化学言語を読み解き、どのようなプロセスで生体がその情報を処理しているかを解き明かすことが、生体システムの理解や制御・予測の基盤となる。
DNA 暗号の解読とセントラルドグマの解明はその金字塔であるが、生体化学情報の一部でしか無い。
本発表では、生体が活用する化学情報解読に chemical informatics や bioinformatics そして深層学習が寄与する可能性とあわせて、化学情報を処理する生体内反応機構の理解にシステム生物学や定量生物学が果たす役割を例示し、両分野の知見・技術の統合とこれからの可能性を議論したい。

― 114 ―

FS03

フォーカストセッション
日時：2023 年 10 月 24 日（火）17：10 － 18：40
会場：研修室生成 AI の研究における適用可能性と今後の研究スタイルの全面的な変化
Applicability in generative AI research and great changes
in future research styles【 モデレーター 】
湯田 浩太郎（株式会社インシリコデータ）
植沢 芳広（明治薬科大学医療分子解析学研究室）
Kohtaro Yuta（In Silico Data,Ltd.）
Yoshihiro Uesawa（Department of Medical Molecular Informatics, Meiji Pharmaceutical University）【 開催趣旨 】昨年の ChatGPT の公開・発表以降、生成 AI が急速に注目を浴びている。
生成 AI 、特に大規模生成 AI は従来の AI の常識を大きく覆し、研究者の専任業務となる「自律化」業務の大部分をこなし、支援する能力を持つ。さらに、研究者が苦手とするプログラミング等の能力も有しており、この点でも大規模生成 AI は極めて優秀である。既に、創造的分野では写真や絵画の分野ではコンテストで優勝や賞を受けている。また、小説や詩等の創作分野でも適用がされており、将来の成果が期待される。
本フォーカストセッションは、今後の研究業務を大きく変える生成 AI をテーマとする。

FS03-01

副作用データベースを用いた毒性発現経路の推定と大規模言語モデルの活用
Estimating Toxicity Pathways with Adverse Effects Databases and Large-Scale
Language Models
植沢 芳広

明治薬科大学医療分子解析学研究室

Yoshihiro Uesawa
Department of Medical Molecular Informatics, Meiji Pharmaceutical University

毒性研究において医薬品の副作用は重要なエンドポイントとなる。特に、リアルワールドである実臨床における副作用
データは、市販前に実施される臨床試験では検討することのできない多様な患者集団に基づいた解析を可能にする。演者の主宰する研究室では、大規模なデータベースを用いて医薬品と副作用の関係を紐解くとともに、副作用発現に寄与する生化学的経路を QSAR 解析手法の援用によって推定する方法論を確立してきた。また、最近急速に一般化した
ChatGPT 等の大規模言語モデルを積極的に投入することによって、副作用データベース解析の簡略化を実施してきた。
本講演では、副作用データベースを用いて定義した毒性誘発物質の化学構造から、当該毒性に関連する核内受容体などの生化学的ハブの推定に至る解析法を解説するとともに、副作用データベース解析における ChatGPT の利用法について紹介したい。

FS03-02

生成モデルを中心とした Elix における AI 創薬
AI drug discovery in Elix centered on generative models
結城 伸哉

株式会社 Elix

Shinya Yuki
Elix, Inc.

自然言語から画像まで近年生成系 AI が大きな注目を集めており、創薬においても生成モデルが注目を集め続けている。
株式会社 Elix は AI 創薬を専門とする企業であり、コア技術としては生成モデルを日常的に活用している。本講演では、Elix における事例も交えつつ、生成モデルを中心に具体的な活用法、現在地や今後の発展などについて議論する。

― 115 ―

FS03-03

LLM（大規模言語モデル）に限界はあるのか？ AGI（汎用人工知能）の出現はどのように？
未来を探る
Is there a limit to LLM (Large Language Model)? How does AGI (Artificial GeneralIntelligenc) emerge? Exploring the future
生島 高裕

株式会社 数理先端技術研究所

Takahiro Ikushima
Advanced Mathematical Technology Laboratory Co., Ltd.

LLM のパラメータ数の拡大による性能向上は多くの課題を提出している。単にパラメータ数を増やすだけで人間の知能を越えるのか、消費電力はいくらまで許容可能か、LLM の生成物に対する根拠とその導出プロセスを説明できるか、推論がどこまでできるかなど。
そして、LLM 以外のアプローチとそのハイブリッド型からの AGI のあるべき姿、フィールズ賞クラスの創造性、その時、人間の創造性はどのようにあるべきかなどがある。これらについて情報提供する。

FS03-04

自律型（オートノマス）研究への生成 AI の適用性
Applicability of generative AI to autonomous research
湯田 浩太郎株式会社インシリコデータ

Kohtaro Yuta
In Silico Data, Ltd.

昨年、湯田は研究のスタイルとして今後は「自律型（オートノマス）研究」が主体になると提案した。昨年は概念的な提案で、実際に「自律型」研究を実施するための道具や武器は存在しなかった。昨年時点での AI 技術では、知的／創造的内容が主体となる「自律型」研究の実施は困難、不可能であった。しかし、昨年 11 月に発表された ChatGPT の出
現により、知的／創造的業務の実施や支援が可能な AI 時代に突入した。今回は ChatGPT に代表される大規模生成 AI や、
一般的な生成 AI の自律型研究への適用可能性について発表する。

― 116 ―

FS04

フォーカストセッション
日時：2023 年 10 月 24 日（火）17：10 － 18：40
会場：307

AI と XR の融合時代における化合物設計とデータ表示の革新
Practical compound design and data display in the era of AI and XR【 モデレーター 】
池田 和由（慶應義塾大学）
江崎 剛史（滋賀大学）
清水 祐吾（理化学研究所）
Kazuyoshi Ikeda（Keio University）
Tsuyoshi Esaki（Shiga University）
Yugo Shimizu（RIKEN）【 開催趣旨 】近年、医薬品開発分野において、AI の進化が活性・副作用予測や Denovo 設計などの重要な課題で劇的な進展を遂げています。この進展により、分子設計の専門家は AI の力を借りて、次に合成するべき化合物の指針を明確に示し、それを合成するメディシナルケミストを納得させる役割を果たすことが求められています。さらに、近年ではブラックボックス化を解消するための新たなアプローチとして、説明可能な AI（Explainable AI : XAI）技術への関心が高まっています。一方、
XR 技術（仮想現実 / 拡張現実 / 複合現実の総称）もまた注目すべき存在です。これは医薬品候補の三次元構造を直感的に把握するために有益な手段であり、さまざまなバックグラウンドを持つチームメンバー間での有意義な議論を活発化させることが期待されます。
このセッションでは、「AI と XR の時代」を牽引する新進気鋭の研究者に焦点を当て、彼らが直面する困難な課題への革新的なアプローチや最新の研究成果について紹介します。医薬品設計分野におけるこれらのテクノロジーの統合がもたらす可能性について洞察を得る機会となることでしょう。このセッションでは、当該分野において AI 及び XR をリードする研究者に焦点を当て、彼らの挑戦的な課題に対する革新的なアプローチや最新の研究内容についてご講演頂きます。医薬品設計分野におけるこれらのテクノロジーの統合がもたらす可能性について議論します。

FS04-01

創薬現場で役立つ AI、XAI
AI and XAI for drug discovery research
渡邉 博文

株式会社ウィズメーティス

Hirofumi Watanabe
WithMetis Co., Ltd.

創薬現場において多くの AI 導入の試みが行われているが AI は、ブラックボックス化しやすく現場でメディシナルケ
ミストを説得するには、不十分なことも多くあるだろう。そこで、このような問題を解決するために CBI 若手の会の有志で、説明可能な AI（XAI）に関する共同研究を行っている。Random Forest のような従来からある機械学習手法や、より新しいグラフ畳み込みニューラルネットワークを用いて、SHAP 値、IG 値など XAI 手法で計算される量を分子構造上に示すなどし、現場での AI 予測の結果のよりより示した方について検討を行っている。メディシナルケミストからの「AI、XAI が示してる傾向は、既知・自明なこと」という反応に対する対応の指針を示す。

FS04-02

複合現実空間で体験する化学反応ネットワークの対話操作
Exploring Chemical Reaction Network Through Mixed-Reality Interaction
石田 祥一

横浜市立大学

Shoichi Ishida
Yokohama City University

複合現実（MR）技術の登場により、現実空間とバーチャル空間が相互に作用する新たな世界が拓かれた。これはあらゆる場所・空間でもバーチャルなオブジェクトを現実の一部として扱えるようにし、従来の拡張現実（AR）や仮想現
実（VR）を超えて、より自然で直感的な没入体験を可能にした。本講演では MR 技術により可能になったことを、化学反応ネットワークの対話操作を題材に紹介していく。

― 117 ―

FS04-03

VR latest technologies in Theoretical and Computational Chemistry
Simon Bennie
Nanome, Inc.

The successful creation and optimization of nanoscale biomolecules, including proteins and small-molecule drugs,
is often contingent on an in-depth understanding of their three-dimensional structures. VR/MR platforms offer
scientists a uniquely powerful and cooperative tool to explore and understand structures and to obtain a distinct
perspective of the nanoscale world. This type of platform paves the way for faster and more effective concept
development, better communication of scientific principles, and a range of tools for improving early-stage Hit all theway to structure optimization.
Immersive software in science has now developed to the point that it can easily integrate with industry workflows
through flexible Python APIs, enabling easier interfacing with widely used modelling methods without scientists
having to learn new programming languages or how to develop within game engines. We will highlight the key
facets of current and upcoming immersive hardware and how state-of-the-art spatial computing approaches arebeing used to accelerate drug discovery, both through improved structural understanding and by creating a newparadigm for interacting with standard biochemical research algorithms. We will discuss the collaborative use ofalgorithms and the unique potential of accelerating their performance through the power of natural human 3D
reasoning and direct molecular control.

― 118 ―

FS05

フォーカストセッション
日時：2023 年 10 月 25 日（水）13：30 － 15：00
会場：研修室第 33 回 FMO 研究会：核酸創薬に向けた構造・機能解析と計算によるアプローチ
Structural and functional analysis and computational chemistry
approaches to nucleic acid drug discovery【 モデレーター 】
福澤 薫（大阪大学）
本間 光貴（理化学研究所）
Kaori Fukuzawa（Osaka University）
Teruki Honma（RIKEN）【 開催趣旨 】近年、創薬モダリティとして、核酸への注目が高まっている。核酸はタンパク質と比較して構造が揺らぎやすく、
構造ベース創薬はいまだ開発途中である。本セッションでは、DNA/RNA の構造や機能、及び相互作用解析を推進している先生を演者に迎えて、実験および計算科学の現状を共有し、今後の核酸創薬に向けた設計手法開発の方針を議論したい。

FS05-01

創薬標的としての RNA 立体構造モチーフ
RNA structural motifs as drug targets
近藤 次郎

上智大学理工学部物質生命理工学科

Jiro Kondo
Department of Materials and Life Sciences, Sophia University

タンパク質に代わる創薬標的として RNA が注目されている。一本鎖として転写された mRNA やノンコーディング
RNA は部分的に折れたたまれて、A-U、G-C 以外の非相補的な塩基対を複数含む内部ループ、ヘアピンループなどの構造をとる。ドラッグデザインの戦略を立てるためには、創薬標的となるこれらの構造モチーフの特徴を捉える必要がある。本講演では、RNA の立体構造構築の基礎と、Ｘ線結晶解析による動的な立体構造の観察技術について紹介し、立体構造予測に向けた展望も示したい。

FS05-02

非二重らせん核酸による疾患関連遺伝子の発現制御
Regulation of expression for disease-related genes by noncanonical nucleic acids
建石 寿枝

甲南大学 先端生命工学研究所

Hisae Tateishi-Karimata
Frontier Institute for Biomolecular Engineering Research (FIBER), Konan University

核酸の標準的な構造は、二重らせん構造であるが、同一の塩基配列でも核酸は、三重らせん、四重らせんのような非二重らせん構造も形成する。非二重らせん構造形成は、周辺の環境変化（共存質や塩濃度、ｐＨの変化など）によって誘起され、非二重らせん構造が形成されると遺伝子発現に関わる生体反応が抑制される。興味深いことに、非二重らせん構造を形成できる配列は、がんや神経変性疾患に関わる遺伝子上に多く含まれる。本講演では、疾患の発症や進行に関わる非二重らせん構造に関する研究成果を紹介し、創薬の標的としての非二重らせん構造の有効性について議論する。

― 119 ―

FS05-03

FMO 法を用いた核酸の相互作用解析
Molecular interaction analysis of nucleic acid using the FMO method
福澤 薫

大阪大学大学院薬学研究科

Kaori Fukuzawa
Graduate School of Pharmaceutical Sciences, Osaka University

フラグメント分子軌道（FMO）法は、分子の立体構造に基づいて電子状態を計算し、分子内・分子間の定量的な相互作
用エネルギーを取り出すことができるため、構造ベース創薬における相互作用の理解や分子設計のアイデアに繋がっている。量子化学計算であるために、エネルギーは力場に依存せず、タンパク質―低分子系ばかりでなく、核酸などの創薬モダリティにおいても、幅広く適用できる。ここでは、核酸とその複合体の FMO 計算による相互作用解析の現状と実験との連携について、最新の進捗を紹介する。

― 120 ―

FS06

フォーカストセッション
日時：2023 年 10 月 25 日（水）13：30 － 15：00
会場：407

先端的計測技術 (1)Advanced Measurement and Analysis (1)【 モデレーター 】
多田隈 尚史（上海科技大学）
石田 誠一（国立医薬品食衛生研究所 / 崇城大学生物生命学部）
藤田 聡史（産業技術総合研究所 生命工学領域 先端フォトニクス・バイオセンシング）
Hisashi Tadakuma（ShanghaiTech）
Seiichi Ishida（National Institute of Health Sciences / Department of Applied Life Science, Sojo University）
Satoshi Fujita（OIL Photo BIO-OIL, AIST）【 開催趣旨 】近年、抗体医薬品、核酸医薬品などのバイオ医薬品の開発が盛んである。生体高分子をベースにしたこれらの医薬品はより複雑な分子作用機序や分子動態を示すので、開発においては、その計測や評価モデル系構築が鍵となる。本フォー
カストセッションでは、気鋭の研究者に御発表いただく。前半は高感度・高精度な生体計測に関して、後半は細胞の操作に関して、広く御討論いただきたい。
前半 2 題は、高感度・高精度な検出について、ご発表いただく。まずは AI や光学観察を基盤とした、（バイオマーカー標識が不要な）、データ駆動型のサイトメトリー（細胞や粒子の分画）に関して、太田禎生先生（東京大学）からご発表いただく。続
いて、武井洋大先生（カリフォルニア工科大学）より、
ゲノム、トランスクリプトーム、核内構造体の三次元情報の 1 細胞解析（マルチオミクス解析）に関して世界のトップを走る最先端のご研究をご紹介いただく。
後半の 4 題は、新規の薬物動態・安全性の評価系として生体計測と連携が期待される臓器モデルを活用した生体計測について、ご発表いただく。はじめに小島伸彦先生（横浜市立大学）より、スフェロイド作製技術と光学観察に関してご発表いただく。
黒澤俊樹先生（帝京大学）より、ヒト iPS 細胞から作製したヒト BBB モデルの有用性、そして BBB-MPS の開発に関してご発表をいただく。続いて、横川隆司先生（京都大学）より、臓器細胞と血管網の界面を 2 次元的あるいは、3 次元的に形成する方法について腎近位尿細管や腫瘍微小環境、あるいは脳オルガノイドを例に御紹介いただく。最後に、山本佑樹先生（HiLung
株式会社）より、ヒト iPS 細胞技術を用いて作製された、生体とほぼ同等のヒト呼吸器上皮細胞に関する話題を御紹介いただく。
前半でご紹介いただく高感度・高精度な生体計測技術と、後半の臓器モデルを活用した生体計測との連携と、新規の薬物評価
系への展開に関して広く御討論いただきたい。
【このセッションは前半・後半に分かれており、17:10 から同室で行われる FS09 に続きます】

FS06-01

Learning Cytometry の開拓
Developing learning cytometry technologies
太田 禎生
東京大学

先端科学技術研究センター

Sadao Ota
Resarch Center for Advanced Science and Technology, The University of Tokyo

サイトメトリー技術を使う時、私たちはバイオマーカーを使って細胞を標識し、マーカー強度やその他代表値から、ソートする細胞を定義します。しかし、目的細胞を定義する良いマーカーがなかったら？どの細胞を標識すべきか、わからないとしたら？私は、人の仮説に基づいた考え方を逆転し、10 年ほど前にデータ駆動型の Learning Cytometry 技術開発を始めました。この講演では、実用化した AI が駆動する画像情報識別型ゴーストサイトメトリー（GC）、マルチモーダルサイトメトリー手法や、高感度・高速ナノ粒子サイトメトリーも時間の許す限りで紹介できたらと思っています。

― 121 ―

FS06-02

単一細胞核の高解像度空間マルチオミクス解析
High-resolution spatial multi-omics analysis of single nuclei
武井 洋大

カリフォルニア工科大 バイオエンジニアリング研究科

Yodai Takei
Division of Biology and Biological Engineering, California Institute of Technology

ゲノム、トランスクリプトーム、核内構造体の三次元情報を一細胞から網羅的に取得することは、細胞核内での遺伝子発現制御を解読する上で重要である。本発表では、同一細胞から 100,049 染色体座、17,856 遺伝子の新生トランスクリ
プトーム、及び様々な核内構造体の同時計測を可能にする高解像度空間マルチオミクスイメージング技術の開発と、複雑な組織における一細胞レベルの詳細解析について紹介する。

FS06-03

スフェロイドの光学観察による創薬の可能性
Possibility of drug discovery by optical observation of spheroids
小島 伸彦

横浜市立大学 生命ナノシステム科学研究科

Nobuhiko Kojima
Graduate School of Nanobioscience, Yokohama City University

スフェロイドは細胞を数千個程度凝集させて作製したミニ組織である。例えば肝細胞は平面培養よりもスフェロイド培
養でより高い代謝能力を示す。このような高い生理学性だけでなく、スフェロイドはその形状など光学的に検出可能な特徴によっても創薬プロセスを支援できる可能性がある。本発表では、メチルセルロース法によるスフェロイド作製技
術が光学観察にもたらす重要性を示す。また、近赤外線による画像解析を用いた細胞毒性の評価についても紹介する。

― 122 ―

FS07

フォーカストセッション
日時：2023 年 10 月 25 日（水）13：30 － 15：00
会場：307

分子ロボティクス研究会「人工生体分子で脂質膜間通信を目指す」
Molecular Robotics Research Group: Toward information transmission
across lipid bilayer using artificial biomolecules【 モデレーター 】
川又 生吹（東北大学）
佐藤 佑介（九州工業大学）
小宮 健（海洋研究開発機構）
多田隈 尚史（上海科技大学）
豊田 太郎（東京大学）
Ibuki Kawamata（Tohoku University）
Yusuke Sato（Kyushu Institute of Technology）
Ken Komiya（JAMSTEC）
Hisashi Tadakuma（ShanghaiTech University）
Taro Toyota（The University of Tokyo）【 開催趣旨 】分子ロボティクス研究会では、人工的な生体分子システムを合理的な設計に基づいて作製することを目標としている。そのようなシステムの一例として、脂質膜で覆われたカプセル型のスマートドラッグが想定される。カプセルの外部環
境から情報を受け取り、状況に応じて薬剤分子を内部から外部へ放出するシステムである。しかしながら脂質膜はカプセルの内外を隔てるバリアとして機能し、分子の輸送や情報のやり取りを遮断するため、そのようなシステムの実装は一般に困難である。そこで分子ロボティクス研究会では、DNA やペプチド、タンパク質などを材料に作製される人工の生体分子を用いて脂質膜間で通信を行う方法論に注目している。本セッションでは、脂質膜間で分子の輸送や情報のやり取りを行うシステムの分子シミュレーションや実験研究について情報共有を行い、分子ロボティクスの展開に関して広く議論を行いたい。

FS07-01

分子ロボティクス研究会とセッションの紹介
Introduction of Molecular Robotics Research Group and this session
川又 生吹
東北大学

Ibuki Kawamata
Tohoku University

2023 年に CBI 学会で発足した分子ロボティクス研究会の紹介を行うとともに、本セッションの説明を行う。

FS07-02

人工 DNA チャネル内部のイオン輸送に関する分子シミュレーション
Molecular Simulations of Ion Transport Through Artificial DNA Channels
馬渕 拓哉
東北大学

Takuya Mabuchi
Tohoku University

DNA ナノテクノロジーの発展に伴い様々な DNA を基盤とした人工ナノポアが開発されており、設計するポア径によって輸送されるターゲット分子が異なる。本研究ではイオン輸送 をターゲットとした比較的小さなポア径（<~2nm）の人工 DNA チャネルに着目し、その内部のナノスケールのイオン輸送現象を分子論的に明らかにする。

― 123 ―

FS07-03

環状ペプチドの膜透過メカニズムを明らかにするための分子動力学シミュレーションプロトコルの開発と応用Development and application of molecular dynamics simulation protocols to
elucidate the membrane permeation mechanism of cyclic peptides
杉田 昌岳

東京工業大学

Masatake Sugita
Tokyo Institute of Technology

近年、高活性な薬剤候補分子を容易に探索することが可能なモダリティである環状ペプチドを細胞内へ送り込むために、
その膜透過性を高める技術開発が活発に進められている。その一環として、環状ペプチドの膜透過メカニズムを明らかにするための分子シミュレーション技術の開発が求められている。我々はこれまでに 2 次元レプリカ交換法に基づく分
子動力学シミュレーションプロトコルを開発し、環状ペプチドの膜透過性の予測へ応用してきた。その結果、正確な予測を行うためには、メチル化されたペプチド結合やプロリンの N 末端を含むペプチド結合の cis/trans 異性化のサンプ
リングや膜を構成する脂質分子の調整が必要なことが明らかになった。

FS07-04

ナノポアを形成する人工膜輸送体の構築
Construction of artificial membrane transporters with nanopore structure
川野 竜司

東京農工大学

Ryuji Kawano
Tokyo University of Agriculture and Technology

生体ナノポアは脂質二分子膜に数ナノメートルの孔をあけるタンパク質である。最近ナノポアを利用した一分子 DNA
シーケンサが実用化され、生体内分子の天然での機能とは異なる人工的な応用が注目され始めている。本講演では、
ナノポアを人工的に設計、構築し、膜の内外の物質輸送の制御可能な人工膜輸送システムについて紹介したい。

― 124 ―

FS08

フォーカストセッション
日時：2023 年 10 月 25 日（水）17：10 － 18：40
会場：研修室創薬におけるプライバシー保護連合学習の最新動向
Recent Developments of Privacy-Preserving Federated Learning
in Drug Discovery【 モデレーター 】
藤 秀義（イクトス）
Hideyoshi Fuji（Iktos SA）【 開催趣旨 】機械学習・AI モデルの性能は、学習に使用されるデータの質と量によって決まります。そのため、製薬業界において学習データの統合によるコラボレーションを模索する動きが増えています。しかしながら、従来の機械学習手法では学習データを１つのサーバーにデータ集約する必要があったため、機密性の高い化合物構造や活性値情報を共有することが困難でした。その課題を解決すべく、プライバシー保護連合学習の創薬への応用研究が進められています。プライバシー保護連合学
習とは、学習データを１つのサーバーに集約することなく分散した状態で機械学習を行う手法で、プライバシーに関わるデー
タや機密性の高いデータなど外部と共有できないデータを扱う際に有益な手法です。本セッションでは、創薬におけるプライ
バシー保護連合学習の最新の研究成果や成功事例について、国内外のコンソーシアムで活躍する先生方をお招きし、プライバ
シー保護連合学習の利点や今後の展望についてご講演頂きます。

FS08-01

AMED DAIIA の概要と情報セキュリティ技術への期待
Overview of AMED DAIIA and Expectations for Information Security Technology
本間 光貴
理研

Teruki Honma
RIKEN

AMED DAIIA における公共データと企業データの両方に基づく創薬 AI プラットフォームの構築の進捗の概要を紹介
し、今後の運用に向けた情報セキュリティ技術への期待を話します。

FS08-02

kMol による効率的かつスケーラブルな活性予測のフレームワーク
Efficient and Scalable Framework for Activity Prediction with kMol
Jun Jin Choong
株式会社 Elix
Elix, Inc.

In recent years, the accelerated design and discovery of pharmaceutical compounds through computational methods
have driven the need for fast, scalable and secure frameworks to predict ligand-protein interactions. Such predictions
are pivotal in identifying potential drug candidates and optimizing their binding affinity without compromisingtrade secrets within the pharmaceutical industry. However, with the ever-expanding size of chemical and biological
databases, conventional computational approaches face challenges in handling the complexity of these tasks. To
address this, we introduce a fast and scalable framework with federated learning capabilities for predicting ligandprotein activities. To the best of our knowledge, this is the only framework capable of performing federated
learning for drug discovery end-to-end. This framework is developed in collaboration with researchers from Kyoto
University. Our approach capitalizes on state-of-the-art machine learning algorithms, advanced feature engineering,and efficient data processing techniques to allow for a fast and scalable prediction end-to-end. Utilizing the powerof parallel processing and distributed computing, our framework is capable of handling large datasets, resulting insignificantly reduced computation time while maintaining high prediction accuracy. Computation can be performedon CPU or GPU depending on the nature of the task. Furthermore, with federated learning, one can utilize theframework in conjunction with various other collaborators without the need of exposing private data to others.
Central to our framework is the customizability of the framework itself. The framework is designed with flexibility
in mind, allowing users to customize configuration of models and build a pipeline that is most suitable for the task
at hand. Moreover, the framework is capable of exploiting structural information (graph data), structural features,― 125 ―

molecular descriptors and protein features. This multi-modal data representation enables a comprehensive analysisof the intricate relationships between ligands and proteins, capturing both global and local interactions that influencebinding affinities. By harnessing deep learning architectures such as graph neural networks and convolutional
neural networks, our model effectively learns complex patterns and dependencies from the data, thereby enhancing
predictive performance. For further performance gain, one can also perform fine-tuning within the framework itself.
For analysis purposes, visuals can be generated to have better understanding of the molecular space.
[1] Elix. https://github.com/elix-tech/kmol, 2023.
[2] Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl. Neural Message Passing
for Quantum Chemistry. In International Conference on Machine Learning , pages 1263–1272, July 2017.
[3] Thomas N. Kipf and Max Welling. Semi-supervised Classification with Graph Convolutional Networks. In

Proceedings on the 5th International Conference on Learning Representations , ICLR ’17, 2017.
[4] Ryosuke Kojima, Shoichi Ishida, Masateru Ohta, Hiroaki Iwata, Teruki Honma, and Yasushi Okuno. kGCN: A
graph-based deep learning framework for chemical structures. Journal of Cheminformatics, 12(1):32, May 2020.
[5] Q iang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong. Federated Machine Learning: Concept andApplications. ACM Transactions on Intelligent Systems and Technology , 10(2):12:1–12:19, January 2019.
[6] Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. Learning Deep Features for
Discriminative Localization. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pages
2921–2929. IEEE Computer Society, June 2016.

FS08-03

FLuID, Federated Learning using Information Distillation
Thierry Gilles Hanser
Lhasa Limited

Federated Learning (FL) allows to share knowledge across multiple organisations whilst mitigating the risk ofleaking private information between partners. In recent years Federated Learning has been adopted in Molecular
Discovery [1] to develop a new generation of improved predictive models. In this presentation we will describe a newFederated Learning paradigm where knowledge is transferred using data annotation and Knowledge Distillation [2].
This new data-driven paradigm called FLuID (Federated Learning using Information Distillation) alleviates many ofthe limitations of the current popular model-driven approach whilst capturing knowledge in a robust and versatileformat. We will demonstrate that knowledge can be shared whilst preserving the privacy of the underlying datausing a simple, intuitive, and lightweight method. We will present the application of the method to improve theprediction of secondary pharmacology, discuss recent advances in this approach and describe a visual researchplatform to explore the potential of the method in the industrial context.
[1] Hanser T. Federated learning for molecular discovery. Current Opinion in Structural Biology. 2023 Apr 1;79:102545.
[2] H inton G, Vinyals O, Dean J. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531. 2015
Mar 9.

FS08-04

Industry-Scale Orchestrated Federated Learning for Drug Discovery
Martijn Oldenhof
KU Leuven

To apply federated learning to drug discovery we developed a novel platform in the context of European Innovative
Medicines Initiative (IMI) project MELLODDY (grant n°831472), which was comprised of 10 pharmaceuticalcompanies, academic research labs, large industrial companies and startups. The MELLODDY platform was the firstindustry-scale platform to enable the creation of a global federated model for drug discovery without sharing theconfidential data sets of the individual partners. The federated model was trained on the platform by aggregating thegradients of all contributing partners in a cryptographic, secure way following each training iteration. The platform
was deployed on an Amazon Web Services (AWS) multi-account architecture running Kubernetes clusters in private
subnets. Organisationally, the roles of the different partners were codified as different rights and permissions on theplatform and administrated in a decentralized way. The MELLODDY platform generated new scientific discoveries
which will be shortly highlighted.

― 126 ―

FS09

フォーカストセッション
日時：2023 年 10 月 25 日（水）17：10 － 18：40
会場：407

先端的計測技術 (2)Advanced Measurement and Analysis (2)【 モデレーター 】
多田隈 尚史（上海科技大学）
石田 誠一（国立医薬品食衛生研究所 / 崇城大学生物生命学部）
藤田 聡史（産業技術総合研究所 生命工学領域 先端フォトニクス・バイオセンシング）
Hisashi Tadakuma（ShanghaiTech）
Seiichi Ishida（National Institute of Health Sciences / Department of Applied Life Science, Sojo University）
Satoshi Fujita（OIL Photo BIO-OIL, AIST）【 開催趣旨 】【このセッションは前半・後半に分かれており、13:30 から同室で行われる FS06 の後、休憩（15:00-17:10）
をはさんだ後に引き続き行われます】
近年、抗体医薬品、核酸医薬品などのバイオ医薬品の開発が盛んである。生体高分子をベースにしたこれらの医薬品はより複雑な分子作用機序や分子動態を示すので、開発においては、その計測や評価モデル系構築が鍵となる。本フォーカストセッショ
ンでは、気鋭の研究者に御発表いただく。前半は高感度・高精度な生体計測に関して、後半は細胞の操作に関して、広く御討論いただきたい。
前半 2 題は、高感度・高精度な検出について、ご発表いただく。まずは AI や光学観察を基盤とした、（バイオマーカー標識が不要な）、データ駆動型のサイトメトリー（細胞や粒子の分画）に関して、太田禎生先生（東京大学）からご発表いただく。続
いて、武井洋大先生（カリフォルニア工科大学）より、
ゲノム、トランスクリプトーム、核内構造体の三次元情報の 1 細胞解析（マルチオミクス解析）に関して世界のトップを走る最先端のご研究をご紹介いただく。
後半の 4 題は、新規の薬物動態・安全性の評価系として生体計測と連携が期待される臓器モデルを活用した生体計測について、ご発表いただく。はじめに小島伸彦先生（横浜市立大学）より、スフェロイド作製技術と光学観察に関してご発表いただく。
黒澤俊樹先生（帝京大学）より、ヒト iPS 細胞から作製したヒト BBB モデルの有用性、そして BBB-MPS の開発に関してご発表をいただく。続いて、横川隆司先生（京都大学）より、臓器細胞と血管網の界面を 2 次元的あるいは、3 次元的に形成する方法について腎近位尿細管や腫瘍微小環境、あるいは脳オルガノイドを例に御紹介いただく。最後に、山本佑樹先生（HiLung
株式会社）より、ヒト iPS 細胞技術を用いて作製された、生体とほぼ同等のヒト呼吸器上皮細胞に関する話題を御紹介いただく。
前半でご紹介いただく高感度・高精度な生体計測技術と、後半の臓器モデルを活用した生体計測との連携と、新規の薬物評価
系への展開に関して広く御討論いただきたい。

FS09-01

ヒト iPS 細胞由来血液脳関門モデルを用いた輸送解析および MPS 構築への応用
Transport analysis and application to MPS using a human iPS cell-derived
blood-brain barrier model
黒澤 俊樹

帝京大学 薬学部 薬物動態学研究室

Toshiki Kurosawa
Faculty of Pharma-Science, Teikyo University

血液脳関門（blood-brain barrier; BBB）は、細胞間の密着結合と多様な輸送体により血液 - 脳間の物質輸送を厳密に制御している。中枢疾患治療薬の創剤にとってヒト脳への移行性予測が重要であるが、既存の動物及び細胞評価系では種差や密着結合性の問題が指摘されている。本発表では、ヒト iPS 細胞から作製したヒト BBB モデルの有用性、そしてBBB-MPS の開発を目指した我々の取り組みについて研究成果を中心に報告する。

― 127 ―

FS09-02

腫瘍微小環境および器官形成への応用に向けた血管網を有する Microphysiological systems(MPS)
Microphysiological Systems (MPS) With Vascular Network for Tumor
Microenvironment and Organogenesis Applications
横川 隆司

京都大学大学院工学研究科マイクロエンジニアリング専攻

Ryuji Yokokawa
Department of Micro Engineering, Kyoto University

我々は、臓器細胞と血管網の界面を形成する方法として、多孔質膜上で臓器細胞と血管内皮細胞を共培養する 2 次元的方法（2D-MPS）と、血管内皮細胞の自己組織可能を利用する 3 次元的方法（3D-MPS）を用いたアッセイ系を開発してきた。2D-MPS としてオルガノイド由来細胞を用いた腎近位尿細管モデル、3D-MPS として腫瘍スフェロイドと血管網を吻合した腫瘍微小環境モデルや脳オルガノイドモデルを紹介する。

FS09-03

肺オルガノイドの応用と未来
Application of lung organoids for future innovative therapeutics
山本 佑樹

HiLung 株式会社

Yuki Yamamoto
HiLung Inc

近年の世界的潮流もあり、創薬におけるヒト外挿性の高い細胞モデルへの関心が高まっている。呼吸器疾患領域においても、動物モデルと実際のヒト病態での乖離があり、特に複雑な肺三次元構造を模倣したオルガノイドの創薬応用への関心が高い。HiLung 株式会社では、iPS 細胞技術を活用した呼吸器オルガノイドを用いた創薬応用を推進してきた。
その活動を紹介するとともに、「肺オルガノイド創薬」の展望についても触れていきたい。

― 128 ―

FS10

フォーカストセッション
日時：2023 年 10 月 25 日（水）17：10 － 18：40
会場：406

生命の起源：翻訳の起源
Origin of Life: Origin of Translation【 モデレーター 】
田中 成典（神戸大学）
Shigenori Tanaka（Kobe University）【 開催趣旨 】毎年 CBI 大会のフォーカストセッションで開催させていただいている「生命の起源」セッションも今回で 6 回目となり、今回のテーマは「翻訳の起源」である。生命の起源を考える上でいつも問題となるのが、核酸が先かタンパク質が先かという点である。両者をつなぐ翻訳系の起源を考察することは、その問いに対する直截的な洞察を与える。問題点を整理す
るイントロに続いて、分子シミュレーションに基づく、翻訳機能と創薬応用に関する 2 件の話題提供をいただき、その後、自由な討議の場を設けたい。

FS10-01

はじめにIntroduction
田中 成典
神戸大学

Shigenori Tanaka
Kobe University

翻訳システムの起源に関して、現在の知見を主に構造生物学、バイオインフォマティクス、分子シミュレーションの立場から整理し、生命の起源と関係づけるにあたっての論点を明確にする。

FS10-02

翻訳開始過程における開始 tRNA 認識の分子機構：tRNA とリボソームタンパク質の進化を探る
Molecular mechanism of initiation tRNA recognition in translation initiation
process: Exploring the evolution of tRNAs and ribosomal proteins
森 義治
神戸大学

Yoshiharu Mori
Kobe University

翻訳が開始されるためには開始 tRNA がリボソームに適切に結合する必要がある。本発表においては、分子シミュレー
ションにより明らかにされた開始 tRNA のリボソームへの結合機構を報告する。リボソームタンパク質がその結合機構
において重要な役割を担っており、そこから示唆される tRNA とリボソームタンパク質の進化についても考察する。

FS10-03

翻訳開始因子 eIF4A と RNA の相互作用解析
Analysis of interaction between translation initiation factor eIF4A and RNA
半田 佑磨
星薬科大学

Yuma Handa
Hoshi University

翻訳 開 始 因 子 で あ る eIF4A は、43S リ ボ ソ ー ム 複 合 体 を mRNA 上 に リ ク ル ー ト し、 自 身 は ATP 依 存 的 に RNAから乖離することで、スキャニングを開始する。本発表では、RNA 配列特異的にこの反応を阻害する天然化合物
Rocaglamide A を含む三者複合体の相互作用を in silico によって明らかにした結果について報告する。

― 129 ―

FS11

フォーカストセッション
日時：2023 年 10 月 25 日（水）17：10 － 18：40
会場：307

医療データ AI 解析実践フォーラム
Medical Data AI Analysis Practical Forum【 モデレーター 】
水野 聖士（東北大学）
小島 諒介（京都大学）
荻島 創一（東北大学）
Satoshi Mizuno（Tohoku University）
Ryosuke Kojima（Kyoto University）
Soichi Ogishima（Tohoku University）【 開催趣旨 】AI 関連技術は日進月歩で進化しており、その医療データ解析への応用は、研究デザイン、データ処理、統計解析、
機械学習の適用などを含め、パラダイムシフトが必要である。本フォーラムでは、医療データの AI 解析の中でも精密医療の実現に向けた最新技術とその応用、疾患の層別化や早期予測に基づく行動変容などのために、最先端の AI 解析に実際に取り組んでいる研究者が研究発表を行い、最新の技術情報共有を行う。医療データ AI 解析に実際に取り組んでいる研究者、これから始め
ようという研究者にぜひご参加いただき、医療データの AI 解析を実践している研究者の交流の場としてオープンに議論したい。

FS11-01

大規模グラフニューラルネットワークに基づく多様な医療関連データ解析
Multi-modal medical-related data analysis based on large-scale graph neuralnetworks
小島 諒介
京都大学

Ryosuke Kojima
Kyoto University

近年の人工知能（AI）ブームによって、医療関連の様々なデータに対する AI・機械学習技術の適用が急速に進みつつある。
特に、医療関連のタスクでは、単一データだけでなく、複数のモダリティのデータを取り扱う必要があり、それらの複数モダリティを統合した取り扱いが重要である。本講演では、特に大規模なグラフデータを扱う場合を中心として、複数モダリティのデータの取り扱う方法に関して、グラフニューラルネットワークを用いた実例を挙げつつ紹介する。

FS11-02

中外製薬が進める創薬研究における AI 活用実践
Practical AI Applications in Drug Discovery Research by Chugai Pharmaceutical
徳山 健斗

中外製薬株式会社

Kento Tokuyama
CHUGAI PHARMACEUTICAL

中外製薬は，医療用医薬品に特化し，がん領域・バイオ医薬品に強みを持つ研究開発型の製薬企業です．「個別化医療」の国内パイオニアとして，ゲノムデータ，リアルワールドデータ（RWD），デジタルバイオマーカー（dBM）等のデー
タを解析することで，一人ひとりに最適な治療の提供を目指しています．本セッションでは，当社のデータサイエンティ
ストより、創薬研究における機械学習やインフォマティクス技術の活用事例について紹介します .

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FS11-03

低出生体重の層別化に基づく環境・遺伝要因による予測モデルの開発
The early prediction models of low-birth-weight reveals different influential
environmental and genetic factors for prediction between term and preterm birth
group
水野 聖士
東北大学

Satoshi Mizuno
Tohoku University

行動変容のための疾患の予測モデルはこれまでに多く研究されてきたが、高血圧など多くの多因子疾患で重要と考えられる層別化に基づく、遺伝要因と環境要因の両方を考慮した疾患予測モデルの構築の報告はまだ少ない。本研究では、低出生体重を例に大規模ゲノムコホート調査である三世代コホート調査で収集された網羅的な生活習慣情報および遺伝情報を使用し、早産群と正期産群での層別化に基づく疾患予測モデルの構築およびその解釈により得られた知見について報告するとともに、行動変容へのアプローチについての展望にも言及する。

FS11-04

個人レベルでの効果的な治療・疾患予防のための AI 技術の開発
AI technologies for effective treatment and disease prevention at the individual
level
中村 和貴

協和発酵バイオ株式会社

Kazuki Nakamura
KYOWA HAKKO BIO CO., Ltd.

個別化医療では、個人ごとの特性を考慮した診断と治療により、高い治療効果や先制的な疾患予防の実現が期待される。
機械学習技術の進歩により、複雑な個人の健康情報を処理することで、高性能な診断支援や疾患予測が実現されてきた．
しかしながら、具体的に「何を」「どのように」改善することが効果的であるかに関する、データ駆動型の手法は確立されていない．本演題では、疾患・健康指標の予測に基づき個人レベルでの具体的な治療・予防法を提案するための、介入目標の探索・可視化技術、および目標点までの改善方法の提案技術について紹介したい．

― 131 ―

FS12

フォーカストセッション
日時：2023 年 10 月 26 日（木）13：30 － 15：00
会場：407幹細胞と AI を用いた毒性予測の新アプローチ法の紹介
Introduction to a new approach method to toxicity prediction
using stem cells and AI【 モデレーター 】
曽根 秀子（横浜薬科大学）
Hideko Sone（Yokohama University of Pharmacy）【 開催趣旨 】近年、幹細胞研究と人工知能（AI）の分野は、さまざまな科学的および医療的応用において大きな期待を示している。
これら 2 つの技術が融合する分野の 1 つは、さまざまな物質の毒性の予測である。この新しいアプローチ法（NAMs）は、幹細胞のユニークな特性と AI の計算能力を組み合わせて、化学物質の潜在的な毒性影響を評価するためのより正確かつ効率的な方法が特徴である。本セッションでは、私たちが開発した NAMs を詳しく紹介します。

FS12-01

ヒト幹細胞多臓器毒性予測システム「StemPanTox」の特徴とその価値
Features and Its Value of StemPanTox, Human Stem Cell Multi-Organ Toxicity
Prediction System
藤渕 航

日本新薬株式会社 / 東京大学大学院医学系研究科

Wataru Fujibuchi
Japan Shinyaku Co., Ltd. / Graduate School of Medicine, The University of Tokyo

医薬品等の毒性検査では Ames 試験、hERG 試験、細胞株・オルガノイド試験、動物試験、ヒト臨床試験など段階的に様々な手法が用いられるが、最後のヒト試験で毒性が出ると開発がストップしてしまう。予め毒性が出そうな臓器が予測できれば、何らかの手段を講じることもできるかも知れない。StemPanTox はヒト幹細胞を用いて多臓器毒性のハザードを高性能に予測できるシステムであり、その特徴や利用価値について概説する。

FS12-02

再利用可能な実行パイプライン構築版：ステムパントックスαの紹介
Reusable Execution Pipeline Build: Introducing the Stem Pantox α
高瀬 俊郎

日本アイ・ビー・エム株式会社

Toshiro Takase
Japan IBM Corporation

幹細胞による毒性予測システム StemPanTox の実行には機械学習モデルの学習・検証、学習済みモデルを用いた予測実行などの多様なプロセスが存在する。各プロセスにおいて将来の新たなデータに対しても再利用可能な実行パイプラ
イン：ステムパントックス α を構築した。また毒性予測分類器については Python 言語の LightGBM ライブラリによる再実装を行った。本セッションではその内容について紹介する。

FS12-03

ステムパントックスに用いる化学物質毒性予測アルゴリズムの検討
Study of chemical toxicity prediction algorithm for StemPantox
加藤 毅
群馬大学

Tsuyoshi Kato
Gunma University

StemPanTox は，ヒト幹細胞における発現量の変化を用いて多臓器毒性予測を行うアルゴリズムとして開発された。このアプローチは，ヒト幹細胞の発現量を測定するには多くの費用と時間がかかるため，学習データを大規模化しにくい．
そこで本研究では，学習データを仮想的に増加させる方法を試みた。本発表では，データの水増しにおいて生じるアルゴリズム上の問題を明らかにし，その問題を解決する理論を紹介する。

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O01 臨床インフォマティクス Clinical Informatics

O01-01
Robustness comparison of data preprocessing methods for
exosome-derived miRNA microarray data
Yuto TAKEMOTO*, 1, Daisuke ITO2, Masahisa KATSUNO2, Masahiro NAKATOCHI1,
Laura I. FURLONG3
1
Department of Integrated Health Sciences, Graduate School of Medicine, Nagoya University,
Department of Neurology, Graduate School of Medicine, Nagoya University, 3MedBioInformatics
Solutions S.L., Barcelona, Spain

2( * E-mail: y-takemoto@met.nagoya-u.ac.jp )In recent years, the field of life sciences has witnessed significant advancements in various omics analyses, including
next-generation sequencing and microarray data, which have revolutionized biological research by facilitating theacquisition of extensive datasets. These omics datasets have demonstrated their potential in revealing valuable biologicalinsights. However, when dealing with multiple independently measured omics datasets, careful consideration ofchallenges such as missing values and batch effects becomes imperative, as these issues can lead to erroneous
conclusions and hinder a comprehensive understanding of the underlying biological information. Data preprocessing
techniques play a pivotal role in mitigating these challenges, ensuring more accurate and reliable interpretations ofcomplex biological phenomena.
The objective of this study was to compare various data preprocessing methods for microarray data of microRNAs(miRNAs) and explore the most optimal approach. This study focused on the use of microarray data of exosome-derived
miRNAs from whole blood samples of patients with amyotrophic lateral sclerosis (ALS). Two distinct batch data
(batch 1 :28 samples, batch 2 :47 samples, common samples in each batch:10 samples), each containing independently
measured samples, were utilized to validate and compare ten different data preprocessing pipelines proposed in theliterature. These pipelines encompassed a wide range of methods, including techniques for handling missing values,
normalization strategies, and batch effect correction algorithms.
The comparison of 10 different pipelines showed some valuable insights into the characteristics of each pipeline.
First, batch effects were assessed. Before correction, all pipelines showed a strong batch effect, while after correction,
all pipelines showed a suppressed batch effect. Next, they were evaluated by the consistency score (ICC (intraclasscorrelation coefficients), NRMSE (normalized root-mean-square error)) of the corresponding values between
batches. Consistency scores differed by 1.5-2-fold between the pipelines. The most consistent pipeline had ICC=0.97,
NMRSE=0.04, while the least consistent pipeline had ICC=0.65, NMRSE=0.1. The results of this study emphasize theimportance of selecting the optimal pre-processing pipeline
to ensure accurate and consistent interpretation of miRNA
microarray data in ALS.
In conclusion, this study highlights the essential role ofdata preprocessing in accurately interpreting multiple miRNA
microarray data from whole blood samples in the context of
ALS research. The insights gained from this research contributeto tackling the challenge of understanding and diagnosing
enigmatic diseases such as ALS from a bioinformatics
perspective. Leveraging the validated data preprocessing
pipeline will enable large-scale analyses covering multiple
independently measured cohorts of data.

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O01 臨床インフォマティクス Clinical Informatics

O01-02
GLDADec: Guided LDA deconvolution enabled us
to identify cell type proportions
Iori AZUMA*, Tadahaya MIZUNO, Hiroyuki KUSUHARA
Laboratory of Molecular Pharmacokinetics, Graduate School of Pharmaceutical Sciences,
The University of Tokyo( * E-mail: phazuma19980625@gmail.com )Deconvolution is a computational method for estimating the proportions of cells from transcriptome data. Existingsupervised learning methods use cell-specific gene expression data as prior information, so that differences in expression
levels due to tissue specificity and species differences significantly affect the estimation performance. Here, we introduce
a novel framework, GLDADec, to leverage semi-supervised topic modeling for bulk gene expression deconvolution.
The proposed method was inspired by latent Dirichlet allocation (LDA), an unsupervised learning model, tostatistically estimate potential topics in a sample. We extended to semi-supervised learning using cell-specific marker
gene names as partial prior information and enabled the identification of topics and corresponding cells. We firstbenchmarked our algorithm against existing methods using three blood-derived datasets, GSE65133, GSE107572,
and GSE60424. For each cell type, GLDADec consistently showed high Pearson correlations (from 0.39 to 0.99), andoutperformed existing methods. Next, we applied the method to bulk RNA-Seq of mouse and rat liver treated withvarious liver toxicants to evaluate its performance in estimating immune responses during drug-induced liver injury(GSE237801 and GSE239996). Our proposed method utilizes prior information on cell type marker gene names, which
are easy to access, and enable comprehensive cell type analysis. The obtained Pearson correlations were improved bya large margin (0.584 for mouse and 0.544 for rat), considering all cell types together. We further applied our method
to human tumor samples and revealed new insights into cancer subtyping and clinical prognosis prediction. All theseresults suggest that our GLDADec is a robust model for multiple species.
The proposed method is a deconvolution method with high cell type coverage and identification. It is expectedthat the application of the proposed method to a large-scale database will enable us to obtain integrated knowledge of
immune responses.

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O01 臨床インフォマティクス Clinical Informatics

O01-03Development of the classification model for groups withrelatively lower hippocampal volume using blood amino acid levels
Kazuki FUJIWARA*, 1, Wataru SATO1, Kenji NAGAO1, Akira IMAIZUMI1,
Ikuko N. MOTOIKE2, Mitsuharu SATO2, Kengo KINOSHITA2, Seizo KOSHIBA2,
Masayuki YAMAMOTO2
Research Institute for Bioscience Products & Fine Chemicals, Ajinomoto Co., Inc., 2Tohoku
Medical Megabank Organization, Tohoku University

1( * E-mail: kazuki.fujiwara.sn6@asv.ajinomoto.com )Alzheimer’s disease (AD) is recognized as a condition that needs to be detected at the preclinical stages andprevented before it develops further. In the preclinical stage of AD, previous studies have shown that gray matter or
hippocampal atrophy appears after Ab-amyloid and phospho-tau pathology [1, 2]. Magnetic resonance imaging (MRI)
has proven helpful in measuring and detecting early brain atrophy in various regions. However, due to the initialhardware costs and ongoing running expenses, MRI remains an expensive examination, limited to hospital or centerbased modalities. From this standpoint, there is a growing need to create an early screening test for dementia that ismore cost-effective and easier to perform than MRI-based examinations. This study aims to develop a model for early
dementia detection using blood amino acid levels, a relatively inexpensive examination. We validated the secondary
use of genomic cohort data collected by the Tohoku Medical Megabank Organization (ToMMo) [3] as a cross-sectional
observational study using blood amino acid levels to predict the hippocampus volume fraction in 519 Japanese subjects
aged 60 years and older. In the experiment, models were developed and evaluated in an exploratory manner for
combinations of arbitrarily selected features from blood amino acid levels and 8 different machine learning algorithms,
including neural network and gradient boosting decision trees. Some models developed showed high performancefor training and validation data, respectively, confirming that the amino acids utilized in the features were found to be
associated with cognitive function.
1. Hardy, J. and D. Allsop, Amyloid deposition as the central event in the aetiology of Alzheimer's disease. Trends inpharmacological sciences, 1991. 12: p. 383-388.
2. De Calignon, A., et al., Propagation of tau pathology in a model of early Alzheimer's disease. Neuron, 2012. 73(4): p.
685-697.
3. Hozawa, A., et al., Study profile of the Tohoku medical megabank community-based cohort study. Journal of
epidemiology, 2021. 31(1): p. 65-76.

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O01 臨床インフォマティクス Clinical Informatics

O01-05
Body mass index stratification improves polygenic prediction oftype 2 diabetes in trans-biobank analysis
Takafumi OJIMA*, 1, 2, Shinich NAMBA1, Ken SUZUKI1, 3, Kenich YAMAMOTO1, 4, 5,
Kyuto SONEHARA1, 6, Akira NARITA7, the Tohoku Medical Megabank Project Study Group.7,
the Biobank Japan Project .8, Yoichiro KAMATANI9, Gen TAMIYA2, 7, 10, Masayuki YAMAMOTO2, 7,
Toshimasa YAMAUCHI3, Takashi KADOWAKI3, 11, Yukinori OKADA1, 5, 6, 12
Department of Statistical Genetics, Osaka University Graduate School of Medicine, 2Graduate
School of Medicine, Tohoku University, 3Department of Diabetes and Metabolic Diseases,
Graduate School of Medicine, the University of Tokyo, 4Department of Pediatrics, Osaka
University Graduate School of Medicine, 5Laboratory of Statistical Immunology, Immunology
Frontier Research Center (WPI-IFReC), Osaka University, 6Department of Genome Informatics,
Graduate School of Medicine, the University of Tokyo, 7Tohoku Medical Megabank Organization,
Tohoku University, 8Institute of Medical Science, the University of Tokyo, 9Laboratory of Complex
Trait Genomics, Graduate School of Frontier Sciences, The University of Tokyo, 10Center for
Advanced Intelligence Project, RIKEN, 11Toranomon Hospital, 12Laboratory for Systems Genetics,
RIKEN Center for Integrative Medical Sciences1( * E-mail: ojima.taka@gmail.com )Clinically indistinguishable type 2 diabetes (T2D) is increasingly recognized as the heterogeneous subtype composition withdifferent body mass index (BMI) sensitivity. The difference in subtype compositions is an essential factor of exacerbating polygenic
prediction across populations. Here, we propose a polygenic prediction model of sample stratification based on BMI values.
We performed stratified random sampling based on BMI to isolate highly heritable subtypes and to maintain case-control ratios
for BMI-related diseases, namely T2D and cardiac artery disease (CAD). Thus, using >97,000 individuals (N_T2D = 27,642, N_CAD= 22,650) from each of BioBank Japan (BBJ) and UK Biobank (UKBB), we obtained datasets stratified into two or three groups and
BMI-unstratified datasets.
Comparing two BMI-stratified groups, T2D heritability in the low BMI group was 84.5% and 64.7% greater than for high BMI
in BBJ and UKBB, respectively. Polygenic predictions of T2D toward low BMI targets showed 22.2% increased liability R squarefor BBJ and 23.5% for UKBB over predictions for unstratified targets. Using a pathway-specific clustering model, we showedthat the improved T2D predictions in low BMI groups were due to biological contributions from two pathways involving islet cell
dysfunction. By combining BMI stratification and a method integrating cross-populational effects, T2D predictions showed a 37.4%
and 47.0% improvement for BBJ and UKBB over unstratified matched-populational predictions, respectively. The improved T2D
predictions at low BMI were confirmed when comparing three BMI groups and evaluating odds ratios. We also replicated the resultsof improved T2D prediction in the low BMI group with two independent cohorts, Tohoku Medical Megabank Project (N_T2D = 6,000,
N_control = 20,000) and the second cohort of BBJ (N_T2D = 11,236, N_control = 21,860). In contrast, CAD predictions showed no
improvements, suggesting that T2D subtyping improved the predictions.
Our results suggest the utilityof target stratification basedon existing traits for polygenic
prediction of heterogeneous
diseases. The low BMI T2D cases
showed higher rates of neuropathy
and retinopathy, suggesting thatthe appropriate medication for
low BMI cases could prevent
complications. We replicated theresults for independent biobanksof Japanese, suggesting theimportance of developing various
cohorts to advance polygenic
predictions.

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O01 臨床インフォマティクス Clinical Informatics

O01-06
An innovative attempt to assist REC procedure for the medical research
involving samples and information held by national network
Hiroko TERUI-KOHBATA*, 1, Fuji NAGAMI2, Kinuko OHNEDA2, Takayuki MORISAKI3, Yuichi GOTO4,
Eisei NOIRI5, Hiroyuki NISHIYAMA6, Manabu MUTO7, Mizuki MIRITA8, Soichi OGISHIMA2,
Masayuki YOSHIDA1
Bioethics Research Center, Tokyo Medical and Dental University, 2Tohoku Medical Megabank
Organization, Tohoku University, 3The Institute of Medical Science, The University of Tokyo,
4
Medical Genome Center, National Center of Neurology and Psychiatry, 5NCBN Central Biobank,
National Center for Global Health and Medicine, 6Tsukuba Human Tissue Biobank Center,
University of Tsukuba Hospital, 7Department of Therapeutic Oncology, Graduate School of
Medicine, Kyoto University, 8Graduate School of Interdisciplinary Science and Engineering in
Health Systems, Okayama University
1( * E-mail: kohbbec@tmd.ac.jp )BackgroundThe utilization of biobank (BB) materials and information is essential for the continuous development of scientificresearch. To further expand the use of BB materials and information, we called for research proposals which utilizes
the materials and information from across BBs participating in the AMED biobank network project, and selected 13
research proposals as the support of the Grant-in-Aid for Genomic medicine R&D platform utilization system by
AMED.
Selection process to assist REC procedure: Among selected research proposals, the following research wasselected which conducted by the non-medical researcher entitled, “Information analysis using clinical and genomic
information of breast cancer patients”. This proposal was aimed to use genetic information from the Tohoku Medical
Megabank (TMM) and the BioBank Japan (BBJ). We conducted an innovative attempt to assist the REC (research
ethical committee) review procedure, one of the rate-limiting steps of rapid progress for medical research. We asked
REC belong to Tokyo Medical and Dental University, the Tohoku University Tohoku Medical Megabank Organization(ToMMo), Okayama University, and The Institute of Medical Science, the University of Tokyo to join this project. The
current protocol was submitted to ToMMo’s REC.
The content of REC review support
1) Support for external REC review procedure: we explained the administrative department of proposer’s institute incharge about the detailed procedure of how to outsource the research proposal to ToMMo’s REC followed by thesettlement by the head of the institution for granting the conduct of this research.
2) Support for preparing application documents: the study proposal claimed to use genomic information from
germline that required description of the policy to handle secondary findings in the proposal. To that end, we
needed to confirm the secondary genomic finding policies of each BB. In addition, we needed to check the itemsin the proposal that required by the ethical guideline for medical research, such as opt-out, information transfer
method, and data storage policies, which differ from one BB to the other.
Discussion
Through this innovative project, researchers, BBs in charge, and REC support team were able to meet for
preliminary discussions and information sharing to promptly provide the necessary information to non-medical
researchers unfamiliar with medical research. In addition, this project enabled to organize the information handled
differently by each BB.

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O02-01Development of a Prototype of voice-operated
VR Molecular Design Environment
Hirotaka KONDO*, 1, Spence Gregory GUTMANN2, Kuzuya AKINORI1, 2, 3,
Konagaya AKIHIKO2
Organization for Research and Development of Innovative Science and Technology, Kansai
University, 2Molecular Robotics Research Institute, Co., Ltd., 3Department of Chemistry andMaterials Engineering, Kansai University

1( * E-mail: kondo@kansai-u.ac.jp )In the co-creation environment of molecular design, the VR environment has emerged as an attractive approach
because of its intuitive understanding [1]. However, the current VR controller-based interface still has difficulties
caused by complicated hand manipulation. While hand gesture manipulation is one solution, voice manipulation is moreintuitive and straightforward. Natural language processing has recently made remarkable progress, especially in speech
recognition and is reaching a practical level due to the large language models. The synergy between VR and voice-based
interaction has a great potential to simplify user operation, enhance concentration on observations, and provide a deep
immersive experience.
This study proposes a molecular design system operated mainly by voice commands within a VR simulation. Thissystem can predict the three-dimensional structure of a protein from an amino acid sequence, perform MD simulations,and visualize the results. Our developed voice control interface combines voice recognition, a large language model, andvoice synthesis, allowing the user to operate the entire system by voice commands aiming for DNA origami molecular
design [2].
[1] Konagaya A, Gutmann G, Zhang Y. Co-creation environment with cloud virtual reality and real-time artificial
intelligence toward the design of molecular robots, J Integr Bioinform. 2022, 20, 1, 20220017.
[2] Akinori Kuzuya, Yusuke Sakai, Takahiro Yamazaki, Yan Xu, Makoto Komiyama: Nanomechanical DNA origami
ʻsingle-molecule beaconsʼ directly imaged by atomic force microscopy, Nature Commun., 2011, 2, 449.

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O02-02
Deep Learning-Based Deconvolution of Confocal Laser-scanning
Fluorescence Microscopy Images for Enhanced Visualization of
Giant Vesicles
Soichiro HIORI*, 1, 2, Taro TOYOTA1, 2, 3, Akihiko KONAGAYA2
Department of Basic Science, Graduate School of Arts and Science, The University of Tokyo,
Molecular Robotics Research Institute, Co., Ltd., 3Universal Biology Institute, The University of
Tokyo

1
2

( * E-mail: hiroi-soichiro1729@g.ecc.u-tokyo.ac.jp )In the evolving field of digital visualization, virtual reality (VR) visualization has emerged as a transformative tool
for molecular assemblies whose 3D morphology is important [1]. Giant vesicle (GV) is one of the molecular assemblies
whose morphology is susceptible to surrounding disturbances [2]. Clear images with high-resolution are essential
to accurately represent the subtle details of the surface and morphology of GVs in a VR environment. Although theconfocal laser-scanning fluorescence microscopy has been intensively developed, the problem of blurring in the images
still remains. Such blurring greatly hinders accurate representation of the 3D morphology of GVs in VR. To address
this, techniques such as deconvolution and high-resolution imaging of microscopy images are needed.
Based on our previous study [3], we have employed deep learning for such purpose. Using a model based on
CycleGAN, known for its ability to transform image style and content. In this study, we reconstructed the synthetic
dataset from our previous work and produced a generative model to enhance the clarity of GV microscopy images.
[1] Konagaya, A.; Gutmann, G.; Zhang, Y. Co-creation environment with cloud virtual reality and real-time artificial
intelligence toward the design of molecular robots, J. Integr. Bioinform. , 2022, 20220017.
[2] Lipowsky, R. Remodeling of Membrane Compartments: Some Consequences of Membrane Fluidity, J. Biol. Chem. ,2014, 395 , 253–274.
[3] Hiroi, S.; Toyota, T.; Zhang, Y.; Konagaya, A. Deep Learning Detection of Giant Vesicles using a Synthetic Data Setof Confocal Fluorescence Microscopy Images, Proceedings of the CBI Annual Meeting 2022 , O7-4.

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O02 分子ロボティクス / バイオインフォマティクス Molecular Robotics / Bioinformatics

O02-03Large-scale VR Molecular Rendering for
Co-creation Environment
Yuhui ZHANG*, 1, 2, Gregory GUTMANN2, Akihiko KONAGAYA2, 3, 4
School of Computing, Tokyo Institute of Technology, 2Molecular Robotics Research Institute, Co.,
Ltd., 3Keisen University, 4Advanced Science ELSI Research Laboratory, CBI Research Institute1( * E-mail: betairya@gmail.com )Rapid advancements in computer graphics have paved the way for the realization of real-time visualization systems
in the field of molecular dynamics, particularly through virtual reality (VR) [1].
In this talk, we unveil our pioneering Unity-3D based environment tailored for efficient molecular dynamics
visualization. Our system achieves groundbreaking efficiency by rendering nearly one million particles in real-time.
With a seamless VR interface and a separated client-server architecture, we offer both immersive experiences andextensibility with regards to molecular modeling and viewing.
Various techniques are implemented for such efficiency. In our system, we adopted a hierarchical level-of-details
system with indirect instanced rendering. We also designed a specific data layout scheme in GPU for an optimized
memory-time balance.
One such application is the VR viewing of MD trajectory files. We also discuss our future works, including the
instant visualization of DNA origami structures which are commonly used molecular nanostructures to constructmolecular robot parts.
[1] Konagaya A, Gutmann G, Zhang Y. Co-creation environment with cloud virtual reality and real-time artificial
intelligence toward the design of molecular robots: J Integr Bioinform. 2022 Oct 4;20(1):20220017.

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O02 分子ロボティクス / バイオインフォマティクス Molecular Robotics / Bioinformatics

O02-04
Application of Machine Learning Models in Prediction of Disease
Therapeutic Target Molecules Using Gene Expression Profiles
Narumi HATANO*, 1, Mayumi KAMADA1, Ryosuke KOJIMA1, Hiroaki IWATA1,
Yasushi OKUNO1, 2
1
Graduate School of Medicine and Faculty of Medicine Kyoto University, 2RIKEN Center for
Computational Science (R-CCS)HPC/HPC- and AI-driven Drug Development Platform Division( * E-mail: hatano.narumi.57s@st.kyoto-u.ac.jp )In drug development, identifying therapeutic target molecules related to disease causes and progression is an important
challenge. While the search space for potential target molecules is vast, the number of therapeutic target molecules thatcan be verified experimentally is limited. This situation has prompted the development of target molecule discovery
methods utilizing medical big data. Among these approaches, “target repositioning,” which involves repurposing
existing target molecules for other diseases, has been proposed (Namba et al.; Bioinformatics, 2022). Based on thepremise that knockdown/overexpression profiles of genes encoding drug target molecules reflect the function of drugs
that inhibit/activate these targets, they proposed a machine-learning model to predict the association between the target
and the disease using gene expression data representing the responsiveness of potential drug target genes and diseasespecific gene expression. While this highlighted the potential of target repositioning in identifying new therapeutictarget molecules, there remains room for enhancement in prediction accuracy. Furthermore, when using gene expression
profiles for machine learning, these data resulted in high-dimensional and sparse because the number of genes used asfeature data is large, while the number of disease-related genes are small. That kind of data poses challenges for model
construction and improving its generalization abilities.
In this study, we constructed a machine-learning model to predict the association between therapeutic targets anddiseases. Moreover, we investigated multiple feature selection techniques for high-dimensional datasets and validated
their prediction accuracy. As a result of the verification, our proposed model achieved accuracy comparable to previous
methods. Additionally, the utility of feature selection was confirmed.
The proposed model promises potential applications for efficiently exploring therapeutic target molecules acrossvarious diseases, including those without identified targets. Since feature selection proved beneficial when working withhigh-dimensional data, such as gene expression profiles in machine learning, further exploration of feature selection
techniques could lead to enhancements in the prediction model.

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O02-05
Gene prioritization using a genetics-led approach fortarget discovery of ALS
Yuki KUNIYOSHI*, 1, Rebecca HARRISON2, Hiroyuki IHA1, Siddharth SETHI2,
Harpreet SAINI2, Daisuke D IKEDA1
1

Otsuka Pharmaceutical Co., Ltd., 2Astex Pharmaceuticals( * E-mail: Kuniyoshi.Yuki@otsuka.jp )Introduction
The priority index (Pi) pipeline, a framework to prioritize potential targets by integrating GWAS data with genomic
features, ontologies and network connectivity, was originally developed and tested on immune-related diseases. We
modified the Pi pipeline to identify potential target genes for amyotrophic lateral sclerosis (ALS).
Methods
A total of 575 SNPs in the ALS GWAS (p value < 1E-7) were used as input. Pi used 3 genomic predictors and 3
annotation predictors. Genes with genetic evidence were identified and scored based on (1) genomic proximity to a SNP(nGene), (2) modulation of gene expression evidenced by eQTL in brain cortex and spinal cord from GTEx v8 (eGene),and (3) physical interactions evidenced by PLAC-seq of microglia, neurons and oligodendrocytes (cGene). Among thegenes linked to SNPs, genes with ALS related annotation were identified and scored using the following ontologies:
ALS pathogenic function (fGene), ALS-related phenotypes (pGene) and rare genetic diseases related to ALS (dGene).
Random walk with restart using a STRING network of 5183 genes with strong experimental evidence was used to findgenes that lacked genetic evidence but were in close proximity to seed genes (nGene, eGene, cGene, fGene, pGeneor dGene) in the network. Six predictors were integrated into Pi rank by Fisher’s combined meta-analysis. Moreover,to remove the effect of network structure on Pi rank, the empirical probability of Pi rank was calculated by 1000
simulations with random inputs.
Results
We prioritized 5213 genes based on ALS GWAS. There were 122 genes that had genetic evidence or showed
ALS GWAS dependent Pi rank (empirical probability ≦ 0.05) in the top 150 Pi rank. The genes were enriched in ALS
relevant disease processes such as autophagy. Finally, we identified potential target genes by integrating compound
information, expression data and literature data.

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O02-06
Elucidation of trajectories from healthy state to mibyo anddisease onset through large-scale specific health checkup data
Satoshi NAGAIE*, Satoshi MIZUNO, Ryosuke ISHIWATA, Soichi OGISHIMA
Tohoku University Tohoku Medical Megabank Organization( * E-mail: nagaie@megabank.tohoku.ac.jp )How to realize personalized prevention based on individual data is an important issue. It is necessary to capture
trajectories from the so-called healthy state to mibyo and the onset of disease. Rather than capturing univariate changes
in laboratory test values, it is important to quantitatively capture multivariate changes in health status. This may allow
for a quantitative assessment of the health status of the subjects and the possibility of intervening for individualized
prevention in the state of mibyo condition. Therefore, the purpose of this study is to quantitatively assess multivariate
health status using specific health checkup data and to elucidate trajectories from the healthy state to mibyo and disease
onset.
In this study, we analyzed 4 years (2014-2017) of specific health checkup data consisting of 42 variables without
missing values (17 clinical and 25 lifestyle-related variables) for a population of 96,093 individuals (64,128 males and
31,965 females) in the JMDC database. Firstly, we performed dimensionality reduction using PCA for mixed data and
UMAP to obtain variables defining health status. Secondly, we conducted DBSCAN to detect clusters in the healthstatus space.
As a result, we identified 98 clusters of health status for males and 41 clusters for females. The center of the healthstate space (between -10 and 10 on all axes from UMAP1 to UMAP3) was located for 91.8% of males and 95.1% offemales. For example, focusing on clusters 30, 34, and 29, which had large UMAP3 values and were off-center, wefound that HbA1c and glucose values increased as they moved off-center. For the diabetic phenotype, the percentages ofnormal, 100%, 52%, and 29% decreased, while the percentages of diabetic, 0%, 17%, and 38% increased, respectively.
This suggests that the UMAP3 axis is the axis associated with diabetes. We analyzed and reported the trajectories oftransition patterns between these clusters for individual health status.

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O03 分子計算・機械学習 /AI による創薬研究の最新動向と展望

The latest trends and prospects in drug discovery research using molecular calculation, machine learning, and AI

O03-01
De novo inhibitor and activator design from gene expression profiles
via deep learning and Bayesian optimization
Yuki MATSUKIYO*, 1, Chikashige YAMANAKA1, Yoshihiro YAMANISHI1, 2
Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems
Engineering, Kyushu Institute of Technology, 2Graduate School of Informatics, Nagoya University
1( * E-mail: matsukiyo.yuki566@mail.kyutech.jp )Identifying a molecule with the desired bioactivity in the hit identification is extremely difficult, because the numberof theoretically possible compounds is ≥1060 [1]. It is infeasible to experimentally test all candidate compounds due tothe huge number of possible compounds. To tackle this issue, deep generative models such as variational autoencoder(VAE) and generative adversarial network (GAN) have been actively applied to de novo structure design strategies.
Furthermore, molecular generators creating hit-like molecules with comprehensive biological information such as
gene expression profiles have been developed [2, 3]. However, these previous studies have some problems about
reproducibility of existing active ligand structures, and there is still room for improvement about structure generationfrom gene expression profiles.
In this study, we present a novel computational method to generate inhibitor and activator candidate molecules fortherapeutic target proteins, integrating chemically-induced and genetically-perturbed transcriptome data. Examples
of genetically-perturbed transcriptome data include gene expression profiles reflecting cellular responses to geneknowckdown or gene overexpression of a therapeutic target protein. In the algorithm, firstly, we select multiple
molecules based on the correlation between chemically-induced and genetically-perturbed transcriptome profiles.
Subsequently, we fuse the substructures of these molecules by exploring the latent space of Transformer-based VAE
using Bayesian optimization, and generate new structures of inhibitor and activator candidate molecules. We applied
our model to ten target proteins, and confirmed our model was more likely to generate hit-like molecules that werestructually similar to existing active ligands than previous methods. This model is expected to be useful for more
efficient hit identification.
[1] Bohacek, R. S.; McMartin, C.; Guida, W. C. The Art and Practice of Structure-based Drug Design: a Molecular
Modeling Perspective. Med. Res. Rev. 1996, 16 , 3−50.
[2] Méndez-Lucio, O.; Baillif, B.; Clevert, D.-A.; Rouquié, D.; Wichard, J. De Novo Generation of Hit-like Moleculesfrom Gene Expression Signatures Using Artificial Intelligence. Nat. Commun. 2020, 11 , DOI: 10.1038/s41467-01913807-w.
[3] K aitoh, K.; Yamanishi, Y. TRIOMPHE: Transcriptome-Based Inference and Generation of Molecules with Desired
Phenotypes by Machine Learning. J. Chem. Inf. Model. 2021, 61 , 4303-4320.

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The latest trends and prospects in drug discovery research using molecular calculation, machine learning, and AI

O03-02Development of 53BP1 expression classification model by using
Segment-Anything Model(SAM) and CNN Model in pathological image
Yasuhiko NAKAO*, 1, Maiko TABUCHI1, 2, Kazuhiko NAKAO1, Yuko AKAZAWA2Department of Gastroenterology and Hepatology, Nagasaki University Hospital, 2Department ofHistology and Cell Biology, Nagasaki University Graduate School of Biomedical Sciences1( * E-mail: yasuhiko.dream.new.york@gmail.com )The present study details the development of a classification model for 53BP1 expression in pathology images,
utilizing the Segment-Anything Model (SAM). 53BP1 is an essential biomarker used in cancer research anddiagnosis, as it indicates DNA damage response. Accurate classification of 53BP1 expression levels can offer valuable
observations into the behaviour of tumours and help make better treatment decisions. We utilized SAM, a cutting-edge
image segmentation framework, to build the classification model, which precisely outlines the regions of interest incomplex pathological images. SAM applies advanced deep learning algorithms and learns to segment precise structuresand entities from vast annotated datasets. We segmented and decomposed 30 slides (100x) from normal, low grade
dysplasia, and high-grade dysplasia, as urothelial pathology into 7662 single cell unit images using SAM, an extensive
manual work for the CNN model building process. One-cell images were divided into nine categories based on thepreviously reported types of 1-7, Ki67, and non-classifiable.After the segmentation process, the model identifies thecells in the images and determines the weighing and 53BP1. We conducted experiments on another test dataset toevaluate the performance of our model. The classification model that we developed is expected to aid pathologists andresearchers in accurately and easily assessing 53BP1 expression in pathological images. In turn, this will enhance thecomprehension of tumour biology and direct personalised treatment strategies. Moreover, the integration of SAMs intoclassification pipelines could be utilised in developing several condition-specific diagnostic methods to improve theanalysis of complex pathology images.

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O03 分子計算・機械学習 /AI による創薬研究の最新動向と展望

The latest trends and prospects in drug discovery research using molecular calculation, machine learning, and AI

O03-03
Multi-objective Molecular Structure Generation Using Dynamic
Applicability Domains Adaptation
Tatsuya YOSHIZAWA*, 1, Shoichi ISHIDA1, Tomohiro SATO2, Masateru OHTA3,
Teruki HONMA2, Kei TERAYAMA1, 4, 5
Graduate School of Medical Life Science, Yokohama City University, 2RIKEN Center for
Biosystems Dynamics Research, 3HPC- and AI-driven Drug Development Platform Division, RIKEN
Center for Computational Science, 4RIKEN Center for Advanced Intelligence Project, 5MDX
Research Center for Element Strategy, Tokyo Institute of Technology
1( * E-mail: w225435a@yokohama-cu.ac.jp )A key challenge in drug discovery is to find molecules that simultaneously meet various criteria, such as potency,
selectivity, and pharmacokinetic endpoints. Addressing this challenge involves solving a multi-objective optimization
problem. Recently, molecular structure generation methods have been increasingly applied to drug discovery processes,
and some of these techniques have successfully achieved multi-objective optimization by employing multiple prediction
models to evaluate properties of generated structures [1,2]. When using structure generators employing prediction
models, it is essential to be cautious that exploring new chemical spaces carries a risk of falling outside applicability
domains (ADs) of prediction models. This can cause false positives, as reliable predictions are typically limited toqueries similar to data used for training models. To mitigate this risk and avoid generating false positives, some studies
attempt to generate structures within an AD of a property prediction model [3,4,5]. However, it remains challenging toperform multi-objective optimization incorporating ADs because ADs for multiple prediction models are not ensured tooverlap.
In this study, we propose a strategy for molecular structure generation that performs multi-objective optimization
while dynamically adjusting reliability degrees of multiple prediction models using Bayesian optimization. As adefinition of ADs to regulate reliability degrees, we applied a Tanimoto similarity-based approach, which determines
queries to be within an AD if their structural similarity to training data of a prediction model is above a given thresholdvalue. To generate structures that allow each prediction model to make reliable predictions, it is required to findoptimal thresholds that ensure an overlap of multiple ADs while maintaining as high-reliability degrees as possible. To
address this requirement, we employed Bayesian optimization to search for optimal thresholds for ADs. As a structure
generator, we employed ChemTSv2 [6], which consists of a structure generator based on recurrent neural networks
and an exploration system based on Monte Carlo tree search. As properties to be optimized, three objectives wereselected: inhibitory activity against epidermal growth factor receptor, membrane permeability, and metabolic stability. A
prediction algorithm and input features were set to light gradient boosting machine and Morgan fingerprint, respectively.
Here, we report results that our strategy attempts to adjust reliability degrees of the three prediction models and optimize
the properties.
[1] Luukkonen, S., et al . Curr. Opin. Struct. Biol. 2023, 79 , 102537.
[2] Fromer, J. C., et al . Patterns 2023, 4 , 100678.
[3] Kaneko, H., et al . Mol. Inf. 2017, 36 , 1600032.
[4] Perron, Q., et al . J. Comput. Chem. 2022, 43 , 692–703.
[5] Langevin, M., et al . ACS Omega 2023, 8 , 25, 23148–23167.
[6] Ishida, S., et al . WIREs Comput. Mol. Sci. 2023, e1680.

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The latest trends and prospects in drug discovery research using molecular calculation, machine learning, and AI

O03-04Generating Synthesizable Compound Structures with
Desired Properties via Deep Learning Models
Shinnosuke TAKADA*, 1, Kazuma KAITOH1, 2, Koji TSUDA3, Yoshihiro YAMANISHI1, 2
Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems
Engineering, Kyushu Institute of Technology, 2Department of Complex Systems Science,
Graduate School of Informatics, Nagoya University, 3Graduate School of Frontier Sciences, The
University of Tokyo1( * E-mail: takada.shinnosuke113@mail.kyutech.jp )Drug discovery generally takes more than 10 years and billions of dollars. In the early stage, finding hit compounds
and identifying lead compounds are significant tasks. However, it is difficult to explore the tremendous chemical spaceconsisting of more than 1060 small organic compounds. The chemical structure generator is an algorithm to generates
novel chemical structures with desired properties automatically. Thus, it contributes to enhancement of the efficiency of
drug discovery. Most previous methods have been based on chemical information, and few have considered biologicalinformation. To address the issue, transcriptome-based chemical structure generators were recently proposed, but thesynthesizability for generated chemical structures was not considered. Even if novel chemical structures with desired
properties are designed, they should be evaluated experimentally. Thus, estimating difficulty level of compounds isrequired practically.
In this study, we propose a computational method to generate novel chemical structures considering bothsynthesizability and affinity with an arbitrary target protein. Our proposed method consists of transcriptome-based
compound selection and two types of variational autoencoders (VAEs): reaction-based VAE [1] and structure-based
VAE [2]. Input chemical structures to our proposed model were selected based on the transcriptome profiles, and thenthe chemical structures were expanded by the architectures. As a case study, we took epidermal growth factor receptor(EGFR) as a target protein. Our proposed method was able to generate hit-like chemical structures that had a common
skeleton with Gefitinib, a known EGFR inhibitor, and to generate the corresponding synthetic root of the newly
generated compound. Our proposed method was able to successfully reconstruct a nucleophilic substitution reactionthat was part of the synthetic route for Gefitinib. This result supports the validity of our proposed method. Our proposed
method is expected to be useful for various applications in drug discovery.
[1] D. H. Nguyen and K. Tsuda, Generating reaction trees with cascaded variational autoencoders, Journal of Chemical
Physics, 2022, 156.
[2] K. Kaitoh; Y. Yamanishi, Scaffold-Retained Structure Generator to Exhaustively Create Molecules in an Arbitrary
Chemical Space, Journal of Chemical Information and Modeling, 2022, 62, 2212-2225.

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O03 分子計算・機械学習 /AI による創薬研究の最新動向と展望

The latest trends and prospects in drug discovery research using molecular calculation, machine learning, and AI

O03-05
Benchmarking Deployed Generative Models on Elix Discovery
Vincent Paul Guillaume RICHARD*, Jun Jin CHOONG
Research Department, Elix, Inc.

( * E-mail: vincent.richard@elix-inc.com )The pursuit of discovering novel and effective drugs has always been a significant challenge in the pharmaceuticalindustry. With the advent of generative models, such as generative adversarial networks (GANs) and variational
autoencoders (VAEs), the landscape of drug discovery has witnessed a transformative shift. These generative models
offer immense potential to expedite the drug development process by generating molecular structures with desirable
properties, thus significantly reducing the time and cost required for experimental synthesis. To date, we (Elix) havedeveloped a drug discovery platform that attempts to bridge the gap between AI and medicinal chemists which we call
Elix Discovery.
Recently, much effort from Elix has been focused towards integrating various generative models with the mainemphasis of allowing more choices for medicinal chemists to choose and utilize state-of-the-art generative models.
However, this approach leads to a difficulty of choices. Furthermore, each model has their own strengths and weaknesses
which increases the difficulty of selecting a one-true-best option.
In this work, we attempt to tackle the aforementioned issue by relating real life use cases of Elix Discovery as aplatform for medicinal chemist to explore new potential drugs. We performed in-house benchmark of our generativemodels that has been trained under various conditions.
For our benchmarking process, we employed the MolOpt, a benchmark that emphasizes the quality of the molecules
generated in relation to the frequency of calls made to the reward function. This metric is of particular significance,
given the prevalent use of computationally demanding reward functions, such as docking, in recent methodologies.
Consequently, enhancing this relative performance is essential in a production scenario.
Upon analysis of the obtained results, it becomes apparent that only models utilizing Policy-Based Reinforcement
Learning methods show better performance when the feedback to the reward function increases.
In conclusion, we offer important insights on assessing generative models in a real-world production setting, while
also outlining their potential limitations.

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O03 分子計算・機械学習 /AI による創薬研究の最新動向と展望

The latest trends and prospects in drug discovery research using molecular calculation, machine learning, and AI

O03-06
Improving the Practical Applicabilityof Computer-assisted Chemical Synthesis Planning Approaches
using Substructure Patterns
Haris HASIC*, 1, 2, Takashi ISHIDA1
Department of Computer Science, Tokyo Institute of Technology, 2Elix, Inc.

1

( * E-mail: hasic@cb.cs.titech.ac.jp )Computer-assisted chemical synthesis planning has recently re-emerged as one of the most researched topics in
Chemoinformatics. Most notably, the retrosynthesis-centered machine learning approaches have proven particularly
promising. However, the high level of innovation and reliability required for any computer-assisted synthesis planningapproach to be practically applicable has been repeatedly proven difficult to achieve. The prominent obstacle is the lowquantity and quality of open-source chemical reaction data, but the current modeling practices may inherently lower
practical applicability.
The main objective of this research study is to propose a novel paradigm to improve the practical applicabilityof chemical synthesis planning approaches and limit the dependency on chemistry expert knowledge. This paradigm
encompasses multiple novel concepts, including the analysis of chemical compound substructures using so-calledchemical synthesis substructure patterns, the determination of reactive and non-reactive substructures without theutilization of atom-to-atom mapping and chemical reaction templates, and the re-contextualization of the chemical
reaction structural transformation suggestions within the original chemical reaction context.
Ultimately, the proposed novel paradigm is integrated into existing state-of-the-art computer-assisted chemical
synthesis planning pipelines and directly compared to approaches utilizing atom-to-atom mapping and chemical
reaction templates, where it demonstrates improvements in execution speed while maintaining the expected high levelof accuracy.

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O03 分子計算・機械学習 /AI による創薬研究の最新動向と展望

The latest trends and prospects in drug discovery research using molecular calculation, machine learning, and AI

O03-07Development of Auto-FMO protocol and data accumulationin FMODB through 2023
Chiduru WATANABE*, Kikuko KAMISAKA, Norihiko TANI, Teruki HONMA
RIKEN Center for Biosystems Dynamics Research( * E-mail: chiduru.watanabe@riken.jp )Since 2014, we have performed fragment molecular orbital (FMO) calculations [1] for various drug targets (e.g.,kinase, nuclear receptor, protease, protein-protein interaction, and COVID-19) as the activities of the FMO drug
design consortium (FMODD, https://fmodd.jp/top-en/). The benefit of this fragmentation scheme of FMO calculationis the availability of inter-fragment interaction energy and pair interaction energy decomposition analysis; we clearly
understand the intermolecular interactions of biomolecules through the interaction energy analyses. To accumulate the
FMO data, we developed an FMO database (FMODB, https://drugdesign.riken.jp/FMODB/) and an automated FMO
calculation (Auto-FMO) protocol [2,3]. The Auto-FMO protocol has been developed by BIOVIA Pipeline Pilot andMOE software as a primary type.
In this study, we present the development status of the Auto-FMO protocol and the data accumulation of FMO
calculations in the FMODB using the protocol. The structural preparation and optimization options have been improvedto accommodate various structural data (e.g., structural analysis and construction methods, resolution). We have
implemented the ability of structural preparation conditions for protonation states and N- and C-terminal capping andstepwise structural optimization under various constraints. In addition, improvements are underway to create input
files of FMO calculations based on the structures to perform efficient calculations (side-chain fragmentation of metal
coordinated residues) for metal-containing proteins. These improvements were validated using data sets such as antigenantibody, high-resolution X-ray crystal structure, and apoprotein. At the same time, we are also working on developing
the Auto-FMO protocol in a configuration using free software such as Python and Amber Tools. As a first step towardmaking this free version of the protocol widely available for collaborators, we have extended it to work on the
supercomputer Fugaku, although it had been researched and developed in-house. The development of these functions
for the Auto-FMO protocol will be expected to accelerate the collection of quantum chemical calculation data with highaccuracy for AI drug discovery.
Acknowledgments
The authors thank Dr. Kazumi Tsuda, Mr. Daisuke Murayama, Dr. Shu Koyama, and Dr. Tomoharu Isobe of Science& Technology Systems, Inc., Japan, and Dr. Daisuke Takaya of Osaka University for technical support. This research
was done in activities of FMODD. This research was partially supported by BINDS from AMED under Grant Number
JP23ama121030 and MEXT KAKENHI Grant Number JP 23K18192. The results of FMO calculations were obtainedusing Fugaku (project IDs: hp230131 and ra000017) and HOKUSAI (ID: Q23306).
Reference
1. K. Kitaura et al ., Chem. Phys. Lett. , 1999, 313 , 701-706.
2. C. Watanab et al ., CBI J. , 2019, 19 , 5-18.
3. D. Takaya et al ., J. Chem. Inf. Model. , 2021, 61 , 777-794.

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O04-01
Exploring the Selectivity and Binding Mechanism of ToP-DNJ Toward
Endoplasmic Reticulum α-Glucosidase II
Mochammad Arﬁn Fardiansyah NASUTION*, 1, 2, 3, Gert-Jan BEKKER1, Suyong RE3,
Chioko NAGAO1, 2, 3, Kenji MIZUGUCHI1, 2, 3
Laboratory for Computational Biology, Institute for Protein Research, Osaka University,
Department of Chemistry, Graduate School of Science, Osaka University, 3Artificial Intelligence
Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Healthand Nutrition
1

2( * E-mail: marfin.f@sci.ui.ac.id )ToP-DNJ, a tocopherol-conjugated DNJ-derived compound, has gained attention for its selective activity against
endoplasmic reticulum α-glucosidase II (ER GluII), a glucosidase hydrolase family 31 (GH31) enzyme pivotal for
ER quality control for proper protein folding and thus a promising target for broad-spectrum antiviral drugs. Yet, itsbinding mechanism remains elusive, which hinders our understanding of ToP-DNJ mechanism selectivity toward GluII.
Here, we employed template-based docking, classic molecular dynamics (MD) and MD-based mutational analysis toelucidate ToP-DNJ's interactions at the glucose binding site of GluII and lysosomal alpha-glucosidase (GAA). The
resultant ToP-DNJ binding pose was compared with DNJ, bound to GluII and GAA, and validated against the reported
experimental IC50 values via intermolecular contact stability (R-value) analysis of the MD trajectories. Herein, we
propose two potential modes of binding: (i) ToP-DNJ induces a conformational change in the Trp523-Ser528 loop, and(ii) hydrophobic regions of ToP-DNJ strongly interact with Phe307, forming stable pi-pi interactions that are absent
in GAA as Phe307 is not observed in other GH31 enzymes. This discovery is essential for identifying the interactions
responsible for the GluII's selective activity, influencing ToP-DNJ’s specificity toward GluII and future development ofa stronger, selective GluII enzyme inhibitor.

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O04-02In-silico design of peptide inhibitors containing unnatural amino acidSuyong RE*, 1, Kenji MIZUGUCHI1, 2, Yusaku HONDA3, Daisuke TAKAHASHI3, 4,
Ryo YAZAKI3, Takashi OHSHIMA3
Artificial Intelligence Center for Health and Biomedical Research, National Institutes ofBiomedical Innovation, Health and Nutrition, 2Institute for Protein Research, Osaka University,
3
Graduate School of Pharmaceutical Sciences, Kyushu University, 4Faculty of Pharmaceutical
Sciences, Sojo University
1( * E-mail: suyongre@nibiohn.go.jp )Protein-protein interactions (PPI) are essential for many biological functions and are associated with diseases.
Middle-sized peptides are of interest as PPI inhibitors due to their inherent advantages over small molecules orantibodies. Introducing unnatural amino acids into peptides can enhance hydrophobicity, conformational stability, andpeptidase tolerance, leading to the high target specificity and bioavailability. However, finding the optimal number andposition of unnatural amino acid insertions require a large number of trial-and-error combinations. Here, we present an
in-silico approach to evaluate potential candidates in priori using molecular dynamics simulations. Features representing
dynamical structure of peptides were obtained from the simulations and used to rank candidate peptides. Thisapproaches successfully used to identify the structure of experimentally designed peptide [1]. We further validated theapproach against published data of peptide inhibitors targeting SARS-CoV-2 [2]. We then used the approach to design
the peptide inhibitor containing unnatural amino acid targeting SARS-CoV-2. We found that this approach not only
efficiently narrowed down the candidates to be synthesized, but also allowed us to understand the mechanism of actionof these peptide inhibitors.
[1] Tsuji, Taro, Kayoko Hashiguchi, Mana Yoshida, Tetsu Ikeda, Yunosuke Koga, Yusaku Honda, Tsukushi Tanaka, etal. “α-Amino Acid and Peptide Synthesis Using Catalytic Cross-Dehydrogenative Coupling.” Nature Synthesis 1,
no. 4 (April 2022): 304–12.
[2] Karoyan, Philippe, Vincent Vieillard, Luis Gómez-Morales, Estelle Odile, Amélie Guihot, Charles-Edouard Luyt,
Alexis Denis, Pascal Grondin, and Olivier Lequin. “Human ACE2 Peptide-Mimics Block SARS-CoV-2 Pulmonary
Cells Infection.” Communications Biology 4, no. 1 (December 2021): 197.

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Computational Chemistry (Molecular Modeling) / Drug Discovery Application / Quantum-Structural Life Science / Computer-Aided Drug Design

O04-03
Analysis of the binding profile of quinonoid -form of
dihydropteridine to quinonoid -dihydropteridine reductase
Haruka KONO*, Satoshi HARA, Tadaomi FURUTA, Hiroshi ICHINOSE
School of Life Science and Technology, Tokyo Institute of Technology( * E-mail: kono.h.ab@m.titech.ac.jp )Quinonoid dihydropteridine reductase (QDPR, EC 1.5.1.34) is an enzyme which belongs to NADH-dependent
type short-chain dehydrogenase/reductase (SDR) family. It catalyzes the reduction of quinonoid dihydrobiopterin(q BH2) to tetrahydrobiopterin (BH4) in the BH4-recycling pathway. Although QDPR is a key enzyme for maintaining
intracellular BH4 levels, the precise catalytic properties and reaction mechanisms of QDPR are poorly understood
due to the instability of quinonoid -form substrates. In this study, we analyzed the binding profile of q BH2 to human
QDPR in combination with in silico and in vitro methods. First, we performed docking simulation of q BH2 to QDPR
to obtain possible binding modes of q BH2 at the active site of QDPR. Then, among them, we determined the mostplausible binding mode using molecular dynamics simulations revealing its atomic-level interactions and confirmed itwith the in vitro assay of mutant enzymes. These findings in this study provide important insights into biopterin andfolate metabolism and would be useful for developing drugs for neglected tropical diseases, because trypanosomatid
protozoans, including Leishmania and Trypanosoma , require exogenous sources of biopterin for their growth.
Publication
Kono et al ., J. Biochem . (In press)― 153 ―

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O04-04
Study Improves Performance of Pocket Comparisons
Using Machine Learning
Risa SHIOZAWA*, Masakazu SEKIJIMA, Nobuaki YASUOTokyo Institute of Technology( * E-mail: lisa120106@gmail.com )In recent years, there has been a growing trend to apply computational methods to drug discovery research with theaim of reducing R&D costs. In the search for compounds essential for drug discovery research, it is considered better tofocus on the structure of the ligand binding site, called a “pocket”.
Many methods have already been developed to compare pocket structures. Among them, DeeplyTough is a method
using pairwise pocket matching, in which the 3D structure of the pocket is convolved with a CNN and n-dimensionaldescriptors are extracted to compare the structures, although DeeplyTough showed higher AUC values than otherbenchmarks in comparison with TOUGH-M1, ProSPECCTS showed lower AUC values than the other benchmarks inseveral sub-datasets of ProSPECCTS.
In this study, we pointed out the problems with DeeplyTough and made improvements to develop new features that
better capture the nature of the pockets. We aimed to propose a new pocket structure vectorization model by adjusting
three points: the loss function, the need to account for the flexibility of the protein structure, and the channel structure
when convolving with the CNN.
The results showed that the improvement of the loss function improved the accuracy of all indicators, and theimprovement using Molecular Dynamics Simulation enabled us to take into account the fluctuations of the protein
structure. For the indicators sensitive to conformational changes, the addition of information on the protein surface
suppressed the effect.

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Computational Chemistry (Molecular Modeling) / Drug Discovery Application / Quantum-Structural Life Science / Computer-Aided Drug Design

O04-05Fundamentals of Molecular Dynamics Simulation
Yuto KOMEIJI*, 1, 2, 3, 4
Health and medical research institute, AIST, 2Department of Chemistry, Rikkyo University,
3
Center for Biosystems Dynamics Research, RIKEN, 4Graduate school of pharmaceuticalsciences, Osaka University

1( * E-mail: y-komeiji@aist.go.jp )We present our recent publication, a comprehensive textbook titled “Fundamentals of Molecular Dynamics
Simulation” [1]. This work serves as a practical guide to molecular dynamics (MD) simulations, elucidating bothfoundational algorithms (see Fig. 1) and the underlying theories that govern MD. The book delves deeply into the realmof MD encompassing essential topics such as time integration, molecular modeling, boundary conditions, solvation
effects, forces and potentials, ensembles, constraints and restraints, geometry optimization, and trajectory analysis.
Rather than presenting novel techniques and applications, the primary objective of this work is to offer readers asystematic and in-depth exposition of traditional MD methodologies.
While the primary focus of this text is MD simulations involving biological molecules, the methodologies described
possess broad applicability across various molecular systems. Moreover, the book imparts a profound understanding of
pertinent physics concepts, including Hamiltonian and non-Hamiltonian dynamics, classical versus quantum treatmentsof chemical bonds, and the Maxwell-Boltzmann distribution. Another noteworthy facet of the book lies in its provisionof technical insights for ensuring the robust execution of MD simulations.
In this context, our presentation will offer an overview of the textbook's content. The lecture will be delivered inJapanese.
[1] Yuto Komeiji, “Fundamentals of Molecular Dynamics Simulation, 1st Edition (in Japanese),” CBI eBook No. 6.
Available at: https://cbi-society.org/home/pub_ebook.html.

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Computational Chemistry (Molecular Modeling) / Drug Discovery Application / Quantum-Structural Life Science / Computer-Aided Drug Design

O04-06MAXS reveals a disorder-to-order transition of the intrinsicallydisordered region in active MAP2K6
Takashi MATSUMOTO*, 1, Seisuke HASEGAWA2, Takashi SATO1,
Takayoshi KINOSHITA 2
1

Rigaku Corporation, 2Graduate School of Science, Osaka Metropolitan University( * E-mail: t-matumo@rigaku.co.jp )The protein function in solution is closely related to its unique and complex three-dimensional structure andstructural transition between disorder and order state, especially the characteristic part recognized as intrinsicallydisordered regions (IDRs).
Recent advances have dramatically changed the landscape of protein structure analysis, including higher resolution
and precision in single crystal structure analysis and improvements in single particle analysis and electron diffraction
techniques using electron microscopy. In addition, structure prediction methods such as AlphaFold1) have beendeveloped, and their precision has also been improving. As a result, high-resolution protein structures have become
more versatile and familiar.
However, transitions between the disorder and order state of IDRs, which is considered to be closely related
to protein function, are challenging to elucidate by these methods since they observe the targets in a ‘frozen’ state.
Therefore, observation of proteins in the solution, under the condition where the targets work liberated from themethodological limitations, is essential for better understanding the IDR functions and may lead to new insights into thedrug design strategies employing the function of IDRs.
Small angle X-ray scattering (SAXS) has been used to obtain protein structural information in solution. However,the small-angle information is intrinsically limited and insufficient for analyzing molecular behavior in detail, such as
the displacement of a loop region in a domain structure. But, if we can obtain the scattering data in good quality up toa sufficient resolution to see the more detailed characteristics and conformational movements in detail, it does help tounderstand the protein function in the solution, which is not known yet.
We use the solution scattering method, abbreviated MAXS (middle angle x-ray scattering), which employs scattering
data in a wide range up to higher q area to obtain more detailed structural information of proteins in solution.
Here, we present a MAXS analysis of the key kinase crucial in the MAP kinase signaling cascade. Specifically, thedisorder-to-order transition of the IDR in human MAP2K6 in an active state was investigated. The combination of thecrystal structure, AlphaFold predicted structures, and the results delivered from the MAXS experiment suggested thatthe structure in the N-terminal part of the MAP2K6 changed through a disorder-to-order transition in the process ofcomplex formation between MAP2K6 and p38α MAP kinase and is shedding light on the structural transition during
complex formation that is difficult to observe by other methods observing ‘frozen’ state. MAXS provides uniqueinformation under solution conditions where the actual events occur. The finding here may lead to a novel mechanism
for function and discovery through a new strategy for regulating active MAP2K6.
Reference
1. Jumper, J., et al. Nature (2021). 596, 583-589.

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Computational Chemistry (Molecular Modeling) / Drug Discovery Application / Quantum-Structural Life Science / Computer-Aided Drug Design

O04-07
Near atomic-resolution cryoEM structure of PROTAC ternary complex
Jack YAN*, Yifan HU, Jiaquan WU
CryoEM department, Wuxi Biortus Biosciences Co. Ltd.

( * E-mail: jack.yan@wuxibiortus.com )PROteolysis-TArgeting Chimera (PROTAC) emerges as a paradigm-changing strategy for drug target degradation,and has undergone extensive studies with a number of PROTAC compounds in clinical trials. Some crystal structuresof the E3 ligase-PROTAC-target ternary complexes based on several E3 ligase platforms have been reported, providing
invaluable information on the interaction fingerprints for the PROTAC system. However, due to the dynamic nature of
the system, it is extremely challenging to successfully obtain crystal structures of PROTAC complex. Here, we present,
first-time-ever, a near atomic-resolution structure of a ternary complex demonstrating that cryoEM is more suitable orefficient to determine such a dynamic complex structure.
We have successfully solved the cryoEM structure of the VHL(ElonginB/C-Cul2)-PROTAC-target ternary complex
at ~3.5 Å resolution. The high-resolution atomic structure has revealed the pose of PROATC molecule, and detailed
interaction interface between the PROTAC and VHL/target at atomic level. Interestingly, we found a neo-interface
between VHL and the target, which enhances the cooperativity of the ternary complex formation. In addition, using 3Dclassification, we obtained both open and close conformations of the complex, showing the dynamic movements due tothe elongation of ubiquitin chain.
In addition to the application of cryoEM single-particle analysis (SPA), we are also going to show that microED canbe used for the rapid structure determination of the PROTAC molecules and the CMC impurities.

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Computational Chemistry (Molecular Modeling) / Drug Discovery Application / Quantum-Structural Life Science / Computer-Aided Drug Design

O05-01
Molecular details on the binding of inhibitors to c-Src kinase revealed bymolecular dynamics simulations
Ai SHINOBU*, 1, 2, Suyong RE3, Hiraku OSHIMA2, 4, Yuji SUGITA2, 5, 6
WPI Premium Research Institute for Human Metaverse Medicine, Osaka University, 2Center for
Biosystems Dynamics Research, RIKEN, 3Artificial Intelligence Center for Health and Biomedical
Research, National Institutes of Biomedical Innovation, Health, and Nutrition, 4Graduate
School of Science, University of Hyogo, 5Cluster for Pioneering Research, RIKEN, 6Center for
Computational Science, RIKEN
1( * E-mail: shinobu.ai.prime@osaka-u.ac.jp )Molecular description of protein-ligand binding is crucial for understanding the mechanism of drug action.
Computational methods are used with great success in drug discovery, significantly reducing costs and speeding up thediscovery process. Methods based on all–atom molecular dynamics (MD) simulations are especially powerful, as they
are capable of giving insight into the molecular mechanism of the binding process.
In this work, we focus on the protein kinase (PK) family. PKs play pivotal roles in cellular processes and theirmalfunction is related to diseases such as cancer. The action mechanism of PKs is controlled by intricate dynamic
relations between their conserved structural motifs, thus they are considered attractive but difficult drug targets. We
studied the binding process of two inhibitors to the c-Src kinase protein using MD simulations. We aimed to inspect theeffect of size and flexibility on the binding mechanism. For this, we used PP1, which is relatively small and planar, andthe cancer drug dasatinib, which is larger and has several rotatable bonds.
Protein-ligand binding occurs over a timescale of microseconds to milliseconds, which conventional MD simulations
struggle to sample efficiently. We thus used a two-dimensional replica-exchange method (gREST/REUS), adding biases
in the form of increased solute temperature (gREST) as well as a spatial restraint along the reaction coordinate (REUS).
We used a total of 240 replicas which exchanged parameters at constant intervals during the simulation, facilitating
the crossing of high energy barriers. We performed the simulations on the supercomputer Fugaku. The simulation
method, as well as the use of massively parallel computing architecture required rigorous modeling, tuning andcalibration of system parameters.
In our simulations, we sampled protein-inhibitor distances ranging from the bound pose to a distance at which the
inhibitors no longer interact with the protein. We sampled 100 ns (PP1) and 150 ns (dasatinib) per replica and obtained
converged free-energy landscapes, allowing description of the complete binding process.
We were able to identify the canonical bound pose as well as an additional pose in which the inhibitors reside inside
the binding pocket. In addition, we identified poses in the intermediate and encounter regions and characterized thepaths connecting between them. We found that the two inhibitors travel similar paths upon approaching the binding site.
The difference between the inhibitors arises upon entering the binding site where dasatinib can adopt a bent form which
can play a mechanistic role. This difference between the inhibitors might be the structural origin for the difference intheir selectivities and affinities. Such mechanistic understanding is crucial as it lays the foundations for rational designof new drugs with improved efficacy.

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Computational Chemistry (Molecular Modeling) / Drug Discovery Application / Quantum-Structural Life Science / Computer-Aided Drug Design

O05-02
Structural and Computational Insight into Dynamics and Intermediate
State in Activation of Orexin 2 Receptor
Shun YOKOI*, 1, 2, Ayori MITSUTAKE1
Department of Physics, School of Science and Thechnology, Meiji University, 2Structural Biology
Department, School of Medicine, Stanford University

1( * E-mail: syokoi@meiji.ac.jp )G protein-coupled receptors (GPCRs) are superfamily of membrane proteins which consist of seven transmembrane(TM) domain. GPCRs are involved in various biological processes through activation, so they are important as drug
discovery targets, and over 30% of small molecule drugs on the market are targeted to them. Once GPCRs activated,
they bind to intracellular signaling cascades, such as G protein and β-Arrestin, and transduce the extracellular signals tointercellular side. Orexin 2 receptor (OX2R) is one of the GPCRs and plays important roles in the regulation of sleepwake cycle. About 3 million people suffer from narcolepsy due to OX2R dysfunction. Thus, the drug development of
the agonist for OX2R is expected as effective therapeutics. Recently, the structures of both inactive and active states
were clarified, and the activation mechanisms discussed based on these static data. However, it has been revealed that
GPCRs form various intermediate states while the activation, so that the insight into the dynamics is important for the
drug discovery.
In this study, we performed over 30 microsecond-scale molecular dynamics (MD) simulations for various systems
to elucidate the atomic-level dynamics and intermediate state in OX2R activation. Based on the inactive and active state
structures (PDB ID: 5ws3, 7l1v), we performed the MD simulations for wild type (WT) monomer, V309Y mutant whichshows constitutively active in other GPCRs, complex with antagonist or agonist, and complex of OX2R and Gi protein
to investigate interface dynamics. The OX2R-lipid bilayers complexes were constructed using CHARMM-GUI, and the
MD simulations were performed using Amber force fields and Amber software.
First, we treated the simulation data as a single dataset and applied relaxation mode analysis (RMA) to investigate
characteristic conformational transitions between inactive and active states. Then, we found that relaxation mode 1(RM1), which is the slowest mode of structural change, corresponds to the conformational differences between inactive
and active states. Furthermore, we extracted three characteristic amino acid residues on TM3, Q134, V142 and R152,
based on the structural fluctuation along the RM1. V142 and R152 are conserved in many GPCRs called PIF motif(PVF in OX2R) and DRY motif, respectively, and known to important in the GPCR activation. Here, we discuss thequantitatively evaluations for the characteristics of these amino acid residues in different systems and the activation
mechanism attributed to TM3. Also, we discussed biased-signaling mechanism based on the MD simulations and GPCR
database. In addition, it was suggested that the complex of OX2R and Gi protein stabilized in a conformation resembled
to non-canonical (NC) state, which is an intermediate state in the activation of neurotensin 1 receptor (NTSR1). Here,
we discuss the possibility of the NC state in OX2R.

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Computational Chemistry (Molecular Modeling) / Drug Discovery Application / Quantum-Structural Life Science / Computer-Aided Drug Design

O05-03
Conformation Search of Ternary Complexes for Rational PROTAC
Linker Design Using Enhanced Sampling Method
Ryunosuke YOSHINO*, 1, 2, Genki KUDO3, Takumi HIRAO4, Takatsugu HIROKAWA1, 2
Faculty of Medicine, University of Tsukuba, 2Transborder Medical Research Center, Universityof Tsukuba, 3Physics Department, Graduate School of Pure and Applied Sciences, University ofTsukuba, 4Master’s Program in Medical Sciences, Graduate School of Comprehensive Human
Sciences, University of Tsukuba
1( * E-mail: yoshino.r.aa@md.tsukuba.ac.jp )Proteolysis-targeting chimeras (PROTACs), which form a ternary complex with E3 ligase and target proteins
to induce ubiquitination, are attracting attention as a new chemical modality [1]. To function as a drug, PROTAC
requires stabilizing the ternary complex and lysine residues recognition of target protein for the E2 ligase domain for
ubiquitinate. This chemical modality is divided into a ligand that binds to each protein and a linker part that connects
them. The linker part is essential for forming stable ternary complexes [2].
Prediction of ternary complexes by in silico techniques is demanded for rational PROTAC design. However,conventional in silico techniques such as small molecule drug-protein complex prediction and protein-protein docking
simulations are insufficient to accurately predict the structure of ternary complexes. Thus, we applied Parallel Cascade
Selection Molecular Dynamics (PaCS-MD) simulation and Outlier FLOODing (OFLOOD) method [3,4], an extended
sampling method, to efficiently predict the conformation of the ternary complex. In this presentation, we will discuss theresults of sampling PROTAC, which forms a ternary complex with von Hippel–Lindau protein (VHL) and the second
bromodomain of Brd4 (Brd4BD2) [5], and introduce the application of supercomputers such as Fugaku to study new
chemical modalities.
Reference[1] Toure M, Crews CM. Angew Chem Int Ed Engl. 2016;55(6):1966-1973.
[2] Tang R, Wang Z, Xiang S, et al. JACS Au. 2023;3(6):1775-1789.
[3] Harada R, Kitao A. J Chem Phys. 2013;139(3):035103.
[4] Harada R, Nakamura T, Shigeta Y. J Comput Chem. 2016;37(8):724-738.
[5] Chan KH, Zengerle M, Testa A, Ciulli A. J Med Chem. 2018;61(2):504-513.

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O05-04
Conformational studies of oligo (N -methylalanine) and applicationof the oligomer for designing a ligand against a cancer-related
protein MDM2
Jumpei MORIMOTO*, 1, Marin YOKOMINE1, Yota SHIRATORI2, Takumi UEDA3,
Koji UMEZAWA4, 5, Koichiro MIYANISHI6, 7, Wataru MIZUKAMI7, Koh TAKEUCHI3, Shinsuke SANDO1, 2
Department of Chemistry and Biotechnology, Graduate School of Engineering, The Universityof Tokyo, 2Department of Bioengineering, Graduate School of Engineering, The University ofTokyo, 3Graduate School of Pharmaceutical Sciences, The University of Tokyo, 4Departmentof Biomedical Engineering, Graduate School of Science and Technology, Shinshu University,
5
Institute for Biomedical Sciences, Interdisciplinary Cluster for Cutting Edge Research, Shinshu
University, 6Division of Advanced Electronics and Optical Science, Department of Systems
Innovation, Graduate School of Engineering Science, Osaka University, 7Center for Quantum
Information and Quantum Biology, Osaka University
1( * E-mail: jmorimoto@chembio.t.u-tokyo.ac.jp )Peptides have a wide surface area and, therefore, can bind to proteins with high affinity. However, the conformational
flexibility makes it difficult to rationally design peptides with high protein-binding affinity. Recently, we have shownthat the oligo(N -methyl-L-alanine) works as a scaffold for targeting the protein surface1–4. In this presentation, first, theconformational analysis of oligo(N -methyl-L-alanine) will be briefly described. Oligo(N -methyl-L-alanine) is shown tostably form a rod-like shape and serve as a scaffold for displaying substituents on the amide nitrogens. Then, a strategy
for designing a ligand against a cancer-related protein MDM2 using the oligomer as a scaffold will be shown. Next, we
will show that combining N -methyl-L-alanine (L-NMA) residues and N -methyl-D-alanine (D-NMA) residues realizes
peptides with diverse scaffold shapes5. First, we show that the conformational space of L-NMA residue and the D-NMA
is restricted per residue basis via density functional theory (DFT) studies of minimal model structures. Next, wedemonstrate that combining the two monomers can realize a bottom-up design of diverse scaffold shapes. The structuresof the hexamers consisting of the two monomers have been investigated by molecular dynamics simulations and DFT
calculations, and the obtained results were validated by nuclear magnetic resonance analysis.

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O05-05
Uncovering the Mechanism of  Molecular Glue Protein Degraders:The high energy water molecules at the P-P interface play key rolesin the recruitment of RBM39 to DCAF15
Osamu ICHIHARA*
Application Science, Schrödinger KK( * E-mail: osamu.ichihara@schrodinger.com )The anticancer agent Indisulam inhibits cell proliferation by causing the degradation of RBM39, an essential mRNA
splicing factor. Indisulam promotes an interaction between RBM39 and the DCAF15 E3 ligase substrate receptor,
leading to RBM39 ubiquitination and proteasome-mediated degradation. Indisulam potently binds (187 nM) in ahydrophobic pocket formed between DCAF15 and RBM39 upon association, providing additional interactions between
the two proteins. Although the X-ray co-crystal structure reveals several direct and water-mediated interactions, it has
been unclear how Indisulam promotes the strong binding of the two proteins.
In order to shed light on this problem, we performed MD simulations of the RBM39/DCAF15 complex andanalyzed the thermodynamic properties of the water molecules trapped within the interface hydrophobic cavity. The
analysis identified a number of high energy water molecules and suggested that the desolvation energy associated withthe displacement of the water molecules is the key driving force of the "gluing" effect of Indisulam. The pEC50 of theIndisulam analogue is shown to correlate extremely well with the calculated desolvation energy. Moreover, it has been
demonstrated that the Piper protein-protein docking of RBM39 and DCAF15 accurately discerns the favored interface
between the two proteins as observed in the X-ray structure. The accurate docking conformation, closely resembling
the X-ray structure, could be readily distinguished from various poses through WaterMap hydration state analysis of theinterface.

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O05 計算化学（分子計算）/ 創薬応用 / 量子構造生命科学 / Computer-Aided Drug Design

Computational Chemistry (Molecular Modeling) / Drug Discovery Application / Quantum-Structural Life Science / Computer-Aided Drug Design

O05-06Development of hit-to-lead molecular optimization bymulti-objective Monte Carlo Tree Search
Takamasa SUZUKI*, 1, Nobuaki YASUO2, Sekijima MASAKAZU1
Department of Computer Science, Tokyo Institute of Technology, 2Academy for Convergence ofMaterials and Informatics, Tokyo Institute of Technology

1( * E-mail: suzuki.t.dq@m.titech.ac.jp )Drugs have saved millions of lives in human history, but the cost of developing a new drug has risen dramatically inthe last decades. Recently, more and more computer-aided drug discovery (CADD) methods have been practiced in thepharmaceutical industry. To address that problem, studies on molecular generative models that can generate compoundswith novel structures have been raised in recent years. Early generative models decide their search direction on
optimization for a single objective function, however, a drug candidate required multiple evaluations. Recent generativemodels achieve multi-objective optimization with the weighted sum or product. These models replace the difficulty ofmulti-objective optimization with the difficulty of weighting and comparing objective functions.
In this study, we developed a new deep learning-based extendable multi-objective hit-to-lead molecular optimizer,
which could simultaneously optimize molecules based on their properties and their affinity towards target proteins. This
optimizer utilizes a recurrent neural network (RNN) to generate molecules and Pareto Multi-Objective Monte Carlo
Tree Search (Pareto MOMCTS) to decide the search direction. By using our method, we have validated the generationof compounds for specific target proteins using the drug-like properties and docking scores as objective functions. In
each search process, a new Monte Carlo search tree is built, and the change of the Pareto front, which decides the search
direction, shows the transfer of the search process in different stages. Additionally, some evaluations are not suited toobjective functions such as toxicity. Because some evaluations required simply better than threshold values, not to be
optimized. Thus, in this study, we compared the performance of evaluations as objective functions and as thresholdvalues. This study will enable our method to provide more effective drug design assistance when compared to methods
with a single objective, such as QED.

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O06-01
Changes in the conformational landscape of Src kinase
upon substrate recognition
Song-Ho CHONG*, 1, Yuji SUGITA2
1

Global Center for Natural Resources Science, Faculty of Life Sciences, Kumamoto University,
2
Center for Pioneering Research, RIKEN( * E-mail: chong@kumamoto-u.ac.jp )Protein kinases inherently sample active and inactive conformations. These conformational changes play a crucialrole in regulating the kinase activity. However, how kinases switch conformations in response to external signals
remains incompletely understood. Here, we investigate changes in the conformational landscape of Src tyrosine kinase
upon binding with a peptide substrate and demonstrate that, quite unexpectedly, the substrate recognition significantly
enhances the transitions to inactive conformations. This was done by conducting unbiased molecular dynamics
simulations (totaling 50 microseconds) initiated from the apo and substrate-bound forms. All the simulations werecarried out on the Fugaku supercomputer using the GENESIS software. We show that the substrate binding inducessuch inactive conformations in which the alpha-C helix is directed outward, the regulatory spine is broken, and the
ATP binding site is perturbed. Our results are in fact consistent with the recent experiments reporting the negative
cooperativity between ATP and substrate binding to tyrosine kinases, and will contribute to advance our understandingof the regulation mechanisms of this protein family.

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O06 分子認識と分子計算 / 創薬応用 Moleuclar recognition and Molecular modeling / Drug Discovery Application

O06-02Development of a machine learning model for predicting
protein-ligand interactions based on FMO data
Ryosuke KITA*, 1, Chiduru WATANABE2, Masateru OHTA3, Naoki TANIMURA4,
Koji OKUWAKI5, Mitsunori IKEGUCHI3, 6, Kaori FUKUZAWA7, Teruki HONMA2,
Tsuyohiko FUJIGAYA1, Koichiro KATO1
Department of Applied Chemistry, Kyushu University, 2RIKEN Center for Biosystems Dynamics
Research, 3RIKEN Center for Computational Science, 4Mizuho Research & Technologies,
5
Engineering Technology Division, JSOL Corporation, 6Graduate School of Medical Life Science,
Yokohama City University, 7Graduate School of Pharmaceutical Sciences, Osaka University
1( * E-mail: ryosuke092413@gmail.com )Evaluating protein-ligand interactions through molecular simulations plays a crucial role in identifying drug candidates from avast pool of compounds in SBDD-based computational drug discovery. In general, classical force field-based methods have been thestandard for evaluating protein-ligand interactions. Although quantum mechanical (QM) calculations are ideally suited for theevaluation of protein-ligand interactions due to their computational accuracy, the computational cost of QM makes them difficult toimplement with general purpose methods. Nowadays, fragment molecular orbital (FMO) method has made it possible to evaluate
such interactions at QM level. By using the FMO method, protein-ligand interactions can be quantified as inter-fragment interactionenergy (IFIE). However, even using the supercomputer Fugaku, FMO method requires several hours of computation time per
structure, and further reduction of computational cost has been desired. Thus, it has recently been expected to develop a machinelearning model that predicts IFIE without requiring explicit FMO calculations.
This study presents a machine learning model that predicts IFIE obtained by FMO method, which are ligand-protein interactions
at the QM level. To enable the model to handle any ligand, we employed the following descriptors and algorithms. For thedescriptors, we employed Atom-Centered Symmetry Functions (ACSF) [1], a method that can vectorize the surrounding environmentof individual ligand constituent atoms. As a machine learning algorithm, we used High-Dimensional Neural Network Potentials(HDNNP) [2], which can learn IFIE as a sum of interactions acting on individual ligand constituent atoms. Although ACSF andHDNNP were initially proposed to construct machine learning force fields, we employed them to predict protein-ligand interactionsin this study. The target protein selected was CDK2. For each of the 80 ligands known to bind to CDK2, 20 binding poses weregenerated using rdock [3] for a total of 1600 FMO calculations to construct a QM-level protein-ligand interaction dataset. The 1600
prepared data were divided into 1300 training data and 300 test data. Subsequently, the R2 values, which were used to assess thevariability of the predictions from the calculated FMO values, were evaluated. The total IFIE was successfully predicted with a highaccuracy of R2 value of 0.59 (Figure 1).
Acknowledgements
This work was conducted as a part of the Life Intelligence Consortium (LINC) and the FMO Drug Design
Consortium (FMODD). This work was supported in partby the Japan Agency for Medical Research and Development (AMED) under the Drug Discovery and Life Science
Research Support Platform Project (BINDS) (Grant No.
JP23ama121030).
Reference[1] J. Behler, J. Chem. Phys. 134, 074106 (2011).
[2] J. Behler, M. Parrinello, Phys. Rev. Lett. 98, 146401(2007).
[3] S. Ruiz-Carmona, PLoS Comput. Biol. 10, e1003571(2014).

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O06 分子認識と分子計算 / 創薬応用 Moleuclar recognition and Molecular modeling / Drug Discovery Application

O06-03
Utilizing the FMO Database for Transfer Learning in Constructing
Machine Learning Force Fields for Biomolecular Systems
Hiromu MATSUMOTO*, 1, Chiduru WATANABE2, Masateru OHTA3, Naoki TANIMURA4,
Koji OKUWAKI5, Mitsunori IKEGUCHI3, 6, Kaori FUKUZAWA7, Teruki HONMA2,
Tsuyohiko FUJIGAYA1, Koichiro KATO1
Department of Applied Chemistry, Graduate School of Engineering, Kyushu University, 2RIKEN
Center for Biosystems Dynamics Research, 3RIKEN Center for Computational Science, 4Mizuho
Research & Technologies, Ltd., 5Engineering Technology Division, JSOL Corporation, 6Graduate
School of Medical Life Science, Yokohama City University, 7Graduate School of Pharmaceutical
Sciences, Osaka University
1( * E-mail: matsumoto.hiromu.238@s.kyushu-u.ac.jp )In molecular simulations for drug discovery, it is extremely important to achieve both higher accuracy and lower computationalcost. In contrast to classical molecular force fields and quantum mechanical (QM) calculations, machine learning force fields(MLFFs) are expected to be a method that can achieve both higher accuracy and lower computational cost. QM-based datasets havebeen used to build MLFFs, typically derived from conventional DFT or ab-initio methods. However, their huge computational costs
for biomolecules have restricted research scope of MLFFs. To address this, we employed the fragment molecular orbital (FMO)
method1), offering efficient QM calculations for whole biomolecule systems. To validate the utility of FMO data in constructing
MLFFs, we chose Trp-Cage, a small protein, as a model for larger protein studies. We also assessed the effectiveness of transfer
learning in improving the precision of MLFFs by utilizing the FMO Database (FMODB), an exhaustive public database of FMO
results.
For our MLFF training dataset, various Trp-Cage configurations were sampled by molecular dynamics (MD) simulations.
Both potential energies and forces on each atom were calculated for every structure via FMO method. MD simulations of the TrpCage NMR structure (PDBID:1L2Y) in water were performed using GROMACS software with the Amber ff14SB force field andthe TIP3P water model. We obtained 5,000 structures from these simulations at every 1 ns over 50 runs of 100 ns each. We thenperformed FMO calculations (FMO2-MP2/6-31G*) to evaluate energy and force on each structure using the ABINIT-MP programon the Fugaku supercomputer (hp230131). The dataset was divided into train, validation, and test dataset (8:1:1). Our MLFF is basedon the high dimensional neural network potential (HDNNP) framework proposed by Behler and Parrinello2). A pre-trained model fortransfer learning was built using 15,454 FMODB energy records composed of atomic species C, H, N, O, S, F, and Cl.
The R values (raw vs. predicted FMO data) for Trp-Cage's energy (Figure 1a) and force (Figure 1c) were 0.58 and 0.70,
respectively. It was suggested that the constructed MLFF could learn the relationship between structure and force/energy, and
FMO data was effective in MLFFs construction. Figure 1b,dshows prediction results for Trp-Cage via FMODB datatransfer learning. Both energy and force predictions showed
improvements (R = 0.61, 0.78), supporting the efficacy of largescale data pre-training.

Acknowledgments

This work was conducted as a part of the Life Intelligence
Consortium (LINC) and the FMO Drug Design Consortium(FMODD). This work was supported in part by the Japan
Agency for Medical Research and Development (AMED) under
the Drug Discovery and Life Science Research Support Platform
Project (BINDS) (Grant No. JP23ama121030).

Reference

1) Kitaura, K. et al., Chem Phys Lett 313, 701–706 (1999).
2) Behler, J. & Parrinello, M., Phys Rev Lett 98, 146401(2006).

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O06 分子認識と分子計算 / 創薬応用 Moleuclar recognition and Molecular modeling / Drug Discovery Application

O06-04
Understanding the RNA Sequence Specificity of Translation Initiation
Factor Inhibitors through Dynamical FMO Analysis
Yuma HANDA*, 1, 2, Hironori SAITO3, 4, Koji OKUWAKI1, Takayuki FURUISHI1,
Etsuo YONEMOCHI1, Koichito KATO5, Chiduru WATANABE6, Teruki HONMA6,
Takuhiro ITO7, Shintaro IWASAKI3, 4, Kaori FUKUZAWA1, 2
Department of Physical Chemistry, Hoshi University, 2Quantum life science and bioinformatics,
Osaka University, 3RNA Systems Biochemistry Laboratory, RIKEN Cluster for Pioneering
Research (CPR), 4The University of Tokyo, 5Department of Applied Chemistry, Graduate Schoolof Engineering, Kyushu University, 6Laboratory for Structure-Based Molecular Design, RIKEN
Center for Biosystems Dynamics Research (BDR), 7Laboratory for Translation Structural Biology,
RIKEN Center for Biosystems Dynamics Research (BDR)
1( * E-mail: d2002@hoshi.ac.jp )Rocaglamide A (RocA), a natural compound found in Aglaia odorata, has potent anticancer activity and acts as atranslation inhibitor targeting to translation initiation factor eIF4A. This compound is known to function in a uniquemechanism; clamping eIF4A onto purine sequence selectively for steric hindrance for scanning ribosomes [1]. However,the detailed molecular mechanism for such sequence selectivity provided by the compound remains unclear. Therefore,
we performed dynamical fragment molecular orbital (FMO) calculations combining classical molecular dynamics(MD).
Utilizing the ternary complex structure of eIF4A, RNA, and RocA (PDB ID: 5zc9) as an initial structure, we
prepared both the wild-type and four mutant structures (G6U, A7U, G8U, and A9U), and conducted 50 ns MD
simulations for each complex. The MD simulations employed the following force fields: protein: Amberff14SB, RNA:
OL3, RocA: Gaff2. Ten structures were extracted from each MD trajectory at 3 ns intervals, and FMO calculations wereconducted at the MP2/6-31G* level.
The significant interactions of the bases to RocA were unveiled in this analysis. We found that RocA forms π/πinteraction with the base portion of A7 and hydrogen bond, as well as a π/π interaction, with the base portion of G8,
conferring purine sequence specificity on eIF4A. In addition to RNA, amino acid residues on eIF4A also contributed topurine selectivity. Specifically, Phe163 formed a CH/π interaction with RocA. Moreover, Gln195 binds to the compound
through both hydrogen bond and CH/π interaction. Importantly, the interactions of these two residues remain consistentregardless of RNA mutation. However, Asp164, Phe192, and Arg282, although crucial interactions in the wild type,undergo changes due to RNA mutation. Specifically, the following alterations were observed: Asp164 and G8 form ahydrogen bond in the wild type, but this bond is lost in the G8U mutant; Phe192 forms a CH/π interaction with G6 in
the wild type, but this interaction does not occur in the G6U mutant; Arg282 interacted with the phosphate group at
position 6, but the structure was fluctuating
and sometimes interacted with the phosphategroup at position 5 in all mutants. These
results would suggest that Asp164, Phe192,
and Arg282 play an important role inpurine sequence specificity. We are actively
testing whether mutations in these residues
attenuate the RocA-mediated sequence
selectivity. The outcomes of this study will
be reported on that day.
[1] Iwasaki, S.; Floor, S. N.; Ingolia, N.
T. Rocaglates Convert DEAD-Box
Protein EIF4A into a Sequence-Selective
Translational Repressor. Nature 2016,
534 (7608), 558–561.

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O06 分子認識と分子計算 / 創薬応用 Moleuclar recognition and Molecular modeling / Drug Discovery Application

O06-05
Free Energy Analysis of FtsXECL1 Domain Motion by
Divide-and-Conquer MD simulation
Hiromitsu SHIMOYAMA*
Noguchi Institute( * E-mail: simoyama@ccs.tsukuba.ac.jp )In order to develop an antimicrobial drug for streptococcus pneumoniae (the pneumococcus), bacterial cell division
is an attractive target. During growth and propagation, a bacterial cell enlarges and subsequently divides its PG sacculus,
a continuous mesh-like layer, by PG hydrolysis.
The conserved FtsEX complex recently emerged as a major regulator of PG hydrolysis during bacterial cell division.
Though FtsEX structurally resembles an ABC transporter, although several pieces of evidence suggest that FtsEX acts
as a signal transduction system, rather than as a transporter of an unknown substrate. FtsX is an integral membraneprotein with cytoplasmic amino and carboxyl termini, four transmembrane segments, and one large extracellular loop
(ECL1) and one small extracellular loop (ECL2).
In 2019, Britta et al determined the three-dimensional structure of ECL1; the structure reveals a central corecomposed of a four-stranded antiparallel β-sheet (β1,4-6) and two helices (α1,3), an α-helical lobe (residues 107 to135) including the α2 helix, and an extended β-hairpin (β2-3). They reported that the central core adopts well-defined
conformation, on the other hand,α2-β5 linker is highly flexible and the flexible helical-lobe and β-hairpin regions may
contribute to dimerization or to interactions with a binding partner like PcsB. Interestingly, FtsXECL1 with mutation
L115A and M119A was stably fold, however, unable to bind the partner. Their finding suggests that the residues around
L115 and M119 might be a novel target to treat the pneumococcus including wild type FtsXECL1.
In this work, the free-energy landscape (FEL) corresponding to the domain motion of wild-type FtsXECL1and FtsXECL1(L115A M119M) was calculated by using a NMR-data-driven divide-and-conquer MD (D&C-MD)
simulation.

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O06 分子認識と分子計算 / 創薬応用 Moleuclar recognition and Molecular modeling / Drug Discovery Application

O06-06
Transformer Encoder-based Generative Adversarial Network for
Design of Polypharmacological Drugs
Kasumi YASUDA*, 1, Chen LI1, 2, Kazuma KAITOH1, 2, Yoshihiro YAMANISHI1, 2
Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems
Engineering, Kyushu Institute of Technology, 2Graduate School of Informatics, Nagoya University
1( * E-mail: yasuda.kasumi426@mail.kyutech.jp )Cocktail therapy, which utilizes the synergistic effects of combining multiple drugs, is attracting attention as aneffective treatment for multifactorial diseases such as cancer and neurodegenerative disorders. Cocktail therapy is atherapy that uses multiple drugs to control different therapeutic target proteins, thereby enhancing therapeutic efficacy.
However, the indiscriminate combination of multiple drugs increases the risk of adverse side effects. Therefore, thedevelopment of a single molecule that acts on multiple therapeutic target proteins simultaneously is expected. Indeed,drugs that act in combination on multiple therapeutic target proteins often provide synergistic therapeutic effects, fewerside effects, and are more cost-effective than cocktail therapies. Protein structure-based approach has been proposed todesign polypharmacological drugs, but it has problems in terms of computational cost and accuracy.
In this study, we propose a deep learning model for the de novo design of polypharmacological drugs. The proposed
method consists of two steps. First, a quantitative structure-activity relationship (QSAR) model is created to predict
the bioactivity value of a therapeutic target protein based on compound structure and bioactivity value information.
Next, a chemical generator was constructed in the framework of transformer encoder-based generative adversarial
network (GAN) with objective functions reflecting the polypharmacology, which is made possible by an extensionof transformer-based objective-reinforced GAN. As a case study, we generated new molecules that would bind two
therapeutic target proteins ADPRA2A and PDE4D for asthma and confirmed the usefulness. The proposed method isapplicable to any combination of therapeutic target proteins, which is expected to be useful for designing molecules thatsimultaneously interact with a variety of therapeutic target proteins.

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O07 データサイエンス / ADME・毒性 Data science / ADMET

O07-01
In silico and in vitro studies for identification of
UDP-glucuronosyltransferase isoforms in acotiamide metabolism
Kazuyoshi YOSHII*, Ryoko TODA
Ethical drug research, Zeria Pharmaceutical Co., Ltd.

( * E-mail: kazuyoshi-yoshii@zeria.co.jp )In the selection of drug candidates, the substrates for cytochrome P450 (CYP) tend to be avoided because many
drug-drug interactions for CYP have been reported. As a result, drugs that are metabolized by non-CYP enzymes as
UDP-glucuronosyltransferase (UGT), carboxylesterase and aldehyde oxidase are increasing.
The ICH M12 guideline recommends that isoform identification tests and inhibition tests for UDPglucuronosyltransferase (UGT) are performed for UGT substrate. Therefore, it is becoming important to judge whether
a candidate is UGT substrate. Although there are UGT isoforms prediction software currently available, it is difficult tounderstand the overall model because the compounds used for training and features may not be opened.
In order to avoid the above points, we constructed a 2-class UGT substrate prediction model using about 150
compounds. This 2-class classification model predicted UGT substrate by f1-score 0.8. This model might be useful for
UGT substrate prediction because the training data is clear even when prediction failed.
Acotiamide, a drug for the treatment of functional dyspepsia was selected as a case study. Since our model classified
acotiamide as UGT substrate, in vitro study for identification of UDP-glucuronosyltransferase isoforms was performed.
Acotiamide was metabolized in microsomes expressing UGT1A7, UGT1A8 and UGT1A9. Inhibitor for UGT1A9strongly inhibited acotiamide glucuronidation in human liver microsomes. These results suggested that acotiamide ismetabolized by UGT1A9 in human liver.
Totally, it was considered that our model can be applied to UGT substrate prediction. In addition, acotiamide isconsidered to be a highly safe drug because it is conjugated by UGT1A9, for which drug-drug interactions have notbeen reported.

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O07 データサイエンス / ADME・毒性 Data science / ADMET

O07-02
Scaffold-Retained Transformer GAN for Molecular Generation with
Chemical Property Optimization
Chen LI*, 1, 2, Yoshihiro YAMANISHI1, 2
Graduate School of Informatics, Nagoya University, Chikusa, Nagoya, 464-8602, Japan,
Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems
Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka, 820-8502, Japan1
2( * E-mail: li.chen.z2@a.mail.nagoya-u.ac.jp )With the rapid development of artificial intelligence, deep generative models, such as GANs, have achieved
significant milestones in drug discovery. In deep generative models, molecules can be represented as simplified
molecular-input line-entry system (SMILES) strings, which provide a way to apply natural language processing
techniques to generate molecules.
Unlike most deep generative models that generate molecules from scratch [1], this study focuses on a scaffoldretained approach to exploring the chemical space and designing molecules with specific core structures. Specifically,
we propose a reverse transformer (i.e., a decoder followed by an encoder) GAN that generates molecules with chemical
property optimization from a given scaffold. A transformer is an ideal tool for generating SMILES strings, whose selfattention mechanism with a global receptive field allows each atom in a SMILES string to access all others. However,two problems exist in the positional encoding of the transformer for scaffold-retained tasks. First, the positional
encoding cannot memorize the location of the attachment point on a scaffold. The chemical properties and structuresof decorations vary for different attachment points. Therefore, the location of the attachment point is critical for
scaffold-retained molecular generation. Second, the number of atoms in a decoration should be arbitrary. However,
the transformer cannot encode strings of unknown length. In this study, we propose segment IDs and offset IDs toredesign the positional encoding of the transformer to solve these two problems. Then, the transformer decoder andencoder act as the generator and discriminator, respectively. The generator is a variant of the transformer decoder used
to make decorations with a given scaffold. The discriminator is a variant of the transformer encoder used to evaluate
the quality of generated molecules. The reinforcement learning algorithm is used to optimize the chemical propertiesof the generated molecules. In addition, we propose an enhanced model, which considers the Wasserstein distance andmini-batch discrimination [2] for the stability of GANs. In the experiments, we evaluated the drug-likeness, solubility,
synthesizability, and bioactivity of the generated molecules, confirming the usefulness of the proposed models on
scaffold-retained molecular generation. Furthermore, ablation studies explored the effects of different hyperparametersand validated the effectiveness of each model component.
[1] C. Li, C. Yamanaka, K. Kaitoh, and Y. Yamanishi. Transformer-based objective-reinforced generative adversarial
network to generate desired molecules, Proc. in the 31th International Joint Conference on Artificial Intelligence(IJCAI), 3884-3890, 2022.
[2] T. Salimans, et al. Improved techniques for training GANs, Proc. in the 30th Conference on Neural Information
Processing Systems (NIPS 2016), 2016.

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O07 データサイエンス / ADME・毒性 Data science / ADMET

O07-03
Computational prediction of target molecules of drug candidate
compounds from cell morphology images
Shinya ISHIHARA*, 1, Michio IWATA1, Hiromu HAYASHI1, Momoko HAMANO1,
Kazumasa SHIMOFURUTA2, Akihiro KITANI2, Naomasa SUITA2, Yoshihiro YAMANISHI1, 3
1
Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems
Engineering, Kyushu Institute of Technology, 2Minase Research Institute, Ono PharmaceuticalCo., Ltd, 3Graduate School of Informatics, Nagoya University( * E-mail: isihara.sinnya153@mail.kyutech.jp )Identifying target molecules of drug candidate compounds is a critical task in drug development. Computational
approaches for predicting target molecules have been proposed to reduce the burden of experimental screening. Most
computational approaches such as quantitative structure activity relationship (QSAR) are based on the chemical
structures of compounds [1]. However, the performance of the structure-based approach depends on the applicability
domains covered by compounds in a learning dataset [2][3].
In this study, we propose a novel machine learning method for predicting target proteins of compounds fromcompound-induced cell morphology images in the framework of residual neural networks (ResNet), a class of
convolutional neural networks (CNNs). First, U2OS cells were plated in quadruplicate in 384- well plates, exposed
to compounds on plates that were incubated for 24 hours, and each well was treated for 48 hours. Then, staining
was performed for each of the eight cell organelles using the six fluorescent dyes, and images were acquired at 20x
magnification and six viewing angles using an automated falling - fluorescence microscope. We applied the proposed
method to analyzing 30,000 compound-induced cell morphology images. It is observed that the proposed morphologybased approach worked better than the structure-based approach when the structures of query compounds were different
from those of training compounds. It implies that cell morphology image information enables high performance
independent of the applicability domains. By employing transfer learning and data augmentation within the CNN
framework, we achieved further improvement of the prediction accuracy. The proposed method is expected to be usefulfor various applications in drug discovery.
[1] Keiser.M.et al, Predicting new molecular targets for known drugs, Nature, 2009, 462(7270):175- 181.
[2] Lapins.M.et al, Evaluation of Gene Expression and Phenotypic Profiling Data as Quantitative Descriptors for
Predicting Drug Targets and Mechanisms of Action, bioRxiv, 2019.
[3] Hofmarcher.M.et al, Accurate Prediction of Biological Assays with High-Throughput Microscopy Images andConvolutional Networks, Journal of Chemical Information and Modeling, 2019, 59(3),1163-1171.

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O07-04
Insight into Federated Learning for
Compound-Protein Interaction Prediction
Takuto KOYAMA*, 1, Hiroaki IWATA1, Shigeyuki MATSUMOTO1, Ryosuke KOJIMA1,
Takao OTSUKA1, Aki HASEGAWA1, Yasushi OKUNO1, 2
1

Graduate School of Medicine, Kyoto University, 2HPC- and AI-driven Drug Development Platform
Division, RIKEN Center for Computational Science( * E-mail: koyama.takuto.82j@st.kyoto-u.ac.jp )Identifying Compound-Protein Interactions (CPIs) is of great importance for drug discovery. Since experimentally
validating CPIs is time-consuming and costly, deep learning (DL) based CPI prediction model is expected to accelerate
the process with low computational cost. Although constructing CPI prediction models with high performance requires
abundant training data, intellectual property and related financial interests remain substantially hindering the compounddata sharing from one institute to each other. Federated Learning (FL) is one of the most powerful solutions in whichdata owners collaboratively train a model without sharing their data with others but with the trained model parameters[1,2]. However, it often happens that a chemical space one institute has is different from others, and the effectiveness of
FL in such a situation remains to be elucidated.
This study evaluated the effectiveness of FL in a situation where each client has compound data from a different
chemical space. We utilized the ChEMBL activity data targeting GPCRs as a CPI dataset and divided it into multiple
clusters using k-means clustering before splitting it into training and test data. We utilized a chemoinformatics library,kMoL [3] for applying FL under the condition that each client trains the CPI data of each cluster. The results showedthat while local models couldn’t accurately predict out-of-domain data, i.e., test data clustered into different chemicalgroups, the FL model had high robustness on the out-of-domain data. The present study demonstrated that Federated
Learning is beneficial for constructing the CPI prediction model with high generalizability, providing a guideline forpromoting FL in real-world drug discovery.
References[1] Chen, S., Xue, D., Chuai, G., Yang, Q., & Liu, Q. (2020). FL-QSAR: a federated learning-based QSAR prototype
for collaborative drug discovery. Bioinformatics, 36(22-23), 5492-5498.
[2] Heyndrickx, W., Mervin, L., Morawietz, T., Sturm, N., Friedrich, L., Zalewski, A., ... & Ceulemans, H. (2022).
MELLODDY: cross pharma federated learning at unprecedented scale unlocks benefits in QSAR withoutcompromising proprietary information.
[3] https://github.com/elix-tech/kmol― 173 ―

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O07-05
Binding Compound Database Screening using Deep Learning:
Strategies for Improved Candidate Enrichment
Thomas AUZARD*, 1, David JIMENEZ BARRERO1, Nazim MEDZHIDOV1,
Naoki TARUI2
Elix, Inc., 2SEEDSUPPLY Inc.

1

( * E-mail: thomas.auzard@elix-inc.com )Screening large compound libraries physically is a time-consuming and expensive process making in-silico
approaches attractive in drug discovery. Advancements in machine learning and potential speed gains continue to prompt
researchers to explore application of this technology to drug discovery. This study presents two proposed deep learningmodels for in-silico binding compound database screening, and shows an examination of parameters impacting binderenrichment efficiency, providing data procurement guidelines. Utilizing a proprietary protein-ligand binding database,
we developed predictive models for expediting virtual screening in large ligand libraries and enrichment of truebinders. Two architectures were developed both leveraging the protein language model ESM2[1]: one using a modified
tiered graph neural network, and another using a featurization of both protein and ligand and a transformer attention
mechanism. Each model was trained 10 times on different splits of the data, and then ensembled together. Performancewas evaluated using a held-out test set of proteins and an extensive proprietary compound library consisting of ~450K
ligands, with only a handful of positive samples per target. A variability in enrichment efficiency motivated us to
explore parameters contributing to the proper enrichment in some examples vs poor enrichment in others, aiming to
set guidelines on how to achieve best results for end users of the developed technology. The enrichment performance
was noted to improve when the training set proteins effectively covered the breadth of the target protein space. This
concept of “coverage”, including aspects such as the balance of positive/negative samples, number of samples, andin particular, target protein similarity to test data, was further investigated. To better understand this sensitivity andpotentially optimize binder enrichment efficiency, further experiments were designed where we could control and vary
the similarity of training data to the target. In conclusion, this study proposes deep learning methods for large compound
library screening as well as a roadmap for users engaged in virtual screening processes, outlining effective data selection
strategies for improving screening performance.

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O07 データサイエンス / ADME・毒性 Data science / ADMET

O07-06
AdaSplit: an adaptive dataset split method
Nathan Robert LUGG*, Thomas PEACOCK, Joel C FORSTER, Ross Henry JOHNSTONE,
Benjamin INGLEDOW, Koki SHIMADA
SyntheticGestalt( * E-mail: n.lugg@syntheticgestalt.com )A fundamental component in machine learning is creating train and evaluation dataset splits, where the performanceof a model is evaluated based on metrics on the validation set. In the context of machine learning on molecular data, it
has been shown that the performance on the validation dataset is rarely reproducible when applied to an unlabeledinference dataset. In practical terms, the failure to reproduce validation performance can result in the misallocation of
resources and in overlooked opportunities due to inaccurate predictions. The challenge of reproducing performance on
unseen data has prompted the use of alternative methods which rely on molecular information to create data splits, suchas the scaffold split. However, this approach has its own drawbacks, and has not been conclusively demonstrated to
improve performance on unseen data.
In this presentation we offer a novel method called AdaSplit (Adaptive Split) which brings the validation andinference datasets closer. The tradeoff is some leakage between train and validation datasets. The high level idea is: weselect a subset of the labeled data which is closest to the inference data under some user defined metric. More concretely,
we represent an entire dataset as either a covariance matrix or density matrix and minimize the distance between
subsets of the training and inference representations using stochastic gradient descent. In Fig. A we show validation andinference dataset distributions: the random split shows a different distribution, while the AdaSplit methods are moreclosely distributed (using the first and second PCA components of Morgan fingerprints). The three AdaSplit methods we
explore are: CORAL; Von Neumann Entropy; and (approximate) Wasserstein distance. In the presentation we also show
a quantitative analysis of these distributions.
To explore the effect of AdaSplit on model performance we use a labeled dataset as a proxy inference dataset. The
proxy inference dataset contains labeled data, so we can measure model performance on this inference dataset directly.
We consider three proxy inference datasets: Therapeutic Target Database (TTD); GoStar; and a private experimental
dataset (Exp), selected from the Enamine HTS Collection. In Fig. B we plot the PRAUC and ROCAUC of models
evaluated on validation (x-axis) and inference (y-axis) datasets. While the random split achieves almost perfectperformance on the validation dataset,performance drops significantly on theinference dataset. On the other hand, using
AdaSplit, the performance on the validation
dataset closely tracks the performance on
the inference dataset. In the presentation wealso present a comparison to the scaffold
split.
We conclude that using AdaSplit,
machine learning researchers can have amore realistic view of how their models
perform on unseen data which, in addition
to the intrinsic benefit, could be used tofurther optimize model performance byfeature or model architecture selection.

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O08 分子ロボティクス Molecular Robotics

O08-01
Experimental Investigation of Cascaded DNA Generation
Reaction as A Signal Amplification Circuit
Ken KOMIYA*, 1, Chizuru NODA1, Masayuki YAMAMURA2
1

Institute for Extra-cutting-edge Science and Technology Avant-garde Research, Japan Agency
for Marine-Earth Science & Technology, 2School of Computing, Tokyo Institute of Technology( * E-mail: komiyak@jamstec.go.jp )We had previously proposed simple cascading of DNA generation reaction to produce single-stranded DNA (ssDNA)
molecules at a physiological temperature in response to nucleic acid inputs [1]. SsDNA can function as a transmitter of
information and energy between DNA-based sensors, processors, and actuators [2]. Toward construction of functional
molecular robotic systems, we experimentally investigated the amplification performance and the input interface of
cascaded DNA generation reaction in this study. In contrast to the exponential ssDNA amplification reaction [3], thepresent reaction appeared to avoid undesired non-specific amplification events independent on the use of artificial
nucleic acids, and thus, effective when introduced into a tiny reaction chamber. In addition to biological nucleic acids,
UV irradiation can also be used for input of ssDNA generation reaction [4]. The optimal interface design for accepting
various types of input will be discussed.
[1] K omiya, K.; Yamamura, M. Cascading DNA generation reaction for controlling DNA nanomachines at aphysiological temperature, New Generation Computing , 2015, 33, 213-229.
[2] Hagiya, M.; Konagaya, A.; Kobayashi, S.; Saito, H.; Murata, S. Molecular robots with sensors and intelligence,
Accounts of Chemical Research , 2014, 47, 1681-1690.
[3] Komiya, K.; Komori, M.; Noda, C.; Kobayashi, S.; Yoshimura T.; Yamamura, M. Leak-free million-fold DNA
amplification with locked nucleic acid and targeted hybridization in one pot, Organic and Biomolecular Chemistry ,2019, 17, 5708-5713.
[4] S ato, Y.; Komiya, K.; Kawamata, I.; Murata S.; Nomura, S. M. Isothermal amplification of specific DNA molecules
inside giant unilamellar vesicles, Chemical Communications , 2019, 55, 9084-9087.

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O08 分子ロボティクス Molecular Robotics

O08-02Eﬃcient DNA-based structure automated exploration withmachine-learning models
Aoi TAKEGUCHI*, Aubelt-Kato NATHANAEL
Department of Advanced Sciences, Ochanomizu University( * E-mail: takedrago@gmail.com )Structures composed of DNA molecules are bound to attract attention as materials for molecular robots. Cazenille etal. developed a tool based on Quality-Diversity (QD), a type of black-box optimization algorithm, that explores thespace of theoretical assembly paths and outputs them on a grid, where the axes correspond to characteristics ("features")of the respective assembly paths [1]. The algorithm relies on computationally expensive simulations to search forstructures with specific physical properties, such as low potential energy.
In this study, an exploration algorithm with built-in machine learning is developed to search for the structures withlow potential energy efficiently, taking inspiration from another QD algorithm named DA-QD [2], as follows: first, wetrain our machine learning model on the structures found by Cazenille et al.'s approach and simulation results from
oxDNA [3]. Our model predicts the average potential energy of structures formed from a given set of DNA strands.
Next, we replace the evaluation function in Cazenille et al.'s algorithm with our model, thus skipping the simulation
step. At the same time, the reliability of the prediction result is calculated. After a run of the algorithm, sets of strands
with an expected accuracy below a threshold are forwarded to oxDNA for validation. Data generated by this validation
step are then added to the training set, which is then used to re-train the model. The updated model is then fed back to
the QD algorithm and the optimization is restarted from the last sets of strands from the previous iteration.
In this study, our model is an ensemble of 100 SVMs trained using the bootstrap method. The returned potential
energy is the average of their predictions. This method not only eliminates bias in the data set to some extent but also
allows the standard deviation to be calculated at the same time as the reliability of the prediction. Figure 1 shows a flowof the process performed in one loop of the algorithm.
When the top 30% structures with the lowest
reliability were given to oxDNA, and the Calculation
steps per structure were set to 100000 times, it took 9.02
seconds per structure, which is less than 1% of the timerequired by oxDNA to perform all calculations. Thus,
we demonstrated the potential of machine learningmodeling to increase the efficiency in searching for DNA
structures. Challenges for the future are quantifying
the algorithms' efficiency and considering how to takeadvantage of the properties of the structures obtained.
[1] Cazenille, L., A. Baccouche, and N. Aubert-Kato.
“Automated exploration of DNA-based structureself-assembly networks.” Royal Society OpenScience 8.10 (2021): 210848.
[2] Lim, Bryan, et al. “Dynamics-aware qualitydiversity for efficient learning of skill repertoires.”2022 International Conference on Robotics andAutomation (ICRA). IEEE, 2022.
[3] P. Šulc, et al. 137, 135101 (2012)― 177 ―

O08 分子ロボティクス Molecular Robotics

O08-03
Lipid Based Artificial Multicellular Systems for Compartmentalized
and Stimuli-Responsive Drug Delivery
Shin-ichiro M. NOMURA*, 1, Tsuyoshi INABA1, Richard James ARCHER1,
Hamada SHOGO2, Hideaki T. MATSUBAYASHI3, Ibuki KAWAMATA1, Satoshi MURATA1
Department of Robotics, Graduate school of Engineering, Tohoku University, 2School ofComputing , Tokyo Institute of Technology, 3FRIS, Tohoku University

1( * E-mail: shinichiro.nomura.b5@tohoku.ac.jp )Smart drug release systems have gained attention as a method of using existing medicines in a more effective
manner. However, topically applied medicines still rely on uncontrolled diffusion for delivery. Here we propose theuse of patches with compartmental interiors to control the release of encapsulated drugs. We have reported a facile andscalable method to create multicellular-like, multi-compartment (MC) structures. Aqueous droplet-based compartments
ranging from 50 to 400 μm were stabilized and connected together by hydrophobic layers composed of phospholipids
and an emulsifier. Planar centimeter-scale MC structures were formed by droplet deposition on a water interface.
Further, the resulting macroscopic shapes were shown to be achieved by spatially controlled deposition. These noveland functionalized structures exemplify a route toward future applications including compartmentally assembled“multicellular” molecular robots. Then we demonstrate the facile self-assembly of MC into tight-packed centimeterscale multicellular systems within a portable polysaccharide matrix. The compartmentalization allows for the storage ofmultiple separated drugs, with multiple release conditions leading to multi-step or sustained delivery outputs. This work
could lead to medical patches for smart and customizable drug delivery.

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O08 分子ロボティクス Molecular Robotics

O08-04
3D Point Cloud Analysis of Microtubule Motility Dynamics
Chen MA*, 1, 3, Masayuki YAMAMURA1, Akira KAKUGO2, 3, Gutmann Spence GREGORY3,
Akihiko KONAGAYA3, 4, 5
1School of Computing, Tokyo Institute of Technology, 2Division of Physics and Astronomy
Graduate School of Science, Kyoto University, 3Molecular Robotics Research Institute, Co., Ltd.,
4
Keisen University, 5Advanced Science ELSI Research Laboratory, CBI Research Institute( * E-mail: ma.c.ab@m.titech.ac.jp )Microtubules often form streams at high density, which glide in the same directions on Kinesin coated glass surface
in microtubule motility assay [1]. At higher density of microtubules, they tend to move together (snuggling) to avoid
collision and overriding of microtubules. Sometimes they go out or in focus to make suddenly appear and disappear. As
a result, microtubule groups emerge motion patterns showing straight, curved or wave like trajectories.
In order to analyze the microtubule group motion patterns, we applied 3D point cloud analysis and visualization.
At first, we assumed that “the higher, the brighter” to convert the signal intensity to height map, obtained by amicrotubule gliding assay device. Then we made point clouds of microtubules in 3D. According to the 3D point cloudview, it appeared that the random movement are more likely in lower layer and stream like movement are in higher
layer. The Earth Mover's Distance (EMD) method [2] for 3D point cloud analysis enabled us to observe the motion
patterns by solving the optimal transport problem of point clouds between each frame. In other to improve the matching
performance, we also applied the Localized Sinkhorn Distance (LSD) method [3] which optimized the corresponding
entropy regularization term of transport problem in the EMD method.
[1] Mousumi Akter, Akira Kakugo, et al: Cooperative cargo transportation by a swarm of molecular machines, Science
Robotics. April 20, 2022.
[2] Yossi Rubner, Carlo Tomasi, Leonidas J. Guibas: The Earth Mover’s Distance as a Metric for Image Retrieval,International Journal of Computer Vision, 40(2), 99–121, 2000.
[3] MarcoCuturi: Sinkhorn Distances: Lightspeed Computation of Optimal Transport, NIPS 2013.

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O08 分子ロボティクス Molecular Robotics

O08-05High-Resolution AFM Imaging of DNA Structures: An Approach
via Cycle GANs and Virtual Reality Integration
Xiaoran HU*, 1, 3, Qing LIU2, Gregory GUTMANN3, Masayuki YAMAMURA1,
Akinori KUZUYA2, Akihiko KONAGAYA3, 4, 5Tokyo Institute of Technology, 2Kansai University, 3Molecular Robotics Research Institute, Co.,
Ltd, 4Keisen University, 5CBI Research Institute1( * E-mail: hu.x.ab@m.titech.ac.jp )Advances in nanoscale imaging have revolutionized our understanding of living and non-living materials. Amongthese techniques, atomic force microscopy (AFM) stands out as a powerful tool for visualizing surface structures. Its
unique ability to analyze samples in physiological solutions and environmental conditions makes it indispensable instudies involving DNA molecular dynamics and structure. However, the pursuit of high-resolution AFM images has
been hampered by the high cost of equipment and the need for specialized knowledge. As a result, researchers often facechallenges when acquiring super-resolution images, resulting in the loss of critical information about DNA structure,including major and minor grooves. To address these limitations, this paper presents a novel method that leverages thesynergy of multiple cycle generative adversarial networks (Cycle GAN) [1] and virtual reality (VR) techniques [2] tocreate super-resolution AFM images of dsDNA. The basis of the method is the integration of real low-resolution AFM
images and simulated AFM images generated in a virtual environment. The VR system generates simulated AFM DNA
images, replicating the complexity of AFM imaging. The first Cycle GAN bridges the gap between real and simulated
images by converting blurred AFM DNA images into a simulated AFM image space. Based on the translated images,
a second Cycle GAN utilizes paired simulated AFM image datasets to generate super-resolution images. By utilizing
information extracted from simulated images, the second GAN effectively enhances the resolution of low-resolution
inputs. Finally, a fine-tuning process refines the transformation and mapping in an end-to-end manner, optimizing theaccuracy and consistency of the resulting super-resolution AFM images. The resulting super-resolution AFM DNA
images demonstrate submolecular structures, including complex major and minor grooves, mirroring features observedin real AFM images. This method gives us a great advantage of precise DNA strand manipulation when developing a
micro-scale manipulator on AFM.

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O08 分子ロボティクス Molecular Robotics

O08-06
DNA reaction–diffusion model with polymerization
for pattern formation
Keita ABE*, 1, Satoshi MURATA1, Ibuki KAWAMATA1, 2
Graduate School of Engineering, Tohoku University, 2Faculty of Core Research, Ochanomizu
University

1( * E-mail: keita.abe.e1@tohoku.ac.jp )Synthetic DNA is known as a programmable material due to the predictability of its sequence-based interactions. In
the field of DNA computing, design methods for various temporal dynamics have been proposed and applied to program
spatiotemporal dynamics. However, such designs have mainly focused on the programming of advanced reaction
systems rather than diffusion.
Here, focusing on the effect of molecular size on the diffusion coefficient, we propose a method to program bothreaction and diffusion for building a DNA-based reaction–diffusion system. The pairs of DNAs are designed to hybridize
each other alternately to form a DNA polymer. When the polymerization proceeds in the hydrogel medium, the diffusion
stops on and the polymer is immobilized there. Based on the mechanism, we constructed a reaction–diffusion system inwhich the DNAs diffuse from the left and right reservoirs prepared in hydrogel medium, reacts on the bisector between
them, and are immobilized there (bisector pattern). Although this system is based on reaction–diffusion mechanism, it
is difficult to describe the dynamics of all chemical species with partial differential equations because of the unlimited
number of intermediates assumed due to the polymerization.
Thus, we have classified the intermediates referring to their reactivity and the number of DNAs they consistof to decide reaction equations and assign diffusion coefficients. We also limited the size of the polymer based on
experimental results and described partial differential equations. Simulations were performed under 27 conditions for thereference diffusion coefficient and reaction rates, and the results were compared with the experimental results to fit thevariables. As a result, not only bisector pattern formation involving polymerization but also the advanced superpositionand cascaded pattern formation were reproduced well.
This model has a potential to utilize various polymerization reactions and similar reactions. In DNA nanotechnology
which we can utilize polymerization and other chain reactions such as HCR, DNA hydrogel, or DNA droplet, artificial
reaction–diffusion systems will be a more powerful tool to realize intelligent hydrogels that incorporates sensing andchanging physical properties with spatial gradients.

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P01 分子認識と分子計算 Moleuclar recognition and Molecular modelingP01-01
Design of scalable perturbation maps for relative free energy
calculations
Kairi FURUI*, Masahito OHUE
Tokyo Institute of Technology( * E-mail: furui@li.c.titech.ac.jp )Free energy perturbation calculations are a powerful tool to support the lead optimization process in drug discovery.
To enhance the accuracy and statistical reliability of free energy perturbation methods, it is essential to properly
construct a compound network known as a perturbation map.
In recent years, with the advancement in computational capabilities, attempts have been made at lead optimization
using free energy perturbation methods on a relatively large scale, involving hundreds of cases.
However, constructing perturbation maps of such a scale has been challenging. Lomap is the most commonly usedmethod for generating perturbation maps, and it operates by sequentially eliminating unnecessary edges based on
constraints.
However, due to the need to evaluate all edges, Lomap struggles with execution on large-scale graphs.
In this study, we propose a faster method called FastLomap to enable the construction of large-scale perturbation
maps.
This allowed us to obtain the same output as Lomap but more than 10 times faster in the case of large perturbation
maps and hundreds of times faster in the case of a especially large number of edges.
Additionally, we compare the perturbation map generation methods of FastLomap and the Fedorov exchange
method, and analyze the optimality of perturbation maps in large-scale scenarios.

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P01 分子認識と分子計算 Moleuclar recognition and Molecular modelingP01-02In silico screening of protein-protein interaction inhibitors
using oligo (N -methylalanine) as a scaffold
Marin YOKOMINE*, 1, Daisuke KURODA2, Takumi UEDA3, Koh TAKEUCHI3,
Kohei TSUMOTO1, 4, 5, Jumpei MORIMOTO1, Shinsuke SANDO1, 5
1
Department of Chemistry and Biotechnology, Graduate School of Engineering, The Universityof Tokyo, 2Research Center for Drug and Vaccine Development, National Institute of Infectious
Diseases, 3Graduate School of Pharmaceutical Sciences, The University of Tokyo, 4Institute ofMedical Science, The University of Tokyo, 5Department of Bioengineering, Graduate School ofEngineering, The University of Tokyo( * E-mail: marine-y13@g.ecc.u-tokyo.ac.jp )Protein–protein interactions (PPIs) are involved in many biological processes and attractive targets in drug discovery.
Since the PPI surface is generally broad and flat, targeting PPIs by small molecules is difficult. Middle sized moleculesthat can recognize the wide PPI interface, such as peptides, are attracting attention as drug modalities for targeting
PPIs. If in silico screening of peptidic molecules can be conducted like small molecules, PPI inhibitors can be obtained
rapidly. However, practical in silico screening of peptidic molecules has not been realized because of the difficulty inpredicting the 3D structure adopted by a peptidic molecule upon binding to a protein.1 The difficulty in predicting the 3D
structure is derived from the fact that typical peptidic molecules are composed of conformationally flexible monomers
and thus the number of possible conformations of a single compound is enormous.
In this presentation, we report in silico screening of peptidic molecules composed of conformationally restricted
monomers. We focused on oligo(N -methylalanine) (NMA), which are highly conformationally restricted at themonomer level and whose 3D structures can be easily predicted. Previously, oligo-NMA scaffolds have been shownto maintain the predicted 3D structures even when functional groups are introduced on the amide nitrogen.2 Here, wefirst show that oligo-NMA scaffolds maintain the predicted 3D structure even when functional groups are introducedon the α-carbons by conformational analysis using computational methods based on density functional theory (DFT)studies and molecular dynamics simulations, and NMR. Next, we performed in silico screening of compounds thathave functional groups on the amide nitrogens and the α-carbons of oligo-NMA scaffolds to obtain MDM2–p53 PPI
inhibitors based on the 3D structure of oligomers predicted from NMA monomers.3
1. M. Ciemny, et al ., Drug Discov. Today 2018, 23 , 1530–1537.
2. J. Morimoto, et al ., J. Am. Chem. Soc . 2019, 141 , 14612–14623.
3. M. Yokomine, et al ., Angew. Chem., Int. Ed . 2022, 61 , e202200119.

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P01 分子認識と分子計算 Moleuclar recognition and Molecular modelingP01-03
Interaction analysis of Keap1 and the inhibitors using Fragment
Molecular Orbital Method
Mariko IBARA*, Norihito KAWASHITA
Faculty of Science and Engineering, Kindai University( * E-mail: 2010330011w@kindai.ac.jp )Keywords: Fragment Molecular Orbital Method, Keap1, Nrf2, protein-protein interaciton
The Keap1-Nrf2 pathway plays a central role in the biological defense mechanism against oxidative stress. Amongthese, the transcription factor Nrf2 is involved in the activation of a group of genes related to various biological defense
mechanisms. In addition, Keap1 is a regulatory factor that promotes the degradation of Nrf2 under non-stress conditions.
Therefore, this pathway is known to be deeply involved in the cause of various diseases. Oxidative stress caused byreactive oxygen species is considered a contributing factor to the development of cancer and lifestyle-related diseases,
as it leads to damage to DNA, proteins, and lipids. In this study, we used the fragment molecular orbital (FMO) method [1]to analyze the complexes of Keap1 with the inhibitors to provide crucial information for the design of Keap1 inhibitors.
Twenty-four crystal structures of complexes of Keap1 with the inhibitors were downloaded from Protein Data Bank
(PDB) [2-5]. Furthermore, the major structures were used as a template, complexes with 44 inhibitors of references 2-5
were constructed by docking simulation using MOE. Structural modifications and optimizations were performed using
MOE. FMO calculations were carried out using the supercomputer Fugaku to calculate inter-fragment interaction energy(IFIE) and pair interaction energy decomposition analysis (PIEDA) [6] between protein-ligand complexes. ABINIT-MP
was used as the FMO calculation, and MP2/6-31G* was used as the calculation level.
The results of the FMO calculations showed correlations between experimental values and IFIE with R2 values
greater than 0.50 in references 2, 3, and 5. In particular, a strong correlation was observed in reference 2 with an
R2 value of 0.76, and in reference 3 with R2 value of 0.65. Analysis using PIEDA for references 2 and 3, revealed
interactions involving hydrogen bonds with residues Ser508, Glu530, and Ser555, π-π stacking with Tyr525, andelectrostatic interactions with Arg483 as important residues. Therefore, retention of these interactions may be important
for the design of more potent inhibitors.
This research was done in activities of the FMO drug design consortium (FMODD). For FMO calculations, RIKEN
R-CCS Fugaku was used (hp220143 quota).
[1] K. Kitaura, et al., Chem. Phys. Lett., 2000, 318(6), 614-618.
[2] G. D. Thomas, et al. J. Med. Chem., 2019, 62(9), 4683-4702.
[3] J.-A. W. Alison, et al. J. Med. Chem., 2021, 64(21), 15949-15972.
[4] K. Jan, et al. J. Med. Chem., 2022, 65(4), 3473-3517.
[5] B. Anders, et al. J. Med. Chem., 2021, 64(8), 4623-4661.
[6] K. Kitaura, et al., J. Comput. Chem., 2007, 28, 475-480.

― 184 ―

P01 分子認識と分子計算 Moleuclar recognition and Molecular modelingP01-04
FEP-suite: free-energy calculation pipelines for biomolecules
Shun SAKURABA*, 1, 2
1Institute for Quantum Life Science, National Institutes for Quantum Science, 2Graduate School of
Science and Engineering, Chiba University( * E-mail: sakuraba.shun@qst.go.jp )In molecular simulations, the free-energy difference between states can be calculated by carefully prepared inputs
and protocols, which is called free-energy caclulation. As the free-energy differences correspond to various equilibrium
constants - ligand-receptor binding free-energy corresponds to the dissociation constant, and free-energy change change
estimation upon introducing a mutation into a protein corresponds to the stability - free-energy calculations are useful
in molecular design. However, these free-energy calculation tasks are tedious and complex which hinders us from adaily use of the protocol. In this poster I present an open-source free-energy calculation pipeline called FEP-suite,which includes (1) free-energy perturbation with absolute binding free-energy (FEP-ABFE) for ligand-receptor binding,and (2) free-energy perturbation enhanced by replica exchange solute tempering (FEP/REST) for relative free-energychange calculation. The pipeline is built upon molecular dynamics simulation package GROMACS with some patches.
The dependency existing in the pipeline is tightly integrated with various batch job systems, including SLURM, PBS
Pro and Fujitsu PJ manager. The design decisions behind the pipeline will be discussed and the benchmarks will bepresented. The software is released on Github (https://github.com/shunsakuraba/fepsuite).

― 185 ―

P01 分子認識と分子計算 Moleuclar recognition and Molecular modelingP01-05Construction of a system for predicting the activity of
low-molecular-weight compounds using FMO calculations
Sota TANAKA*, 1, Yuma HANDA2, Yushi TIAN1, Daisuke TAKAYA1, Kaori FUKUZAWA1Quantum life science and bioinformatics, School of Pharmaceutical Sciences, Osaka University,
2
Hoshi University

1( * E-mail: tanaka-sota@phs.osaka-u.ac.jp )BackgroundIn drug discovery research, in silico screening is widely used to identify new lead candidates. Prediction of thebinding activity of candidates using a scoring function based on protein-ligand complex structures has limited accuracy,
and its improvement is still a challenging task.
We utilized the ab initio Fragment Molecular Orbital (FMO) calculation to estimate Inter-Fragment Interaction
Energies (IFIE) and Pair Interaction Energy Decomposition Analysis (PIEDA) energies between ligand molecules andproteins. Moreover, In this study, we will build a system to automate a series of procedure related to in silico screening,such as preprocessing of the protein structures, prediction and estimation of the protein-ligand complex structures, FMO
calculation, and correlation between the interaction energies and the ligand binding activity.
Methods
A dataset of protein-ligand complexe structures of epidermal growth factor receptor (EGFR) and their inhibitory
activities in DUD-E [1] was selected for preliminary validation. Preprocess of the structures and the molecular docking
were performed by MOE. FMO calculations (MP2/6-31G* ) were performed by ABINIT-MP in supercomputer Fugaku.
Finally, a correlation analysis was conducted between the calculated energies and the inhibitory activities.
Results and Discussions
We have developed a system that can automate the in silico screening process (Fig. 1). To validate this automatedsystem, we calculated the predicted binding affinity for the EGFR dataset and analyzed the results. The correlation
between IFIE/PIEDA and the inhibitory activities was low for prediction accuracy. These results may be raised from theinnaccurate docking structures and the excessive influence of the ligand charge on the IFIE/PIEDA value.
Further studies are under consideration to investigate whether these issues can be resolved by conducting FMO
calculations in a solvent environment or refining docking calculations using pharmacophore models to preserve
important interactions. By continuing to expand the automated system, we plan to perform an exhaustive performance
test of DUD-E on all targets using the developed system.
[1] Mysinger MM, Carchia M, Irwin JJ, Shoichet BK J. Med. Chem., 2012, 55, 14, 6582–6594.

― 186 ―

P01 分子認識と分子計算 Moleuclar recognition and Molecular modelingP01-06
Evaluation of MD-based high-throughput screening methods
using supercomputer Fugaku
Tomoya NABETANI*, 1, Toru EKIMOTO1, 2, Tsutomu YAMANE3, Mitsunori IKEGUCHI1, 2, 3
Department of Science, Yokohama City University, 2Graduate School of Medical Life Science,
Yokohama City University, 3Center for Computational Science, RIKEN

1( * E-mail: s202076a@yokohama-cu.ac.jp )In the field of in silico screening, candidate compounds are often selected on the basis of docking simulations totheir static structure by ranking them according to empirical and physicochemical scores such as the docking score
and the MM-GB(PB)SA binding free energy. However, the hit rate of these scores is not usually high enough (roughly~1%). Since proteins are dynamic, docking simulations to static structures are not always accurate in terms of binding
poses, and ligand flexibility is also important for binding free energies. In previous studies, molecular dynamics(MD) simulations have been used along with docking simulations to assess the stability of docked poses (Liu, K. etal., J. Comput. Aided Mol.Des. 2017 ). However, the use of MD for in silico screening has been limited due to thecomputational cost of incorporating MD simulations into high-throughput screening methods. Recently, with theavailability of large supercomputers such as Fugaku, in silico screening using MD is becoming more realistic.
In this study, we evaluated the effectiveness of MD in in silico screening by performing thousands of MD
simulations of ligand-protein complexes using the Fugaku supercomputer. In our previous study of in silico screeningof the protein UHRF1 inhibitors, 130 compounds were experimented, and two hit compounds were found (Kori, S. etal., Bioorg. Med. Chem. 2021). In addition, another group also reported different ligands that bind to the same pocket of
UHRF1 (Chang,L.et al., Sci. Rep.2021). In this study, we used a data set containing 3 active ligands out of 600 ligands
after considering structural isomers and tautomers of ligands. In addition to UHRF1, tyrosine kinase (PDB ID: 2OF2)
was used for the evaluation. Active and decoy ligands of the tyrosine kinase were extracted from the DUD-E database,and a data set containing 10 active ligands out of 1000 ligands was created (Mysinger MM, et al., J.Med.Chem.2012).
Compounds in each dataset were then ranked by MM-GBSA binding free energy or docking score alone or by filtering
compounds by docking pose stability with short MDs, and then by MM-GBSA binding free energy or docking score.
Finally, enrichment factors were compared when compounds were ranked by MM-GBSA binding free energy or
docking score alone, and when compounds were ranked by MM-GBSA binding free energy or docking score after
filtering compounds by docking pose stability at short MDs.
We found that filtering compounds by docking pose stability with short MDs and then ranking them by conventionalscores (MM-GBSA binding free energy or docking score) improved the enrichment factor compared to ranking them by
conventional scores alone. These results demonstrate the effectiveness of using MD simulations to assess the structural
stability of docking poses in in silico screening.

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P01 分子認識と分子計算 Moleuclar recognition and Molecular modelingP01-07
Binding Pathway of Hydroxycarboxylic acid receptor 2 (HCAR2)― Niacin Explored by Tree-Search Molecular Dynamics (TS-MD)
Yukina NAKAI*, 1, Toru EKIMOTO1, Tsutomu YAMANE2, Kei TERAYAMA1,Sam-Yong PARK1, Mitsunori IKEGUCHI1, 2
1

Dept. of Med. Life Sci., Yokohama City Univ., 2R-CCS, Riken( * E-mail: w225420g@yokohama-cu.ac.jp )Understanding the binding process of ligands to proteins plays an essential role in the elucidation of diseasemechanisms, and design of drug discovery. Recently, drug design that considers the ligand-protein binding process has
been attracting attention in the field of computational drug discovery. Therefore, simulating the binding of a ligand to atarget protein from a distant position by using molecular dynamics (MD) simulations is promising from a drug discovery
perspective. However, the timescale for ligand binding exceeds the usual range of conventional MD. To overcome
the problem, Tree-Search Molecular Dynamics (TS-MD) [1], a pathway-search technique that is a combination ofreinforcement learning and MD simulation, was applied to the ligand binding in this study. TS-MD easily escapes fromlocal stable structures, because the pathway search is conducted, looking back at the past structures. In this study, weapplied the method to a system of G-Protein Coupled Receptor (GPCR), Hydroxycarboxylic acid receptor 2 (HCAR2),and its agonist, Niacin.
First, the appropriate hyperparameters and the evaluation function of TS-MD were examined. Under the conditions
determined there, TS-MD simulation to explore the ligand binding pathway (starting from the ligand dissociated state,referred to as “TS-MD for binding”) and TS-MD simulation to explore the ligand dissociation pathway (starting fromthe ligand bounded state, referred to as “TS-MD for unbinding”) were performed 10 times respectively. In the pathway
obtained by TS-MD for unbinding, the ligand hardly moved from the binding site in all 10 out of 10 trials. In thepathway obtained by TS-MD for binding, the ligand reached the vicinity of ECL2, but did not reach the binding sitein all 10 out of 10 trials. We hypothesized that these results suggest that ECL2 of HCAR2 plays a gate-like role at theligand binding site. Therefore, to explore the ligand binding pathways, we examined the movement of ECL2 in the
apo state by performing conformational sampling (100 μs in total) of HCAR2 in the apo state using the supercomputer"Fugaku". The generated trajectories were analyzed by Principal Component Analysis (PCA), and an ECL2 open
structure was picked up from there. From the ECL2 open structure, we successfully obtained the pathway of the ligand
binding process of HCAR2 in all 9 out of 10 trials (7/10 of the trials reached less than 1.0 Å, 2/10 of the trials reached 1.0– 2.0 Å, and 1/10 of the trials reached ~3.5 Å of ligand RMSD).
This work used computational resources of Fugaku provided by RIKEN through the HPCI System Research Project(Project ID: hp220236(T.E.)).
[1] Shin K et al. Enhancing Biomolecular Sampling with Reinforcement Learning: A Tree Search Molecular Dynamics
Simulation Method. ACS Omega. 2019. 4(9), 13853-13862.

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P01 分子認識と分子計算 Moleuclar recognition and Molecular modelingP01-08
Exploring Cryptic Binding Sites through Noble Gas Interactions
Shinji IIDA*
School of Frontier Engineering, Kitasato University( * E-mail: iida.shinji@kitasato-u.ac.jp )Molecular binding sites of a protein tell us how to target the protein from their structural information, which is
required for structure-based drug design. However, these binding sites are not always readily apparent in the absence ofligands, and are refereed to as cryptic binding sites.
Cryptic binding sites are widespread in the human proteome, with approximately 70% of the proteome containing
such sites that are associated with human diseases. This result highlights the importance of studying cryptic binding sites
in the field of structure-based drug design. Nonetheless, identifying such sites from protein structures remains extremely
challenging, and no standard method has been proposed for this purpose.
In this study, I present preliminary results of a novel strategy to identify cryptic binding sites in proteins from their
apo states. The main idea is based on the observation of X-ray protein structures with noble gases, particularly xenon,
which have been found to bind to sites that are typically occupied by ligands. Building on this observation, I hypothesisethat noble gases may bind to cryptic binding sites. To test this hypothesis, I performed atomistic molecular dynamics
simulations of apo-state proteins with known cryptic binding sites in the presence of noble gas atoms. The simulations
have revealed that noble gas atoms tended to occupy cryptic binding sites, implying that noble gas atoms can serve as aprobe to explore these elusive sites.

― 189 ―

P01 分子認識と分子計算 Moleuclar recognition and Molecular modelingP01-09Practical visualization of interaction energies by FMO for
drug design II: Updates and improvements
Hirofumi WATANABE*, Yuji TANAKAMARU
WithMetis Co., Ltd.

( * E-mail: h.watanabe314@gmail.com )The fragment molecular orbital (FMO) method provides computational evaluations of protein-ligand interactions,such as the IFIE and the PIEDA, based on quantum mechanics. We have been developing Q-AIR FLAIR Viewer andconsistently enhancing its utility for drug design in pharmaceutical research. At the CBI society annual meeting lastyear, we reported features of the viewer, for example, a diagrammatic depiction of the IFIE and the PIEDA between aligand and surrounding amino acid residues where we tuned positions of amino residues for easy to compare among
multiple protein-ligand complexes. This year, we will provide updates and improvements to the viewer. We improved
the viewer as responses to suggestions by the audiences. For example, we have been developing another tuning of
2D-diagrammatic depiction keeping information on 3-dimensional positions and distance of amino acid residues
surrounding the ligand. In addition, we would like to discuss useful visualization of characteristic interactions such asπ-π interactions, CH-π interactions, hydrogen bonds. Moreover, we would also like to discuss useful ways for comparing
different scaffolds and different substituent modifications.

― 190 ―

P01 分子認識と分子計算 Moleuclar recognition and Molecular modelingP01-10Applicabilities of Neural Network Potential to Predict
ligand-RNA Interactions
Hiroto ASANO*, 1, Hiromu MATSUMOTO2, Shu OHNO1, Yuma HANDA1, 3,
Asuka HATABU1, Daisuke TAKAYA1, Kenji IKEDA1, Yu-Shi TIAN1, Kaori FUKUZAWA1,
Kato KOICHIRO2
Graduate School of Pharmaceutical Sciences, Osaka University, 2Department of Applied
Chemistry, Graduate School of Engineering, Kyushu University, 3School of Pharmacy andPharmaceutical Sciences, Hoshi University
1( * E-mail: u677293c@ecs.osaka-u.ac.jp )Background
Dynamic FMO calculations that combine classical MD simulation and ab initio FMO calculation have been
applied to multiple systems and achieved fruity results, including good correlations between experimental activities ofligands and calculated ligand-receptor interaction energies and explanations of protein-protein interactions. However,FMO calculation requires large computational resources. To overcome this shortcoming, the complemental methods
proposed are creating an FMO force field and combining dynamic FMO and machine learning. Our group is involvedin such research and has successfully constructed a neural network model to predict atomic charges calculated by
FMO from the atomic arrangement and atomic species calculated by MD for proteins. In addition, we are developing
neural network potential (NNP), which predicts energy and force for protein systems using FMO data. Besides ligandprotein interactions, ligand-RNA interactions have also been highlighted, and antisense oligonucleotides, siRNA, andmiRNA targeting on RNAs are widely studied as novel drug candidates. Therefore, precision analyses of ligand-RNAinteractions are also considered important. This study aimed to reconstruct our NNP model to make it feasible fordynamic FMO calculations of ligand-RNA complexes.
Methods
To predict the interaction force and energies between ligands and RNA, we constructed an NNP model based on ourpreviously reported model using Python and Chainer (ver. 7.8.1). This model was pretested using our calculated ligandRNA interaction system.
Results and Discussion
We are in the process of applying NNP to proteins and have found it to be highly accurate in predicting energy andforce. Its adaptability to a wide range of atomic species and handling of structural files with varying atomic counts
underscored its applicability to diverse protein systems. In this study, we established the model's adaptability to nucleic
acids containing phosphorus, confirming its extendibility. We then focused on enhancing the efficiency of model
construction by expanding input formats. Previously, dealing with structural files of differing atomic counts required
segmenting and storing files separately or splitting input files by categories like force or energy. To address this, wedeveloped a program to automatically generate input data from the computational log files of FMO calculations. This
improvement streamlined data preparation for training model, allowing for direct learning from computational files
and augmenting model construction efficiency. This development has the potential to facilitate rapid model creation forvarious nucleic acids in the future. Besides assessing nucleic acid applicability, improving model construction efficiency,
and advancing nucleic acid computational accuracy, we commit to collecting actual nucleic acid data and ongoingmodel development. We intend to report our progress on these endeavors during the upcoming presentation.

― 191 ―

P01 分子認識と分子計算 Moleuclar recognition and Molecular modelingP01-11
Computational Interaction analysis between Interleukin-10 andInterleukin-10 receptor Using the Fragment Molecular Orbital Method
Kenta KAMIMURA*, 1, Yuma HANDA1, 2, Takayuki FURUISHI1, Etuto YONEMOTI1,
Yu-shi TIAN2, Daisuke TAKAYA2, Kaori FUKUZAWA1, 2, Toma MIYAGISHI2,
Midori TAKIMOTO-KAMIMURA3, Mayumi ENDO4
1

School of Pharmacy and Pharmaceutical Sciences, Hoshi University, 2School of Pharmaceutical
Sciences, Osaka University, 3 CBI research institute, 4PRISM BioLab( * E-mail: s191507@hoshi.ac.jp )IL-10 is one of the important immunomodulatory cytokines that suppress inflammatory immune responses. The
multifaceted nature of IL-10 is expected to provide a new perspective on the therapeutic effects of IL-10 against
autoimmune diseases that cause excessive inflammatory responses. The aim of this study was to elucidate the detailsof the molecular interactions in the complex between IL10 and IL-10 receptor (IL-10R) using quantum chemical
calculations.
Preprocessing of Cryo-EM structures (PDBID: 6X93), such as homology modeling and energy minimization was
performed by the “Structure Preparation” module implemented in MOE (CCG). After removing the missing residue andoptimizing the cap structure, structural optimization of side chain was performed with constraint (Tether 0.5). Fragment
molecular orbital (FMO) calculations were performed at the MP2/6-31G* level.
The binding energies and inter-fragment interaction energy decomposition analysis were performed. Since theligand is a giant protein, the analysis was performed according to the procedure of protein - protein and residue - residue
interactions within the complexes. Since the IL10 homodimer forms a complex with heterodimeric IL-10R (α,β), wequantitatively evaluated the binding properties between IL-10 ligands that bind to one each of the heteroreceptors.
The IL-10 homodimer, which forms a heteromeric complex with two heteroreceptors (α,β), was quantitatively
confirmed to have grater interaction with the receptorβ. At the binding interface between IL-10 and IL-10R, amino acidresidues on the IL-10R side that were strongly involved in binding were defined as Hotspot candidates and color-codedin the figure. The interaction with IL-10Rαand IL-10Rβwere shown in the left and right figure, respectively. Aminoacid residues identified by cryo- EM were colored purple, and residues identified from FMO calculations were colored
red and light blue for IL-10 interactions from each monomer in the dimeric structure. Amino acid residues suggested tohave interactions based on cryo-EM observations were confirmed to have strong interactions in the FMO analysis. In
addition, FMO calculations revealed that many amino acid residues are candidates of hot spots. The IL-10 dimer was
found to recognize receptorα at two α-helix and one loop region, and to strongly recognize receptorβ at two α-helix
regions.

― 192 ―

P01 分子認識と分子計算 Moleuclar recognition and Molecular modelingP01-12
Interaction analysis of IL-10 receptor complex using
MD and FMO calculations
Toma MIYAGISHI*, 1, Kenta KAMIMURA2, Yuma HANDA1, 2,
Midori TAKIMOTO-KAMIMURA1, 3, Mayumi ENDO4, Koji OKUWAKI1,
Yoshiharu MORI5, Shigenori TANAKA5, Yu-Shi TIAN1, Daisuke TAKAYA1,
Kaori FUKUZAWA1
School of Pharmaceutical Sciences, Osaka University, 2School of Pharmacy and Pharmaceutical
Sciences, Hoshi University, 3CBI research institute, 4PRISM BioLab, 5Graduate School of System
Informatics Department of Computational Science, Kobe University

1( * E-mail: miyagishi-t@phs.osaka-u.ac.jp )Interleukin 10 (IL-10) is an immunoregulatory cytokine and one of the therapeutic target proteins in ulcerative
colitis. The IL-10 dimer binds to the receptors IL-10Rα and IL-10Rβ. Although no crystal structure of the receptor
complex has been obtained, the structure of the ternary complex of mutant IL-10 (Super-10) with enhanced affinity for
IL-10Rβ, IL-10Rα, and IL-10Rβ was obtained using cryo-EM (PDBID: 6x93). Elucidation of the interactions important
for the binding of IL-10/IL-10R may provide suggestions for the design of new anti-inflammatory immunomodulators.
Our fragment molecular orbital (FMO) calculation for the cryo-EM structure suggested that IL-10 recognizes IL10Rα in two helix regions (α1 and α5) and one loop region (L1) and IL-10R β in three helix regions (α1, α2, and α3).
However, it has not been clear whether the binding interface is stably maintained only from calculating one static
structure. Therefore, in this study, we combined the FMO with the classical molecular dynamics (MD) methods toevaluate the interaction considering structural fluctuations.
We used the cryo-EM structure of Super-10 as a template to create the wild-type IL-10 (WT) complex structureusing MOE (Figure: Comparison of WT and Suepr10 mutation sites). 100 ns MD simulations were performed for each
WT and Super-10 complex using the GROMACS program. The molecular force field used was ff14SB for protein andspc216 for water. From each MD trajectory obtained, 100 structures were extracted every 1 ns, and FMO calculations
were performed at the MP2/6-31G* level using the ABINIT-MP program. The computer used was the supercomputer
Fugaku (hp230131).
MD simulations showed that IL-10 was located close to IL-10Rα at the α1, α5, and L1 regions and close to IL-10Rβat the α1, α2, and α3 regions. FMO calculations showed that compared to the static FMO calculation, for IL-10Rα, IL10 residues of Lys34 in the α1, and Glu142 and Glu151 in the α5 maintained the stable interaction similarly, and otherresidues fluctuated. For IL-10Rβ, Tyr18 in the α1, Glu81 in the α2, and Glu96, and Trp104 in the α3 maintained thestable interaction similarly, and other residues fluctuated. To summarize the dynamic analysis, similar results to the static
FMO analysis were obtained for the regions important for binding, and the residues whose stability was maintained
through MD simulations were identified.

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P01 分子認識と分子計算 Moleuclar recognition and Molecular modelingP01-13
Conformational behavior, dynamics, and affinity of A18-modified
aptamers toward human Immunoglobulin G
Ozora KUDO*, 1, Akari ENDO2, Masahiro SEKIGUCHI1, Azumi ITO2, Takeshi ISHIKAWA3,
Taiichi SAKAMOTO2, Kenji YAMAGISHI1
1

College of Engineering, Nihon University, 2Faculty of Advanced Engineering, Chiba Institute ofTechnology, 3Faculty of Engineering, Kagoshima University( * E-mail: ceoo23009@g.nihon-u.ac.jp )Aptamers are single-stranded nucleic acids with high binding affinity and specificity to target molecules. An
aptamer that binds to the Fc segment of human Immunoglobulin G (IgG) is identified using the systematic evolutionof ligands by exponential enrichment technique [1]. Our investigation unravels the mechanisms behind this highaffinity and specificity of the aptamer toward IgG using a computational in silico approach. Using fragment molecularorbital calculations, we have previously revealed that G7 nucleotide shows the strongest interaction energy with IgGamong all the nucleotides [2]. In particular, the phosphate group of the G7 nucleotide shows in a robust electrostaticinteraction with Lys340, that would be the driving force of aptamer-IgG binding. Furthermore, molecular dynamics(MD) calculations revealed that the sugar pucker conformation of the A18 nucleotide in this aptamer affects the whole
structure of the aptamer. These results indicated the importance of the dynamics of A18 nucleotide in maintaining thehigh affinity of this aptamer toward the target molecule.
In this study, we have designed and synthesized A18-modified aptamers, namely, A18-LNA, A18-BNA, and A182′OMe, to gain a comprehensive understanding of our findings. We have also conducted the MD calculation of these
A18 modified aptamers and determined binding affinities and kinetic parameters between these aptamers and IgG using
surface plasmon resonance. Figure shows the distribution of the torsion angle α for the G7 nucleotide and the distance
between the phosphate group of G7 nucleotide and Lys340, as obtained from MD calculations for these aptamers. We
discussed the conformational behavior, dynamics, and binding affinity of A18-modified aptamers.
Reference[1] S. Miyakawa, et al., RNA , 14, 1154 (2008).
[2] H. Yoshida, T. Ishikawa, T. Sakamoto, K. Yamagishi, et al., Chem. Phys. Letts. , 738, 136854 (2020).</FONT>― 194 ―

P01 分子認識と分子計算 Moleuclar recognition and Molecular modelingP01-14Structure and interaction analysis of complexes between BzDANP
and bulged RNAs using NMR and dynamical FMO calculation
Shu OHNO*, 1, Aina FUJIWARA2, Gota KAWAI2, Kazuhiko NAKATANI3, Yuma HANDA1, 4, Yu-Shi TIAN1,
Daisuke TAKAYA1, Kaori FUKUZAWA1
1

School of Pharmaceutical Sciences, Osaka University, 2Department of Life and Environmental
Sciences, Faculty of Engineering, Chiba Institute of Technology, 3The Institute of Scientificand Industrial Research, Osaka University, 4Hoshi University, School of Pharmacy andPharmaceutical Sciences( * E-mail: u428653a@ecs.osaka-u.ac.jp )Background: Micro-RNAs (miRNAs) are a class of non-coding RNAs and can be produced from precursor miRNAs(pre-miRNAs) by the RNA-cleaving enzyme Dicer. The Dicer-mediated RNA processing of pre-miRNA-29a and premiRNA-136, which possess the same cytosine (C)-bulged site, can be suppressed by 3,6-Bis(3-aminopropylamino)benzo[c][1,8]naphthyridine (BzDANP) [1,2]. Our previous SPR and NMR analyses have suggested that BzDANP
exhibits an affinity to the cytosine (C)- and uridine (U)-bulged sites of RNA duplexes and preferentially stabilize
C-bulged RNA. However, the details of the molecular interactions at the atomic level remain unclear. In this study,
dynamical fragment molecular orbital (FMO) calculations combining classical molecular dynamics (MD) and FMO
methods for model structures based on NMR information were performed on the structures of BzDANP bound to C- or
U-bulged RNAs to quantify the stabilities in these BzDANP-bound bulged structures.
Methods: Bulged RNA and BzDANP complexes were constructed based on RNA-A structure using UCSF Chimera.
Four of the six binding conformations between C- or U-bulge bases and BzDANP structures (i.e., C-4H-major, U-4Hmajor, U-5H -major, and U-5H-minor in the figure) were examined. 100 ns MD simulations were performed for eachcomplex structure using the AMBER22 program (Case 2022) with the force fields used as OL3 for RNA, GAFF2 for
BzDANP, and TIP3P for water. Then, 100 snapshots were extracted at 1 ns intervals, and FMO calculations at theMP2/6-31G* level were performed using the ABINIT-MP program on supercomputer Fugaku (hp230131).
Results: Calculated binding energies between BzDANP and U-bulged RNAs indicated that the U-4H-major structure
was the most stable. Compared to other U-bulged complexes, the U-4H-major structure exhibited less fluctuation
regarding hydrogen bond between BzDANP and U-bulge and stronger hydrogen bonding with U-bulge and π-πinteraction with U20 than the other two structures. However, the U-4H-major structure was less stable than the C-4Hmajor, consistent with the previous experimental findings. These differences can be due to enhanced electrostaticinteractions owing to the orientations of hydrogen bond donors and acceptors between BzDANP and C-bulge.
[1] Asako Murata, Takahiro Otabe, Jinhua
Zhang, and Kazuhiko Nakatani, “BzDANP,a Small-Molecule Modulator of Pre-miR29a Maturation by Dicer”, ACS Chem. Biol.
2016, 11(10):2790-2796[2] Takahiro Otabe, Konami Nagano, Gota
Kawai, Asako Murata, and Kazuhiko
Nakatani, “Inhibition of pre-miRNA-136
processing by Dicer with small molecule
BzDANP suggested the formation of ternarycomplex of pre-miR-136-BzDANP-Dicer”,
Bioorg Med Chem. 2019, 27(10):2140-2148― 195 ―

P01 分子認識と分子計算 Moleuclar recognition and Molecular modelingP01-15
Finding Potent HIV-1 Protease Inhibitors through FMO-Guided
Drug Design
Kowit HENGPHASATPORN*, 1, Thanyada RUNGROTMONGKOL2, Yasuteru SHIGETA1
Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki,
Japan, 2Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn
University, Bangkok, Thailand
1( * E-mail: kowith@ccs.tsukuba.ac.jp )The prevalence of HIV-1 infection continues to pose a significant global public health issue, highlighting the needfor antiretroviral drugs that target viral proteins to reduce viral replication. One such target is HIV-1 protease (PR),responsible for cleaving viral polyproteins, leading to the maturation of viral proteins. While darunavir (DRV) is apotent HIV-1 PR inhibitor, drug resistance can arise due to mutations in HIV-1 PR. To address this issue, we developed
a novel approach using the fragment molecular orbital (FMO) method and structure-based drug design to create
darunavir analogs. Using combinatorial programming, we generated novel analogs freely accessible via an on-the-cloudmode implemented in Google Colab, Combined Analog generator Tool (CAT). The designed analogs underwent cascade
screening through molecular docking with HIV-1 PR wild-type and major mutations at the active site. Molecular
dynamics (MD) simulations confirmed the assess ligand binding and susceptibility of screened designed analogs. Ourfindings indicate that the three designed analogs guided by FMO, 19-0-14-3, 19-8-10-0, and 19-8-14-3, are superior
to DRV and have the potential to serve as efficient PR inhibitors. These findings demonstrate the effectiveness of ourapproach and its potential to be used in further studies for developing new antiretroviral drugs.

― 196 ―

P02 計算化学（分子計算）/ 創薬応用 Computational Chemistry (Molecular Modeling) / Drug Discovery Application

P02-01Comparing predicted structures of cyclic peptides in solvent toexperimentally measured structures in complex
Kentaro TAKAI*, Yoshiaki TANIDA, Toshio MANABE, Chieko TERASHIMA,
Takashi MITSUI, Mitsuhito WADA, Hiroyuki SATOU
Fujitsu Ltd.

( * E-mail: takai.kentaro@fujitsu.com )Cyclic peptides are attracting attention as one of the next-generation modalities in drug discovery, because they have higher
selectivity to target proteins and lower toxicity compared to small molecules, in addition to higher membrane permeability and lower
production cost compared to antibodies. Although these properties depend on their structures, the standard technology to analyze thelarge conformational space of cyclic peptides has not yet been established. Thus, we developed a method to predict the structures insolvent named as High Performance Peptide Modeling (HPPM).
First, HPPM constructs a coarse-grained model [1] from a peptide sequence and explores the stable configurations in this modelby AI and Digital Annealer [2]. Next, the coarse-grained model is transformed to the all-atom model and HPPM performs REST2 [3]+ HMR [4] simulation on HPC using our original force field (FF-FOM) [5]. After that, stable structures are extracted by density peakclustering [6].
HPPM was performed for 25 cyclic peptides registered in PDB to compare predicted structures in water and experimentallymeasured ones in complex. The 25 peptides are in complexes. We calculated RMSD between all structures in REST2 sampling
and the representative structure of each cluster. We defined a ratio of RMSD less than 2 Å as tcc for each cluster, and the cluster
representative structure with the maximum tcc value was taken as the predicted structure. Here, it is noted that a large tcc value
indicates that the structural change of peptides in water is small. Figure 1 shows RMSD between the predicted structure and the PDB
registered structure with respect to tcc. It is found that there were 9 peptides with tcc greater than 0.5, and 6 peptides among the 9
peptides were found to have RMSD less than 2.5 Å. Although there are some exceptions, the results suggest that the structure ofcyclic peptides with small structural change in water is similar to the structure in a complex. In this investigation, we took 11 hours
and 15 minutes using 64 cores of Intel Xeon Gold 6338 (2.00 GHz) and 8 NVIDIA A100 units for 3WBN (17 residues, 7803 atoms
including water).
We proposed HPPM for structural prediction of cyclic peptides in solvent and we demonstrated a possibility that HPPM could
provide clues for structural prediction of cyclic peptides in complex when the structural change in water is small.
[1] A. Godzik, A. Kolinski, and J. Skolnick. J. Comput. Chem., 14, 10, 1194-1202 (1993).
[2] https://www.fujitsu.com/global/services/business-services/digital-annealer/[3] L. Wang, R. A. Friesner, and B. J. Berne, J. Phys. Chem. B, 115, 9431-9438 (2011).
[4] C. W. Hopkins, S. L. Le Grand, R. C. Walker, and A. E. Roitberg, J. Chem. Theory Comput., 11(4), 1864-1874 (2015).
[5] H. Fujitani, A. Matsuura, S. Sakai, H. Sato, and Y. Tanida, J. Chem. Theory Comput. 5, 1155 (2009).
[6] A. Rodriguez, A. Laio, Science 344, 6191, 1492 (2014).

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P02 計算化学（分子計算）/ 創薬応用 Computational Chemistry (Molecular Modeling) / Drug Discovery Application

P02-02
Conserved gatekeeper methionine determines accessibility
to the ATP-sites and selectivity among MAP2K1/4/7
Seigo YUMURA*, 1, Naoyuki YAMASHITA1, Daisuke KITAGAWA2, Kei MORITSUGU1,
Masaaki SAWA2, Takayoshi KINOSHITA1
Osaka Metropolitan University, 2Carna Bioscience1( * E-mail: se22961z@st.omu.ac.jp )Keywords: MAP2K1/4/7, X-ray crystal structure analysis, MD calculation
MAP2Ks (Mitogen-activated protein kinase kinases) 1/4/7 are involved in distinct biological functions, and are
drug targets for respective diseases. In our previous study, x-ray crystal structure of MAP2K7 complexed with 5Z-7oxozeaenol (5Z7O), showing the large discrepancies of the IC50 values against MAP2Ks, elucidated that 5Z7O
covalently binds to cysteine in the outside of the ATP site [1]. We here solved x-ray crystal structures of MAP2K1 and
MAP2K4 complexed with 5Z7O. These structures revealed that 5Z7O binds to MAP2K4 in the outside of ATP sitewith noncovalent manner, while covalently binds to MAP2K1 in the deep ATP site. Differences in binding modes of
5Z7O with MAP2K1/4/7 coincide with structure activity relationship of 5Z7O against MAP2K 1/4/7. Furthermore,
MD calculations suggested that distinct accessibilities of 5Z7O to the deep ATP site are regulated by the configurationof conserved gatekeeper methionine. These structural insights likely promote to produce highly selective and potent
MAP2K 1/4/7 inhibitors.
[1] Y. Sogabe, T. Matsumoto, T. Hashimoto, Y. Kirii, M. Sawa, T. Kinoshita, Bioorg. Med. Chem. Lett. 25 (2015): 593― 198 ―

P02 計算化学（分子計算）/ 創薬応用 Computational Chemistry (Molecular Modeling) / Drug Discovery Application

P02-03
Protein Dynamics Prediction using Video Prediction AI:
An Alternative to Traditional MD Simulations
Yuki SHIMIZU*, 1, Ryo KANADA2, Yuji OKAMOTO1, Shuntaro CHIBA2, Yasushi OKUNO1, 2
1

Graduate School of Medicine, Kyoto University, 2Center for Computational Science, RIKEN( * E-mail: shimizu.yuki.75s@st.kyoto-u.ac.jp )BackgroundIn the realm of drug development, the dynamics of protein structures have been estimated using Molecular Dynamics(MD) simulations. Due to the extensive number of atoms and the prolonged time required for structural changes, thecomputational cost of MD simulations escalates. However, increasing the size of time-step compromises the stability of
the calculations, setting a limit to the acceleration of MD simulations. In this study, we propose an alternative method to
MD simulators by estimating the transition of protein distance matrices using video prediction AI. This approach aims
to expedite the estimation of drug-protein interactions and the active sites of proteins.
Dataset
For the Glutamine Binding Protein (GBP), 25 independent MD simulations were conducted. The computational
time-step width was set at 2fs, spanning a total period of 1000ns. This time-series data was sampled according to thecomputational time width of the video prediction AI model.
Methodology
Sequential protein coordinate data was used to derive distance matrices for each amino acid residue. Utilizing thestate-of-the-art video prediction AI, “Simpler yet Better Video Prediction (SimVP)“, we estimated the transition of
distance matrices for each time step. Furthermore, the predicted distance matrices of amino acid residues were restored
to a three-dimensional structure using Multi-Dimensional Scaling (MDS), and the results were compared with those of
the MD simulator.
Results & Discussion
Although there’s no guarantee that the results predicted by SimVP maintain the conditions of the distance matrix,
it was demonstrated that they can be restored to a three-dimensional structure using MDS. Moreover, it was confirmedthat protein structural changes at a time-step width of more than 2ps could be stably computed. This outcome represents
a time-step width unattainable with traditional MD simulators, suggesting a potential improvement in computational
speed.

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P02-05
Validation of Docking against an Ensemble of Molecular
Dynamics Snapshots
Ryuichiro HARA*, Abijit KAYAL, Stuart FIRTH-CLARK
Cresset( * E-mail: ryuichiro.hara@cresset-group.com )Docking identifies potential binding poses of putative ligands against a static protein structure and is a useful methodin the early stages of drug discovery. While these methods often consider the flexibility of the ligand molecules being
docked, the flexibility of the target protein is often not considered. This oversimplification can mean that ligands whichbind only when there is a change in the target protein structure will not be successfully identified by these docking
methods. One way to address this shortcoming is to dock the ligands across a range of static structures, each of which
represents a different conformation of the target. This ‘ensemble docking’ approach is extremely powerful when
multiple structures of the target protein exist, but there are often cases where multiple structures are not available. In
these situations, it is possible to generate a range of structures representing different conformations of the target protein
by extracting frames from molecular dynamics (MD) simulations that could be subsequently used for an ensemble
docking experiment [1,2].
To test the validity of this approach, we consider ligands which bind to the B-Raf target, which can be potentiallyused to treat cancer. A single crystal structure is sampled by MD simulations and the resulting trajectory is thenclustered. We then compare the docking results obtained from the conformational ensemble with the results generated
when the ligands are docked against their native crystal structures. We go on to show that using conformational
ensembles of the target generated from a single X-ray structure can give docking scores for various ligands that arevery close to the value obtained from the native crystal structure. This suggests that docking approaches that use an
ensemble of MD snapshots of the target protein arean effective approach when multiple structures are not known.
[1] Campbell, A. J.; Lamb, M. L.; Joseph-McCarthy, D. Ensemble-based docking using biased molecular dynamics,
Journal of chemical information and modeling, 2014, 54(7), 2127–2138.
[2] Amaro, R. E; Baudry, J.; Chodera, J.; Demir, Ö., McCammon, J. A.; Miao, Y.; Smith, J. C. Ensemble docking in
drug discovery. Biophysical journal, 2018, 114(10), 2271–2278.

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P02-06
A search method for novel protein functional site based on the spatial
distribution of disease-associated missense variants
Chie MOTONO*, 1, 2, Atsushi HIJIKATA3, Takatsugu HIROKAWA4, 5, Kenichiro IMAI2, 6
Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute ofAdvanced Industrial Science and Technology (AIST), 2Cellular and Molecular Biotechnology
Research Institute, National Institute of Advanced Industrial Science and Technology,
3
Department of Medical Sciences, Tokyo University of Pharmacy and Life Science, 4Divisionof Biomedical Science, Faculty of Medicine, University of Tsukuba, 5Transborder Medical
Research Center, University of Tsukuba, 6Global Research and Development Center for Businessby Quantum-AI Technology (G-QuAT), National Institute of Advanced Industrial Science andTechnology
1( * E-mail: c-motono@aist.go.jp )We propose a method to search for undiscovered protein functional sites based on 3D variant clusters, in which
disease-associated variants are considered as clusters on protein 3D structures (3D variant clusters). Disease-associatedvariants tend to cluster around functional sites in the protein structure, and clusters that are not around known functionalsites may point to undiscovered functional sites. Those functional sites may help to elucidate new protein functions andto discover novel drug targets.
We therefore comprehensively mapped disease-associated variants registered in ClinVar database against humanproteins with known structures and searched for 3D variant clusters. As a result, 1176 3D variant clusters (389 proteins)were identified. The identified 3D variant clusters were analyzed for association with known functional sites. About
30% of the 3D variant clusters were not located near ligand binding sites or protein-protein interaction sites, indicating
they were candidates for novel functional sites.
To obtain more candidate sites, we also mapped disease-associated variants on the predicted structure of humanproteins by AlphaFold2. We found 1167 proteins with 3D variant clusters. Among these, we yielded 578 3D variant
clusters (197 proteins) by considering LOEUF (the loss-of-function observed/expected upper bound fraction) score andautosomal dominant inheritance, which are likely to be related to loss of function or gain of function.
Undiscovered functional sites are likely to be dynamically formed ligand binding pockets (cryptic binding sites)or allosteric sites, rather than simple pockets. We use mixed solvent molecular dynamics simulations to estimate thefunctions of the unknown candidate functional sites. Using small organic compounds as co-solvents, mixed solvent
molecular dynamics simulations can induce conformational changes in proteins and estimate functional sites from theprobability of co-solvent molecules on the protein surface. We also report the development of search methods for novel
functional sites by mixed solvent molecular dynamics simulations.

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P02-07
Insight into the Binding Modes of Allosteric ERK2 Inhibitors using
Metadynamics Simulations
Hajime SUGIYAMA*, 1, Seisuke HASEGAWA2, Mayu YOSHIDA2, Takayoshi KINOSHITA2
Mitsubishi Chemical Corporation, 2Osaka Metropolitan University

1( * E-mail: hajime.sugiyama.ma@mcgc.com )ERK2 (extracellular signal-regulated kinase 2) is a member of the mitogen-activated protein kinase family. STAT3(signal transducer and activator of transcription 3), which plays an essential role in normal glucose homeostasis, plays anessential role in normal glucose homeostasis, and regulates cell proliferation, differentiation, and various other cellularresponses. Based on previous studies on the ERK2/STAT3 pathway, the authors focused on ERK2 as a candidate target
for diabetes treatment and identified small molecule compounds with inhibitory activity through in silico screening.
The binding modes and multiple metastable conformations were predicted by simulating the association anddissociation events of each of ERK2 and p38α. By comparing the results with the crystal structures, we inferred
the mechanism by which the ligands reach their most stable conformations. Since the events typically exceed thesimulatable time scale, we used a combination of metadynamics and Hamiltonian replica exchange methods.
As a result, we confirmed the occurrence of these events during the calculation and were able to calculate the freeenergy surfaces. The detailed results of the analysis will be presented on the same day. These results would greatly
enhance the development of highly potent inhibitors.

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P02-08
Evaluation of Neural Network Potentials on Drug-like Molecules andApplications in Drug Discovery
Junya YAMAGISHI*
Preferred Networks( * E-mail: jyamagishi@preferred.jp )Neural network potentials (NNPs), also known as machine learning potentials, are artificial neural networkmodels that are designed to quickly and accurately calculate the potential energy and forces from quantum mechanics
calculations. Preferred Potential (PFP) is a universal neural network potential developed by Preferred Networks Inc. and
ENEOS Corporation that is able to calculate any combination of the 72 elements. PFP is used as the core technology inMatlantisTM, which is a general-purpose atomistic simulation software marketed by Preferred Computational Chemistry.
However, due to its primary use in materials science research, PFP’s performance on the drug-like molecules has not yet
been fully evaluated.
In this study, we evaluated the performance of PFP for the first time on drug-like molecules with different molecularsizes and structural varieties, and compared its performance with other NNPs, such as ANI-2x and SpookyNet. The
performance of NNPs was evaluated by measuring the error between the potential energy and forces obtained fromquantum mechanics calculations and those obtained from NNPs. Our results showed that PFP outperformed other NNPs
tested in overall performances. These results suggest that PFP could be useful in drug discovery, including binding freeenergy predictions and other related areas. Further details will be shown at the conference.

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P02-09Development of free energy calculation pipeline
in Preferred Networks
Mizuki TAKEMOTO*
Preferred Networks Inc.

( * E-mail: mtakemoto@preferred.jp )Protein-ligand binding free energy calculations are an indispensable method for in silico drug discovery, particularly
useful in the early stages of drug discovery research and during lead optimization. Although highly accurate binding
free energy calculations require a large amount of computational resources, their application is expanding every year
as computational resources become larger. At the same time, the process is becoming more complicated. It is necessary
to be able to flexibly set up the procedures of preprocessing, docking, equilibration MD, extraction of representative
structure, and free energy calculation according to the target protein, as well as to perform the calculation efficiently,
including error handling during the simulation.
Preferred Networks (PFN) owns a private supercomputer, the MN cluster (with over 1300 GPUs), which is a very
powerful facility for drug discovery. On the other hand, the MN cluster is a heterogeneous hardware environment,
which makes it difficult to have a uniform flow of computations and requires flexibility in setting the flow rate tomeet the diverse and high volume demands within the company. PFN's cluster solves this problem with its own tuned
kubernetes and argo workflow. We have built a pipeline to automate the sequence of free energy calculations, which runs
efficiently on the kubernetes cluster. This allows us to calculate a large number of targets and computational conditions
simultaneously, which is expected to improve the efficiency of the in silico drug discovery process. This system is closed
within the MN cluster and can complete calculations without sending data to external services. In addition, we are also
working on the use of PFP, a neural network potential that is the backend of Matlantis (TM), to improve the accuracy of
calculations and to perform calculations on targets that have been difficult to address with classical forcefields.
In this poster we will present the main aspects of the implementation of such a protein-ligand binding free energy
calculation pipeline in PFN, its workflow and features, as well as the results of binding free energy calculations
computed with our pipeline.

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P02-10Quantitative Estimation of Protein-Chemical Substructure Interaction
with Inverse Mixed-Solvent Molecular Dynamics Simulation
Keisuke YANAGISAWA*, 1, Ryunosuke YOSHINO2, 3, Genki KUDO4,
Takatsugu HIROKAWA2, 3
1
School of Computing, Tokyo Institute of Technology, 2Faculty of Medicine, University ofTsukuba, 3Transborder Medical Research Center, University of Tsukuba, 4Physics Department,
Graduate School of Pure and Applied Sciences, University of Tsukuba( * E-mail: yanagisawa@bi.c.titech.ac.jp )The computational methods used in lead optimization to improve activity and reduce the toxicity of compounds
are still evolving. We previously proposed inverse mixed-solvent molecular dynamics (MSMD) simulation for lead
optimization[1]. The ordinal MSMD constructs an atom interaction profile on a protein surface. Contrary to it, inverse
MSMD constructs a protein residue interaction profile on a probe surface expressing preferred protein environmentof a probe that is protein independent information; However, in the previous proposal, qualitative evaluation was only
available and we could only confirm the agreement between the protein surface and profiles of a few probes. In thisstudy, we improved the protein residue interaction profile of a probe obtained through inverse MSMD to quantitatively
evaluate the degree of alignment between a protein surface and a profile. We validated the performance of the methodby comparing it with SAR experimental results. The code to run MSMD simulation is open-sourced at https://github.
com/keisuke-yanagisawa/exprorer_msmd.
[1] K Yanagisawa, R Yoshino, G Kudo, and T Hirokawa. Inverse Mixed-Solvent Molecular Dynamics for Visualizationof the Residue Interaction Profile of Molecular Probes. IJMS 23, 4749, 2022. doi: 10.3390/ijms23094749― 205 ―

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P02-11
Comprehensive structual analysis of PROTAC mediated ternarycomplexes using enhanced conformational sampling methods
Genki KUDO*, 1, Takumi HIRAO2, Takatsugu HIROKAWA3, 4, Ryunosuke YOSHINO3, 4
Physics Department, Graduate School of Pure and Applied Sciences, University of Tsukuba,
Master's Program in Medical Sciences, Graduate School of Comprehensive Human Sciences,
University of Tsukuba, 3Faculty of Medicine, University of Tsukuba, 4Transborder Medical
Research Center, University of Tsukuba

1
2( * E-mail: s2330052@u.tsukuba.ac.jp )PROteolysis-TArgeting Chimera (PROTAC) is the compound that forms the ternary complex between E3 ubiquitin
ligase and the target protein, mediating ubiquitination of the E3 ligase and acting as an inducer of the target protein
degradation [1]. While PROTAC is expected as the new drug modality, the rational PROTAC design is still difficult
because of the complex pharmacological mechanism compared to conventional small molecule drugs. In particular, thedesign of the proper linker moiety in PROTAC is a great challenge, as this moiety needs to induce the formation of thestable ternary complex [2].
In this study, we performed a comprehensive structural analysis of ternary complexes for the rational PROTAC
design. Four PROTACs (MZ1-4), which mediate von Hippel–Lindau protein (VHL) and the second bromodomain of
Brd4 (Brd4BD2) and are known activity as degraders [3], were used for our analysis. Efficient conformational sampling
methods, Parallel Cascade Selection Molecular Dynamics (PaCS-MD) simulation and Outlier FLOODing (OFLOOD)
method, were performed to sample ternary complex structures with each PROTAC and construct Free Energy
Landscapes (FEL) [4,5].
Based on ensembles obtained from our sampling and FEL, we discuss the differences in the affinity, degradation
potency, and stable ternary complex structure between PROTACs.
[1] Toure M, Crews CM. Angew Chem Int Ed Engl. 2016;55(6):1966-1973.
[2] Tang R, Wang Z, Xiang S, et al. JACS Au. 2023;3(6):1775-1789.
[3] Chan KH, Zengerle M, Testa A, Ciulli A. J Med Chem. 2018;61(2):504-513.
[4] Harada R, Kitao A. J Chem Phys. 2013;139(3):035103.
[5] Harada R, Nakamura T, Shigeta Y. J Comput Chem. 2016;37(8):724-738.

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P02-12
Discovery and evaluation of potent covalent inhibitors targeting
SARS-CoV-2 main protease by hybrid in silico drug study
Uika KOSHIMIZU*, 1, Junichi ONO2, Yoshifumi FUKUNISHI3, Hiromi NAKAI1, 2
Department of Chemistry and Biochemistry, School of Advanced Science and Engineering,
Waseda University, 2Waseda Research Institute for Science and Engineering (WISE), Waseda
University, 3Cellular and Molecular Biotechnology Research Institute, National Institute ofAdvanced Industrial Science and Technology
1( * E-mail: k-ui-espoir@ruri.waseda.jp )Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
infection, has been raging ever since the outbreak in 2019. Experimental and theoretical studies are being actively
conducted worldwide to establish preventive and therapeutic against COVID-19 and its sequelae (long COVID). Many
drug studies have focused on main protease (Mpro), which plays an important role in the replication of SARS-CoV-2.
So far, oral antiviral drugs of nirmatrelvir and ensitrelvir developed by Pfizer and Shionogi, respectively, have been
approved. However, there remain ongoing health concerns, possibility of future waves of infection, and emergence ofdrug-resistant mutants. Therefore, the development of additional inhibitors with strong antiviral activity against Mpro anddrug-resistant mutants is required.
Mpro has a catalytic dyad composed of His41 and Cys145 in the active site, and Sγ−(Cys145), which is generated by
the proton transfer between the catalytic dyad, forms a covalent bond with the natural substrate via specific recognition,
resulting in the enzymatic reaction for cleaving polypeptides. Nirmatrelvir was developed as a peptidomimetic covalent
inhibitor, including an electrophile warhead targeting Cys145 in Mpro, with high binding affinity and reactivity. To
propose alternative covalent inhibitors from in silico drug studies, it is necessary to evaluate both the binding affinity
and reactivity, and thus the quantum mechanical treatment in addition to the conventional in silico drug studies isessential.
In this study, we conducted a hybrid in silico drug discovery study to search for new covalent inhibitors targeting
Mpro, by combining ensemble docking, which is used in conventional in silico drug discovery studies to evaluate noncovalent binding affinity, and large-scale quantum molecular dynamics (QMD) simulations to evaluate covalent binding
reactivity. In the QMD simulations, all the atoms, including Mpro, inhibitor, and water solvent, were treated quantummechanically using our original divide-and-conquer density-functional tight-binding (DC-DFTB) method and the
Fugaku supercomputer. A non-peptidemimetic covalent inhibitor with high binding affinity and moderate reactivity was
identified. This is a new approach for evaluating binding reactivity in in silico drug studies, and is important for drug
studies involving covalent bond formation.

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P03-01
Discovery of the middle-sized compounds inhibiting the SARS-CoV-2
viral entry, using in silico approach and NMR analysis
Mariko YOKOGAWA*, 1, Mahoro HORIUCHI1, Shunki KANEICHI1, Taiga OTAKE1,
Tomoki YONEZAWA1, Yugo SHIMIZU1, 2, Kazuyoshi IKEDA1, 2, Yuichiro YAMAMOTO3, Shota SAKAI4,
Kohji NOGUCHI3, Masayoshi FUKASAWA4, Masanori OSAWA1
1

Faculty of Pharmacy, Keio University, 2RIKEN, 3Faculty of Pharmaceutical Sciences, Tokyo
University of Science, 4National Institute of Infectious Diseases( * E-mail: yokogawa-mr@pha.keio.ac.jp )Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Although several
treatments and vaccines are available, definitive treatment has yet to be established. Since the interaction between thereceptor binding domain (RBD) of SARS-CoV-2 spike protein and the extracellular domain of hACE2 is importantfor viral entry, compounds that directly bind to either the RBD or hACE2 and inhibit their interaction should be a drugcandidate for COVID-19. Here, we performed ELISA-based assays to find entry inhibitors targeting the interaction
between the RBD and hACE2 using small-to-medium sized compounds from our PPI-inhibitor library, DLiP [1], and anumber of compounds proposed by in silico approach. The assays discovered multiple hit candidates and most of them
inhibited the SARS-CoV-2 infection confirmed by viral infection assay, however the estimated IC50 values were ~10-4
M. NMR is a powerful tool for analyzing such a weak binding of compounds to their target protein. The NMR-derived
information on the binding sites on the protein would be useful for drug design to improve binding affinity and thus
inhibitory activity of the candidates. Our NMR analyses indicated that some compounds, possessing common chemical
structure, directly bound to hACE2. To identify their binding site on hACE2 and design the compounds with higher
affinity, assignments of the NMR signals of hACE2 are underway.
[1] K. Ikeda et al. , DLiP-PPI library: An integrated chemical database of small-to-medium-sized molecules targeting
protein-protein interactions., 2023, Front Chem. 10:1090643.

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P03-02Development of Chemical Similarity Search Tool for
Hit-Expansion Research
Toshiaki WATANABE*, Masaya FUJII, Kosuke TAKEUCHIDAIICHI SANKYO CO., LTD.

( * E-mail: watanabe.toshiaki.h5@daiichisankyo.co.jp )The use of three-dimensional (3D) structure search offers several advantages over relying solely on two-dimensional(2D) representations. These benefits include increased accuracy, improved chemical similarity analysis, better
pharmacophore mapping, enhanced virtual screening, reliable structure-activity relationship (SAR) analysis, rational
molecular design, and understanding of conformational flexibility.
In this study, we have developed a 3D structure search system based on ROCS.1,2 It explains how the system
performs shape-based overlays of conformers of a candidate molecule onto a query molecule using atom-centered
Gaussian functions. The compounds are preprocessed and converted to 3D structures using OMEGA. The system has auser interface created with BIOVIA Pipeline-Pilot, allowing users to perform 2D and 3D searches of internal or external
catalogs.
We compared 3D and 2D similarity of JAK2 inhibitors and other compound groups. This result showed that 3D
similarity searches explore chemical space differently than 2D searches. A threshold study using a dataset of compounds
shows that a ROCS-Tanimoto combo score of 1.4 or higher has a high expectation value for identifying active
compounds in 3D similarity search.
In summary, we have constructed a 3D search system for internal and external compounds. In order to betterinterpret the search results, we also studied the threshold values for obtaining active compounds with methods describedin the previous literature. As a result, it was found that ROCS tanimoto combo score of 1.4 or higher was found to be adesirable threshold for the data used in this study.
1. Paul C. D. Hawkins, A. Geoffrey Skillman, and Anthony Nicholls. Comparison of Shape-Matching and Docking as
Virtual Screening Tools. J. Med. Chem. 2007, 50, 1, 74–82.
2. Thomas S. Rush, J. Andrew Grant, Lidia Mosyak, and Anthony Nicholls. A Shape-Based 3-D Scaffold Hopping
Method and Its Application to a Bacterial Protein−Protein Interaction. J. Med. Chem. 2005, 48, 5, 1489–1495.

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P03-03
Spatial Predictions of Protein-Protein Interaction with
AlphaFold Multimer
Wenxing HU*, Masahito OHUE
Tokyo Institute of Technology( * E-mail: hu.w.ac@m.titech.ac.jp )The rapid development of protein sequencing technology has led to a disparity between proteins with known
sequences and those with known structures. The current sequence-based predictions may have defectiveness causedby the lack of structural information. However, structure-based prediction methods cannot predict newly sequenced
proteins due to the tardiness of structure determination. The success of AlphaFold in predicting protein structures
provides potential solutions to alleviate this problem and offers a novel method to predict protein-protein interactions.
This research focuses on using deep neural networks to analyze the protein complexes predicted by AlphaFold Multimer
to predict protein-protein interactions. The spatial models of protein complexes are determined by transforming
atomic coordinates through the calculation of atomic volume distributions. To achieve this prediction, advanced image
processing strategies are employed to extract crucial 3D structural information from the protein complexes. DenseNet
and ResNet are network architectures that are fruitful in 2D image recognition area, and the applications have been
extended to the motion recognition area. Because the estimation of protein-protein interactions through protein
complexes is primarily determined by evaluating the distances between residues, this has a high commonality withimage recognition. In this project, both DenseNet and ResNet backbone structures are implemented in 3D convolutional
networks to resolve protein 3D structure data. The models are evaluated by comparing other state-of-the-art protein
interaction predictions, including SpeedPPI (Also referred as FoldDock), which makes estimation based on AlphaFold
prediction, and D-script and DeepTrio, which are sequence-based methods, and PEPPI, a combination of machinelearning and homological search. The proposed method showcases promising results in predicting protein-protein
interactions, thereby highlighting the potential of 3D space rendering processing methods in advancing structural
biology research.

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P03-04
Optimization of DDR1 inhibitor with a desired Pharmacophore using
Deep Reinforcement Learning
Chisato KANAI*, 1, Enzo KAWASAKI1, Tomohiko TASAKA2, Atsushi YOSHIMORI3
1

Value & Access Department, INTAGE Healthcare Inc., 2Chief Executive Oﬃcer, Aﬃnity Science
Corporation, 3Chief Executive Oﬃcer, Institute for Theoretical Medicine, Inc.

( * E-mail: kanai@intage.com )Finding chemical structures that match a target protein pocket in terms of both shape and pharmacophore features
is the goal of drug design. In recent years, using deep generative models based on reinforcement learning has received
much attention as an effective method for de novo design [1]. We have previously developed a strategy to generate
selective kinase inhibitors with a desired pharmacophore using deep reinforcement learning and chemical genomicsbased virtual screening (CGBVS) [2] (called Deep Quartet (DQ)).
Discoidin domain receptor 1 (DDR1) inhibitors were designed using DQ with a lead compound having an IC50
value of 92.5 nM [3]. To optimize the lead compound, we developed a DQ scheme that allows structure generation
with a fixed part of the chemical structure. We obtained DQ-generated compounds that fulfilled the pharmacophore
better than the lead compound. The results showed that our strategy can be adopted into general use for optimizing leadcompounds. A detailed analysis and application will be presented at the meeting.
[1] Olivecrona, M., Blaschke, T., Engkvist, O., Chen, H., J. Molecular de-novo design through deep reinforcement
learning, Journal of Cheminformatics, 9:1, 2017.
[2] Yoshimori, A., Kawasaki, E., Kanai, C., Tasaka, T., Strategies for design of molecular structures with a desired
pharmacophore using deep reinforcement learning, Chem. Pharm. Bull. 68, 227–233, 2020.
[3] Yoshimori, A. et al., Design and Synthesis of DDR1 Inhibitors with a desired pharmacophore using deep generativemodels. ChemMedChem 16, 1–5, 2021.

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P03-05Natural Product Drug Discovery by Electronic-Structure
Informatics: Search for Novel α-glucosidase Inhibitors
Yusuke TATEISHI*, 1, Manabu SUGIMOTO1, 2, 3, 4
Graduate School of Science and Technology, Kumamoto University, 2Facility of Advanced
Science and Technology, kumamoto University, 3Institute of Industrial Nanomaterials, kumamoto
University, 4Center for Green Ammonia Research, National Institute of Technology, Numazu
College1( * E-mail: 235d8761@st.kumamoto-u.ac.jp )Potential inhibitors of α-glucosidase were screened from a natural product (NP) using the electronic structure
information (ESI) method proposed by the authors [1]. As is well known, α-glucosidase is one of the targets in diabetes
treatment. NP-based drug discovery is challenging and promising since many of the marketed drug molecules have beendeveloped from NPs [2]. The ESI method is expected useful because it focuses on the electronic features of molecules
without directly referring to their structural features. Namely, it may be possible to find a variety of scaffolds that are notconstrained within known ones.
In this work, we applied a machine learning to establish a regression model and perform in silico screening froman NP database as follows. First, we collected experimentally determined pIC50 values for the 441 inhibitors fromthe ChEMBL database [3]. For the compounds, we evaluated ESI descriptors including energy-related parametersdescribing e.g. intermolecular interactions. Machine learning was applied to predict pIC50 values for the α-glucosidaseusing the XGBoost (XGB) algorithm [4]. The obtained regression model gave 0.85 for the R2 value (coefficient ofdetermination) to the test set, suggesting that the XGB method may be useful for predicting the pIC50 values and for insilico screening of inhibitors for α-glucosidase.
The screening was performed using the KampoDB [5] registering traditional Japanese drugs. As a result, several
compounds reported as α-glucosidase inhibitors were rediscovered. In order to evaluate binding affinities of the screened
compounds, docking simulations were performed using AutoDock Vina [6]. It was found that some compounds withrelatively large pIC50 values should have sufficient docking capability to the enzyme . These results indicate that the insilico screening approach in ESI can provide promising inhibitor candidates from NP databases.
[1] T. Ideo, M. Sugimoto et al ., Chem. Lett. , 2021, 50 , 849.
[2] D. J. Newman, G. M. Cragg, J. Nat. Prod. , 2020, 83 , 770.
[3] D. Mendez, et al ., Nucleic Acids Res. , 2019, 47 , 930.
[4] T. Chen, C. Guestrin, KDD’16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge
Discovery and Data Mining, 2016, 785.
[5] R. Sawada, et al ., Sci. Rep. , 2018, 8 , 11216.
[6] J. Eberhardt, et al ., J. Chem. Inf. Model. , 2021, 61 , 3891.

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P03-06
Computational-Model-Based Machine-Learning Prediction ofRegioselectivity in Organic Reactions: Application to Electrophilic
Attach to Quinone Derivatives
Yusuke TSUTSUMI*, 1, Manabu SUGIMOTO1, 2, 3, 4
Graduate School of Science and Technology, Kumamoto University, 2Faculty of Advanced
Science and Technology, Kumamoto University, 3Institute of Industrial Nanomaterials, Kumamoto
University, 4Center for Green Ammonia Research, National Institute of Technology, Numazu
College1( * E-mail: Tsutsumi.deepChemInfo@gmail.com )When drug molecules are absorbed into the liver, they have the potential to be transformed into quinone derivatives[1]. As a result, it is crucial to consider the excretion of these transformed compounds, which occurs with cysteine in
the human body, during the drug design process [2]. For optimizing drug candidates, it is desirable to have a priori
knowledge of regioselectivity in electrophilic reactions of quinone derivatives [3].
In this work, we have tried to construct a machine-learning model for predicting the regioselectivity of quinone
derivatives. In the implementation, we are interested in the use of molecular descriptors which can be theoreticallyjustified. Since the most reasonable theoretical justification can be done on the basis of quantum chemistry, we havebeen suggesting “Electronic-Structure Informatics (ESI)” in which molecular descriptors with quantum chemical
background have been developed.
Herein, we will report a new type of ESI method in which local quantum-chemical descriptors are newly introduced.
The details of the quantum chemical descriptors will be explained in detail in the presentation. In order to quantify
the local reactivity of quinone derivatives, which are considered representative metabolite compounds, in glutathione
conjugation, a graph convolutional neural network (GCN) has been applied for a machine learning model reproducing
chemical reactivity given at the Xenosite platform (https://xenosite.org/). The platform has been developed by
Swamidass et al. [4] in which reactivity on each site in a reactant can be highly accurately predicted. In our work, thesimulation (prediction) results have been referred to as an alternative to the experimental data. The most reactive sites
on 853 quinone molecules have been predicted with an accuracy of 82.1 % in our machine-learning model.
[1] J. L. Bolton, et al. , Chem. Res. Toxicol. , 2017, 30 , 13.
[2] E. F. Barreto, et al. , Drug Excretion, Reference Module in Biomedical Sciences , 2021.
[3] C. Limban, et al. , Toxicol. Rep. , 2018, 5 , 943.
[4] T. B. Hughes, S. J. Swamidass, et al. , Chem. Res. Toxicol. , 2015, 28 , 797.

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P03-07
AI-AAM. Increasing the number of active compounds with various
scaffolds and substituents from one active compound
Jun NAKABAYASHI*, Kyosuke TSUMURA, Issei DOI, Shino OHIRA
Analysis Technology Center, FUJIFILM Corporation( * E-mail: jun.nakabayashi@fujifilm.com )Having multiple active compounds with diverse scaffolds and substituents is important for improving the successrate in drug development to avoid various problems, such as low solubility, high toxicity, or low permeability. With theaim of increasing the number of the active compounds from one active compound, we have developed AI-AAM® (AIAmino-Acid Mapping), scaffold and substituent transformation method based on a de novo design AI and a molecularsimulation-based descriptor, Amino Acid Mapping (AAM) [1,2].
AAM is the set of interactions between 20 amino acids and a ligand, assuming the binding between the ligand andits target protein can be described by the interactions. Since the calculation is not based on the structural information of
the protein but only on the interactions between small molecules, AAM requires a short computational time comparedto other accurate prediction methods of protein?ligand binding affinity. AI-AAM is the scaffold and substituent
transformation method to search and design new candidate compounds in which the same order of the binding affinity
as the original active ligand was maintained by a new compound with the high AAM similarity to the ligand. We
previously reported our successful scaffold hopping performed by AI-AAM, a lead compound from a known lead ligand
and a hit compound from a bioactive peptide [3].
Here, we introduce two updates of AI-AAM. First, AAM similarity was confirmed to correspond to the binding
energy computed by FMO and pIC50 (R2 = 0.657 and 0.664) for a kinase inhibitor, SYK (Fig.). Although the comparison
with the experimental values has not been examined yet, we also found out that the correlation between AAM similarity
and FMO was 0.802 for a membrane protein, MexB. This result indicated that AI-AAM, which enables high-speed
calculation since it does not use protein structures, can provide results equivalent to FMO. Next, we extend our de novo
design AI so that we can select the partial structure of the active compound for modifications by AI-AAM while otherparts fixed, and we found new active inhibitors for SYK and MexB.
[1] Mao Tanabe et al., bioRxiv (2023), doi: https://doi.org/10.1101/2023.07.03.547598[2] Fujifilm started a CRO service based on AI-AAM, i.e., drug2drugs®. See the following URL for a detailed
description: https://labchem-wako.fujifilm.com/jp/custom_service/products/95323.html[3] Shino Ohira et al., CBI Annual Meeting 2022, P08-07; Jun Nakabayashi et al., CBI Annual Meeting 2022, P08-14.

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P03-08Prediction of gut microbiota from questionnaire datausing machine learning
Ryuto KOYAGI*, Takuji YAMADA
Department of Life Science and Technology, Tokyo Institute of Technology( * E-mail: longdouxiaobamu@gmail.com )Many bacteria exist in the human intestine, and their complex interactions form the intestinal microflora. The
intestinal microbiota has been reported to be associated with diseases such as colorectal cancer and diabetes, and
it is hoped that testing for intestinal bacteria may be useful for early detection and treatment of diseases. However,the current method of acquiring intestinal bacteria data based on gene sequences and their composition data is timeconsuming and expensive for clinical use and market application of intestinal microbiota testing. Therefore, although
intestinal microbiota data may be in great demand for both clinical and market applications, the time and cost required
to acquire the data is one of the barriers.
The objective of this study is to predict the relative abundance of intestinal bacteria and enterotypes defined by
the structure of intestinal bacteria from a machine learning approach using questionnaire information on eating habits
and lifestyle, which can be obtained relatively inexpensively and quickly. By learning the questionnaire information
as input and various intestinal bacteria information as labels, we will proceed to construct a machine learning modelthat outputs intestinal environment information from the questionnaire information only. Regarding the choice of themachine learning model, since many analyses of intestinal bacteria associations using the random forest model havebeen reported, the random forest model will be used in this study as well.
In this study, we used metagenomic data obtained from stool samples of 237 healthy subjects and 279 itemsof questionnaire information on lifestyle and eating habits. The phylogenetic composition was estimated from themetagenomic data using QIIME, and a total of 122 bacterial genera were detected.
In this study, instead of directly regressing the relative abundance of bacteria, we used the amount of bacteria relative
abundance as a label. That is, for each bacterium, the relative abundance of bacteria was divided into two groups, one
group with a relative abundance greater than the median and the other group with a relative abundance less than the
median, which was used as the label for the abundance of bacteria. A random forest model was used to construct amodel to predict the relative abundance of bacteria using the questionnaire data as input data and the relative abundanceof bacteria of the subjects as labels. 122 This was done for all bacteria and a model was constructed for each bacteria.
A score of 0.663 was recorded in the AUC score when cross-validated using all healthy samples. When the same
analysis was performed on obese subjects (BMI > 25), an AUC score of 0.900 was recorded, indicating that the nut
intake was a significant contributor to this prediction.
These results suggest that information obtained from the questionnaire, such as BMI and nut intake, may be used to
predict intestinal environment information with high accuracy.

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P03 ケムインフォマティクス・機械学習 / AI による創薬研究 Cheminformatics, machine learning, AI-based drug discovery

P03-09A New Molecular Generation Model Combining Deep Learning
and Reinforcement Learning
Hiroaki IWATA*, 1, Taichi NAKAI1, Takuto KOYAMA1, Shigeyuki MATSUMOTO1,
Ryosuke KOJIMA1, Yasushi OKUNO1, 2
1

Kyoto University, 2RIKEN Center for Computational Science( * E-mail: iwata.hiroaki.3r@kyoto-u.ac.jp )Molecular generation plays a pivotal role in advancing drug discovery, materials science, and chemical exploration.
It accelerates the search for novel drug candidates, enables precise material design, and enriches our comprehensionof molecular diversity. Through the utilization of artificial intelligence methodologies, such as molecular generativemodels, researchers have addressed the task of identifying effective molecules possessing desired attributes. In thiswork, we introduce an innovative molecular generative model that integrates a deep neural network based on moleculargraphs with a reinforcement learning approach (Iwata et al., doi: 10.26434/chemrxiv-2023-q8419). We assessed thevalidity, novelty, and optimized physicochemical properties of the generated molecules. Of significance, the model
ventured into unexplored domains of chemical space, facilitating the effective discovery and design of novel molecules.
This approach possesses significant potential to transform the landscape of drug discovery, materials science, andchemical research, thereby expediting scientific innovation. By leveraging advanced techniques and exploring
previously unexplored chemical spaces, this study presents opportunities for the effective exploration and design ofnovel molecules in drug development.

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P03 ケムインフォマティクス・機械学習 / AI による創薬研究 Cheminformatics, machine learning, AI-based drug discovery

P03-10Explainable AI Analysis for Accelerating Drug Discovery ofSequence Modalities
Taisei KAKIBUCHI*, Masato KITAJIMA, Shigeki FUKUTA, Tatsuya ASAI,
Junya OHORI, Hiroshi YANO, Takuya YOSHIDA, Yuzo SHIMADA
Fujitsu( * E-mail: t.kakibuchi@jp.fujitsu.com )Methods for identifying hit compounds in drug discovery often involve screening large libraries across various
sequence modalities, such as phage display for antibody drugs, SELEX for oligonucleotide therapeutics, and mRNA
display for peptides. In such screenings, diversity in sequence modalities is crucial for increasing the success rate of
drug discovery.
We have developed a novel, explainable AI technology to efficiently identify diverse sequences that have highbinding affinities to target proteins. This technology not only predicts hit sequences but also elucidates key binding
motifs. Utilizing this key motif information allows us to identify peptides that bind to multiple distinct sites on thetarget protein, as well as peptides with the ability to bind multiple proteins. Our technology integrates exhaustive motif
extraction and combinatorial search capabilities, empowered by our proprietary explainable AI framework, ʻWide
Learning.ʼ
Currently, we are applying this technology to data from PeptiDream Inc., our research partner specializing in peptide
screening, to assess its efficacy in generating a more diverse set of hit peptides.
In this poster, we introduce the architecture and functionalities of this new explainable AI technology and showcase
its applicability in drug discovery. In the future, this technology are expected to contribute to improved design in the hitto-lead phase as well as screening, further increasing the success rate of drug discovery.

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P03 ケムインフォマティクス・機械学習 / AI による創薬研究 Cheminformatics, machine learning, AI-based drug discovery

P03-11
Investigation on the Transformer’s learning process of chemical
structure comprehension
Yasuhiro YOSHIKAI*, Tadahaya MIZUNO, Hiroyuki KUSUHARA
Laboratory of Molecular Pharmacokinetics, Graduate School of Pharmaceutical Sciences, The
University of Tokyo( * E-mail: yoshikai-yasuhiro701@g.ecc.u-tokyo.ac.jp )Recent years have seen great advancement on deep learning-based cheminformatics, and one of the most researched areas is
to treat chemical structure as string representation like SMILES with natural language processing (NLP) models to generate novel
structure of molecules or to generate descriptors. It is, however, unclear how such NLP models recognize chemical structures, making
the model difficult to be socially accepted or studied for more improvement. To clarify this black box, we investigated the processof descriptor generation by the Transformer model, one of the most commonly used models in NLP. We focused on descriptor
generation in which the model needs to recognize and understand various structures, rather than specific structures that serve limited
purposes.
We trained the Transformer model by the translation task between two different representations of molecules called SMILES, andthe intermediate representation during the translation process was used as the descriptor. To evaluate the relationship between models
and descriptors, we prepared models at various learning progress and compared their properties.
When agreement between the input (before translation) and the output (after translation) about the partial structures and theoverall structures was compared, we found that the partial structures were able to be easily decoded at early phase of training,
while the overall structures needed more training to be decoded perfectly, indicating that partial structure is easy for the model tounderstand and incorporate into the descriptor, compared to overall structures. We also compared the performance on molecularproperty prediction tasks about descriptors generated by the model at various steps of training. As a result, the prediction performance
showed no change by training, and it was found that information of molecules required for property prediction is included in thedescriptor at the initial phase of the training. Furthermore, we found that the accuracy of translation sometimes stagnates for a whileduring the training, and subsequently surges rapidly. Our research on this phenomenon clarified that the chirality of molecules isdifficult for the Transformer model to understand and sometimes causes this stagnation. We also found that the stagnation can beprevented by a specific structural modification of the model. All these results were replicated by several types of representation ofmolecules, indicating that our findings can be attributed to innate properties of compound structures and the Transformer model,
rather than grammatical problems of the representation.
These findings are expected to contribute to reveal a part of the black box in the Transformer treating chemical structures, and todevelop better methods for descriptor generation.

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P03 ケムインフォマティクス・機械学習 / AI による創薬研究 Cheminformatics, machine learning, AI-based drug discovery

P03-12
Recent developments of FMODB:
Enhancement of search functionality
Kikuko KAMISAKA*, 1, Chiduru WATANABE1, Norihiko TANI1, Daisuke TAKAYA2,
Teruki HONMA1
1

RIKEN Center for Biosystems Dynamics Research, 2Graduate School of Pharmaceutical
Sciences, Osaka University( * E-mail: kikuko.kamisaka@riken.jp )The fragment molecular orbital (FMO) method can quantitatively and accurately estimate the inter-fragmentinteraction energy (IFIE) between and within molecules of biopolymers. Additionally, pair interaction energy
decomposition analysis (PIEDA) can handle charge transfer interactions such as hydrogen bonds and dispersion forces
such as CH-π, π-π, and cation-π interactions with high accuracy; it is expected to be applied to drug discovery research [1]and structural biology. Under the activity of the FMO Drug Design (FMODD) Consortium, our group has accumulated
and released a set of data based on input and output files of each FMO calculation via FMODB (https://drugdesign.
riken.jp/FMODB/) [2]. Thus, researchers worldwide can quickly obtain FMO data with constant quality calculated bycomputational chemists through FMODB.
In this study, we will introduce the latest data accumulation status in the database and newly implemented featuresin a web interface. Regarding the registration status, we have already executed FMO calculations for about 6,500 PDB
entries (X-ray crystallographic structures) of apo protein structures and are currently proceeding with registration to the
database. We already provide basic search functions such as ID search, Keyword search, BLAST search, and Ligand
structure search. As the number of registered data increases, the need for more advanced and detailed search capabilities
becomes essential to find desired computational data, such as one with a complete set of calculation conditions. In
addition to the basic search function, the new functions that enable users to search for several types of information
can be combined to narrow down the desired FMO calculation data. The available search condition was additionallybasic information (target biomolecule type, ligand, article, and registration date), modeling method (input structure,
optimization, constraint, protonation, and complementation), FMO calculation method (calculation level, fragment
information, the software used, and the ligand charge), and ligand binding energy (IFIE and PIEDA). Such advanced
searches also facilitate IFIE/PIEDA comparisons between hit structures, as FMODB has a convenient IFIE/PIEDA
analysis function via the web interface.
Acknowledgment
The authors thank Dr. Kazumi Tsuda and Dr. Shu Koyama of Science & Technology Systems, Inc., for technicalsupport. This research was done in activities of the FMO Drug Design Consortium, https://fmodd.jp/top-en/. This
research was partially supported by Platform Project for Supporting Drug Discovery and Life Science Research (Basisfor Supporting Innovative Drug Discovery and Life Science Research (BINDS)) from AMED under Grant Number
JP23ama121030. The results of FMO calculations were obtained using the Fugaku (project IDs: hp230131 and ra000017).
[1] Watanabe, C. et al., J. Phys. Chem. Lett. 2023, 14, 15, 3609-3620.
[2] Takaya, D. et al., J. Chem. Inf. Model. 2021, 61, 2, 777–794.

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P03 ケムインフォマティクス・機械学習 / AI による創薬研究 Cheminformatics, machine learning, AI-based drug discovery

P03-13Composing Property-Specific SMILES Language Models forMulti-Objective Drug-Like Molecule Generation
Shuya NAKATA*, Yoshiharu MORI, Shigenori TANAKA
Graduate School of System Informatics, Kobe University( * E-mail: shuyanakata@gmail.com )Drug discovery involves the identification of molecules that fulfill multiple criteria, such as activity against target
molecules, favorable pharmacokinetic properties, and synthetic accessibility. Generative models offer a promising way
to obtain candidate molecules with desired properties efficiently. However, these models face the challenge of exploring
the vast drug-like chemical space while optimizing specific molecular properties. For example, models trained withactive molecules against a specific target tend to generate compounds similar to those in the training set, thereby limiting
the possibility of further optimization and the discovery of novel chemotypes.
In this study, we propose a new approach to overcoming this limitation by composing prior and property-specific
SMILES language models. The prior model is trained using diverse drug-like molecules to cover a wide range ofsynthetically accessible chemical space. In addition, the property-specific models are fine-tuned using smaller datasetsof experimentally determined compound activities to capture the structure-activity relationships. The composition ofthese models involves the weighted sum of logits, mitigating the bias inherent in each property-specific dataset by
subtracting the logits of the marginal distribution from the logits of the conditional distribution. This method allows thesimultaneous optimization of multiple molecular properties while preserving the structural diversity and drug-likelinessof the prior. Furthermore, the generation of molecules can be controlled by adjusting the weight of each model atinference time without retraining.
We investigate the effectiveness of the proposed method by generating molecules with selective activity profiles
against pre-defined sets of kinases. Evaluation of the generated molecules using activity prediction models, moleculardocking, and ligand similarity analysis shows that the proposed method is advantageous for generating molecules withthe desired predicted activity profile while preserving as much of the structural diversity and drug-likeliness of the prioras possible.
The details of the data, method, and results will be discussed in the presentation.

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P03 ケムインフォマティクス・機械学習 / AI による創薬研究 Cheminformatics, machine learning, AI-based drug discovery

P03-14
Enhancing Model Learning and Interpretation Using Multiple
Molecular Graph Representations for Compound Property andActivity Prediction
Apakorn KENGKANNA*, Masahito OHUE
Department of Computer Science, Tokyo Institute of Technology( * E-mail: kengkanna@li.c.titech.ac.jp )Graph neural networks (GNNs) have demonstrated excellent performance in compound property and activity
prediction by effectively learning complex molecular structures. One major factor influencing both model learningand interpretation of GNNs is the molecular graph representation. While atom-level molecular graphs are commonlyused because of their similarity to natural topology, they may not fully express important substructures or functionalgroups which significantly impact molecular properties. Additionally, their interpretation at atom-level features are
sometimes inconsistent and only partially compatible with chemical intuition. Consequently, recent research proposes
alternative representations employing reduction techniques to integrate higher-level meaningful chemical information
into the nodes and leverages both representations for model learning. However, different representations may not beapplicable for all tasks, and there is a lack of studies regarding different molecular graph representations on model
prediction. For interpretation, most research focuses solely on individual prediction at the atom-level with little analysis
of interpretation on different molecular graph representations. Therefore, this study introduces multiple molecular graphrepresentations, including Atom, Pharmacophore, JunctionTree, and FunctionalGroup, and investigates their effects
on model learning and interpretation from several perspectives. The results indicate that models combining atom-level
and reduced molecular graph yield superior performance in most tasks. Furthermore, the interpretation results fromcombination models provide comprehensive significant features and potential substructures consistently aligning withbackground knowledge. The concept of multiple molecular graph representations and diverse interpretation perspectives
has broad applicability across tasks, graph architectures, and explanation methods, enhancing model learning andinterpretation for relevant applications in drug discovery.

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P04 データサイエンス Data science

P04-01
Japanese food ontology and knowledge graph as its application
Chihiro HIGUCHI*, 1, Agustin MARTIN 1, Mai INOUE1, Masaki YAMAMOTO1,
Michihiro ARAKI1, Kenji MIZUGUCHI1, 2
Artificial Intelligence Center for Health and Biomedical Research (ArCHER), National Institutesof Biomedical Innovation, Health and Nutrition (NIBIOHN), 2Institute of Protein Research (IPR),
Osaka University

1( * E-mail: higuchi@nibiohn.go.jp )Nutrition is a key component of good health. However, many individuals may face challenges in obtaining sufficientnutrition due to factors that affect their health, such as allergies. To conduct nutritional research that yields reliable
and comparable results, it's necessary to use uniform terminology and appropriate food descriptions. Consequently,
there is a demand for a computer-readable Japanese food ontology that can accurately represent the characteristics andrelationships of various foods.
The National Health and Nutrition Survey (NHNS) is a data collection project aiming to assess the nutritional
intake and lifestyle of the Japanese population, with the ultimate goal of improving their health. The NHNS involves
physical measurements and blood tests of 3,412 households in 300 districts across Japan. The survey encompasses
1,630 Japanese food items, classified into three tiers: large (e.g., cereals, legumes, vegetables), medium (e.g., rice and its
processed products, wheat and its processed products), and small (e.g., flour products, bread products).
We employed the Web Ontology Language (OWL) to describe the NHNS data, utilizing its hierarchical classification
scheme and appropriate Uniform Resource Identifiers (URIs). This ontology is published as an alpha version of
FGNHNS on BioPortal (https://bioportal.bioontology.org/ontologies/FGNHNS). Our future work includes enhancing
the ontology by adding Wikidata information, linking with FoodOn, integrating with the Standard Tables of Food
Composition in Japan, linking with the Agricultural Vocabulary System, and incorporating food allergy information.
As an application example of this ontology, we are investigating the possibility of constructing a knowledge graphthat connects food and diseases through intestinal bacteria. Besides using existing database information, we are also
exploring information extraction from a Large Language Model. This paper presents our current progress and thechallenges we face in this research.

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P04 データサイエンス Data science

P04-02Development of efficient process to extract SAR knowledge fromliterature by using CCR algorithm
Kosuke TAKEUCHI*, Takayuki SERIZAWA, Takahisa SHIMIZU, Shuhei UESUGIMedicinal Chemistry Research Laboratories, DAIICHI SANKYO CO., LTD.

( * E-mail: takeuchi.kosuke.pm@daiichisankyo.co.jp )In small molecule drug discovery research, literature (academic papers and patents) is a rich source to extract
background information, knowledge and intuition for new idea. Since there are both commercial and public databases
for literature information, researchers can easily access and analyze them for their purpose. On the other hand, if you
are trying to access brand new patents, you have to extract the information from scratch because external databases takesome time to store it as structured data.
Patent analysis process mainly consists of two parts to get structured information from original documents. The firstone is text mining. It is used to extract ID, compound structures and their properties and to assign correct relationshipbetween them. The other one is SAR analysis to extract information according to your objective.
In this presentation, we will show you exemplified use cases about efficient process to extract SAR knowledge fromliterature. In text mining part, LeadMine [1] was used to accelerate to obtain structured data. In SAR analysis part,
CCR algorithm [2] was used to extract common core and corresponding substituent information.
[1] LeadMine; Chemical text mining package for identification and annotation; NextMove software https://www.
nextmovesoftware.com/[2] Naveja JJ, Vogt M, Stumpfe D, Medina-Franco JL & Bajorath J. Systematic extraction of analog series from large
compound collections using a new computational compound-core relationship method. ACS Omega 4, 1027-1032,
2019.

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P04 データサイエンス Data science

P04-03
Classification and Comparison of Gene-Disease Associations
using BioBERT and PubMedBERT on GeneRIF Data
Atsushi MIDORIKAWA*, Yuuki SAKURAI, Toshiro KIMURA, Kozo KAWAHARAWorldFusion Co,. Ltd.

( * E-mail: midorikawa@w-fusion.co.jp )In life science research, the classification of Gene-Disease associations plays a crucial role in functional annotationand evidence discovery. We present our investigation using AI-based classification with BioBERT and PubMedBERT
models on GeneRIF data, provided by the National Library of Medicine. GeneRIF contains concise functionalannotations for genes, along with references to published papers supporting those annotations.
Our study involved formulating 11 hypotheses as premises based on GeneRIF texts, and we utilized the MNLI
approach to classify Gene-Disease associations. Our findings show that 60 percent of the entailment judgments made byboth BioBERT and PubMedBERT were in agreement, while others differed. The comparison between the two models
revealed intriguing insights and potential applications in life science research.
In this abstract, we briefly outline our research methodology, highlight the commonalities and disparities observed in
the classification results, and discuss the implications of these findings. Moreover, we plan to extend our investigation
by incorporating judgments from ChatGPT-4 in our discussion.
This study contributes to our comprehensive Life Science Knowledge Bank (LSKB) platform and provides valuable
insights into the classification of Gene-Disease associations using state-of-the-art AI models.

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P04 データサイエンス Data science

P04-04TogoID: ID conversion service as a basis for life science
database integration
Shuya IKEDA*
Database Center for Life Science( * E-mail: sikeda@dbcls.rois.ac.jp )In bioinformatic analysis, conversion between related database (DB) IDs is often required. While several ID
conversion tools exist, they often suffer from limitations like restricted target DB categories, undisclosed data sourcing
methods, and infrequent updates.
We have developed TogoID (https://togoid.dbcls.jp/) as an ID conversion service to solve these problems. TogoID
covers 94 types of IDs (as of August 2023) in a wide range of categories such as genes, proteins, chemical compounds,
pathways, diseases, and genetic variants. TogoID can convert IDs not only within a single category, but also across
categories. For example, IDs of proteins can be converted to IDs of pathways in which the proteins function. We
describe the semantic relationships between IDs using a newly developed ontology. The web interface displays therelationship between IDs based on this ontology. On the web interface, converted IDs can be seamlessly converted
to another type of IDs, enabling users to explore indirect relationships among DBs. The converted IDs can be copied
directly to the clipboard or downloaded in CSV or TSV format. The ID correspondence data is extracted from each DB's
RDF data, API, or flat files. The programs used to obtain the data are available in a GitHub repository (https://github.
com/dbcls/togoid-config), and the data are updated weekly using the programs.
As new features, we intend to introduce a function to display labels of entities represented by IDs, and a function
to convert labels to IDs. In addition, we plan to allow multiple definitions between a single pair of datasets. This will
enable, for example, distinction between the relationships “glycan is attached to protein” and “glycan is processed byprotein”.
The maintenance of correspondence between IDs is the basis of DB integration and facilitates knowledge discovery
across DBs. In this poster presentation, we will showcase our web application, discussing its usage examples andpotential enhancements.

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P04-05Prediction of Xanthine Oxidase Inhibitors using Graph Neural Network
MARTIN -*, 1, 2, 3, Xingran WANG1, Reiko WATANABE1, 3, Kenji MIZUGUCHI1, 2, 3
Laboratory for Computational Biology, Institute for Protein Research, Osaka University,
3-2 Yamadaoka, Suita, Osaka 565-0871, Japan, Osaka University, 2Graduate School of
Pharmaceutical Sciences, Osaka University, 1-6 Yamadaoka, Suita, Osaka 565-0871, Japan,
Osaka University, 3Artificial Intelligence Centre for Health and Biomedical Research, National
Institutes of Biomedical Innovation, Health and Nutrition (NIBIOHN), 7-6-8, Saito-Asagi, Ibaraki,
Osaka 567-0085, Japan, National Institutes of Biomedical Innovation, Health and Nutrition(NIBIOHN)
1( * E-mail: martin@phs.osaka-u.ac.jp )Xanthine oxidase (XO) is responsible for converting purines into uric acid, the accumulation of which in the blood
can lead to gout. Allopurinol and febuxostat are Food and Drug Administration-approved inhibitors that lower uric acidlevels by blocking XO, but may entail side effects. Therefore, developing new anti-gout drugs remain crucial. Herein,
we constructed state-of-the-art binary predictive models using graph neural networks (GNNs) to differentiate between
XO inhibitors exhibiting high and low activity based on the molecular graph structure, where atoms are depicted as
nodes and bonds as edges (Figure). We manually and carefully generated a large-scale original dataset consisting of
3,953 compounds (38% high activity and 62% low activity) using 235 scientific and 44 patent publications publishedbetween 1964 and 2023. We applied several GNN architectures, including attentive fingerprint (AttentiveFP) [1], graphisomorphism network (GINEConv) [2], and graph attention networks (GATv2Conv) [3], and achieved satisfactory
performance with AUC-ROC as a primary metric. Furthermore, our models have yielded interpretation and valuable
chemical insights, such as the atom or fragment importance scores for high activity.
[1] Xiong, Z. et al. Pushing the boundaries of molecular representation for drug discovery with the graph attention
mechanism. J. Med. Chem. 63, 8749–8760 (2020).
[2] Hu, W. et al. Strategies for pre-training graph neural networks. In International Conference on Learning
Representations (2020).
[3] Brody, S., Alon, U. & Yahav, E. How attentive are graph attention networks? International Conference on Learning
Representations (2021).

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P04-06
TogoDX/Human: An application for integrated explorationof human-related data
Yuki MORIYA*
Database Center for Life Science, ROIS-DS( * E-mail: moriya@dbcls.rois.ac.jp )In the field of life sciences, a vast number of databases have been constructed to record knowledge accumulated
through experiments, and data-driven research using these databases is now being actively conducted. However,individual databases do not necessarily meet researchers' needs for integrated use, and researchers often spend a lot oftime and effort to search, collect, process, integrate, and analyze data in different formats across multiple databases andresources. Especially in human research where the data is huge and diverse, the effort is immeasurable.
To solve this problem, we have developed a new web application, TogoDX/Human (https://togodx.dbcls.jp/human/).
TogoDX/Human is a web application designed for one-stop exploration of human-related data collected and integrated
from various databases. It is now possible to explore data from databases in diverse fields such as genes, compounds,
diseases, and so on, by various ʻattributesʼ (expression patterns, molecular localization, disease classification, etc.), andto extract data by combining the attributes of interest while understanding the features and relationships of the data. In
this presentation, we will introduce the features and recent updates of the application.

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P04 データサイエンス Data science

P04-07
Elucidation of CLM-based deep generative model with high translation
accuracy as a generator of virtual chemical libraries
with diverse structures
Shumpei NEMOTO*, Tadahaya MIZUNO, Hiroyuki KUSUHARA
Graduate school of pharmaceutical sciences, The University of Tokyo( * E-mail: nemo88@g.ecc.u-tokyo.ac.jp )Virtual Screening (VS) plays a pivotal role in modern drug discovery by enabling swift evaluation of a vast array ofpotential drug candidates through in silico analysis. After Gomez-Bómbarelli’s 2016 study, natural language processing
models fed with SMILES representations, called chemical language models (CLM), have been widely studied[1]. Notably, deep generative models within this framework facilitate easy molecular structure generation and theestablishment of a Virtual Chemical Library (VCL). However, prevalent structure generation techniques primarily serve
end-to-end goals, consequently causing the VCL to exhibit bias towards specific structures aligned with the designatedpurpose. Consequently, Virtual Screening (VS) conducted within such a CLM-constructed VCL explores only a limited
fraction of the vast chemical space potentially available.
To address this issue, we aimed to develop a deep generative model capable of generating a compound set ofdiverse structures. We hypothesize that a CLM with high translation accuracy bridges numbers and structures andworks as a generator of VCL with diverse structures. Focusing on two model architectures (Encoder-Decoder modelwith GRU, Transformer-based generative model) and two tokenization methodologies (character-based and simplified
feature learning), we devised total four CLMs employing 30M ZINC dataset as input. Subsequently, compound sets
were generated by inputting uniform random numbers to each model, followed by decoding. The evaluation of thesecompound sets hinged on established metrics employed in MOSES [2]. Furthermore, we assessed the correlation
between random inputs and the structural similarity of generated compounds and investigated the optimal model for
generating diverse structures.
The combination of GRU and simplified feature learning showcased the highest translation accuracy, with aremarkable approximate 99% accuracy in perfectly aligning input and output. Regarding the properties of the generated
compound set, the structural validity rate of up to 34% was achieved, accompanied by a 97% non-repetition rate. Thishighlights the adaptability of the deep generative model in yielding structures from uniform random inputs. Furthermore,compounds decoded from random numbers displaying positive correlations with the latent representation of a specificcompound exhibited structural similarity to the compound. These results indicate that the properties of random numbers
correspond to those of compound structures and the established generative model works as a VCL generator. Currently,
we are tackling with VS within a VCL generated by the established model to investigate its feasibility.
[1] Rafael Gomez-Bómbarelli; et al, Automatic Chemical Design Using a Data-Driven Continuous Repreesntation of
Molecules, ACS Central Science, 2018, 4, 268-276[2] Polykovskiy Daniil; et al, Molecular Sets (MOSES): A benchmarking platform for molecular generation models,Front. Pharmacol., 2020, 11― 228 ―

P04 データサイエンス Data science

P04-08Advancements in the RDF portal: Enriching cheminformatics insights
Shuichi KAWASHIMA*
Database Center for Life Science, Research Organization of Information and Systems( * E-mail: kwsm@dbcls.rois.ac.jp )We will present the recent progress of the RDF portal and its contributions to the field of cheminformatics. Initiatedin 2015, the NBDC RDF portal was established to address the accessibility challenges of RDF (Resource Description
Framework) data in the life sciences. Instead of merely collecting RDF data, guidelines and a review process wereintroduced to enhance data interoperability, resulting in the establishment of a high-quality RDF data foundation. This
achievement was made possible through the collaboration of domestic RDF developers, including those participatingin the Database Integration Coordination Program supported by Life Science Database Integration Project of Japan
Science and Technology Agency. Furthermore, starting from the previous fiscal year, the scope of RDF registration has
been expanded. In addition to submitted data, publicly available RDF data, such as UniProt and PubChem, as well as
RDF data developed by DBCLS, have been incorporated. This change is anticipated to facilitate the development ofapplications in the field of cheminformatics that leverage the characteristics of RDF to the maximum extent.
From the fiscal year 2023, service provision shifted from NBDC to DBCLS, and as a result, the name has changed
to the RDF portal (https://rdfportal.org). Currently, the RDF portal hosts a diverse range of datasets, including those
relevant to cheminformatics, such as PubChem, ChEMBL, ChEBI, PDB, and Open TG-GATEs.
This portal contributes to cheminformatics research by improving data accessibility and quality, promoting
the utilization of RDF data. Looking forward, the RDF portal is expected to play a significant role in application
development by cheminformatics researchers.

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P04 データサイエンス Data science

P04-093D packing for enhancing extended depth of fieldmicroscopy images
Thanawat TANGPORNPISIT*, 1, Kenji SATOU2
1

Graduate School of Natural Science and Technology, Kanazawa University, 2Institute ofTransdisciplinary Sciences for Innovation, Kanazawa University( * E-mail: thanawat.tangpornpisit@gmail.com )Cancer screening and diagnosis of various diseases are done by examining cells under the microscope, cutting
resources required for microscopic workflow contribute to the health challenge especially in the low to middle
income countries. In this work, we proposed the novel application of compress and decompress of feature space froma successful deep learning network for monocular depth estimation in the field of self-driving car to the Extended
Depth of Field (EDoF) problem. The task is to combine the limited field of view and depth of view images taken frommicroscope at different focal plane to produce enhanced depth of field and spatial resolution of the fused image. We
introduce a new convolutional neural network EDoF-CNN-Pack based on encoder-decoder architecture to perform
image fusion. The encoder and decoder parts preserve fine detail through symmetrical packing and unpacking layers.
The skip connection also utilized to improve learning ability of the model. We evaluated our approach on Fraunhofer
dataset which used low-cost microscope devices produced with intent to deploy to low to middle income countries. The
dataset contains liquid-based cervical cytology samples, 144 EDoFs images are provided, and each image is generated
from aligned 5 images stack. In the figure showing the experimental result, left side is the ground truth, middle is theresult of a state-of-the-art method, and the right side is our result. It illustrates that our approach preserved more finedetail compared to the state of the art method. Performances of two methods were scored and compared in MSE, RMSE,
SSIM, PSNR. The state-of-the-art method scored MSE=0.00289 (lower is better), RMSE=0.06982 (lower is better),
SSIM=91.645 (higher is better), and PSNR=29.019 (higher is better) in average. In our result, all of the scores were
improved: MSE=0.00285, RMSE=0.06865, SSIM=91.868, and SSIM=91.645, and PSNR=29.409 in average. Since weonly applied the technique to the grayscale images in this research, we believe that there is still room of improvement
before applying to RGB images.

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P05 分子ロボティクス Molecular Robotics

P05-01Long-range energy transfer using DNA-scaffolded BRET system
Yuki MINAMIDE*, Koichi TANIMOTO, Fumiaki TAKANO, Tomoya NIKI,
Shiori TABANA, Akinori KUZUYADepartment of Chemistry and Materials Engineering, Kansai University( * E-mail: k119802@kansai-u.ac.jp )Attempts have been made to construct an energy transfer system by placing donors, mediators, and acceptors on a
DNA scaffold [1]. However, since only bulk excitation light sources can be used, all fluorescent dyes in the system were
excited simultaneously and no net energy transfer can be observed.
In this study, we focused on Bioluminescence Resonance Energy Transfer (BRET) [2]. BRET is a phenomenon
in which the luminescence energy of a substrate is transferred to nearby fluorescent molecules. We have developed aDNA-linked BRET system by modifying one of the two complementary DNA strands with NanoLuc [3], a luciferase,
as a donor, and a fluorescent dye as an acceptor on the other DNA strand. In this system, the color can be freely changed
by changing the type of fluorescent dye. It has also been confirmed that energy transfer does not occur in this system
when the donor-acceptor distance is 10 bp.
We attempted to directly observe energy transfer by modifying multiple fluorescent dyes within the DNA moleculethat serving as a linker and Alexa Fluor 594 as an acceptor at the end of DNA. In addition, systems with DNA-binding
fluorescent molecules (SYBR Green I and SYTO 9) were investigated. As a result, the system was able to transfer
energy more efficiently than the system with FAM dyes as mediators.
Long range energy transfer was also investigated in the system using DNA-binding molecules as mediators.
Specifically, the ratio of acceptor to donor luminescence intensity was calculated for the 42-, 63-, and 84-bp systems.
The results showed that the acceptor-derived luminescence was slight but reproducibly observable even in the 84 bp
system.
[1] X. Zhou, S. Lin, H. Yan J Nanobiotechnol. 20, 257 (2022).
[2] S. Sihuai, et al., Int. J. Mol. Sci. 17, 1704 (2016).
[3] A. S. Dixon, et al., Anal. Chem. 11, 400-408 (2016).

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P05 分子ロボティクス Molecular Robotics

P05-02
Regulation of spatiotemporal formation of DNA droplets
Shogo KAI*, 1, Yusuke SATO2
1

Sch. Comp. Sci. Syst. Eng., Kyutech, 2Grad. Comp. Sci. Syst. Eng., Kyutech( * E-mail: kai.shogo203@mail.kyutech.jp )Droplets, formed via liquid-liquid phase separation (LLPS), are spatiotemporally regulated in living cells. Suchdroplets can be used as components of artificial cells and molecular robots. Although researchers have constructedvarious artificial droplets, spatiotemporal regulation of their formation, especially their formation and position, remains
technically challenging. Here, we propose a DNA nanotechnology-based method to regulate spatiotemporal droplet
formation. We employed bistable molecular circuits that generate patterns of single stranded DNAs (ssDNAs) overtime. Based on the previously reported method (A. S. Zadorin, et al. Nature Chemistry 9, 990-996(2017)), three typesof enzymes (polymerase, nickase, and exonuclease) are used for, production, inhibition, and degradation of ssDNAs
generated depending on the concentration gradient of inhibitor DNA strands. We designed the sequence of ssDNA
generated by the reactions to induce the LLPS of DNA nanostructures, enabling spatiotemporal formation of DNA
droplets. It was observed that the designed ssDNA could induce the LLPS at the optimal reaction temperature (45°C)
for the bistable circuit. In addition to the experimental demonstration, we aim to simulate the system using numericalsimulation based on the Michaelis-Menten equation to represent the bistable circuit behavior and Cahn-Hilliard
equation to represent the LLPS. In the presentation, we will present design details of our system and discuss theintercomparison of experimental and simulation results.

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P05 分子ロボティクス Molecular Robotics

P05-03Phase-separated microstructures with sequence-designed
DNA and peptides
Hiroaki OHNO*, 1, Sato YUSUKE2
Sch. Comp. Sci. Syst. Eng., Kyutech, 2Grad. Sch. Comp. Sci. Syst. Eng., Kyutech

1( * E-mail: oono.hiroaki439@mail.kyutech.jp )Micrometer-sized droplets composed of biopolymer molecules, including DNA, RNA, peptides and proteins, areassembled inside cells by a phenomenon called liquid-liquid phase separation (LLPS). The droplets are recognized asmembrane-free organelles that play essential roles in various cellular functions. The strength of molecular interactions isimportant in LLPS. DNA nanotechnology offers a feasible approach to construct controllable LLPS systems because theinteractions can be tuned by sequence design [1]. In recent years, DNA-based LLPS has attracted much attention due toits high programmability and designability. A recent study reported that the DNA-based LLPS system can be applied tocancer diagnosis based on the sequence-specific interaction [2]. Although DNA has advantages in the programmability
that can be applied to computation, it is difficult to add other functions. The combination of DNA and peptides would
allow the implementation of additional functions to the DNA-based LLPS system, because peptides and/or proteins playvarious functions in cells. In this work, we aim to establish the hybrid LLPS system composed of sequence-designed
DNA and peptides. As a first step, we designed a Y-shaped DNA nanostructure (Y-motif) [3] and investigated the effectsof the addition of poly-L-lysine (PLL) on the DNA droplets’ behavior depending on the solution temperature and thecharge ratio of negatively charged DNA and positively charged PLL (N/P ratio). We have so far found that the DNA
droplets mixed with PLL have higher temperature stability and the sequence specificity of DNA is preserved, suggestingthat the droplets composed of the Y-motifs and PLL are not formed by electrostatic aggregation alone. We will present
these experiments and discuss the results obtained from the experiments and their mechanisms.
References(1) J. SantaLucia,Jr.(1998) PNAS., 95(4) 1460-1465. DOI: https://doi.org/10.1073/pnas.95.4.1460.
(2) J. Gong, N. Tsumura, Y. Sato, M. Takinoue.(2022) Adv. Funct. Mater., 32, 2202322. DOI: 10.1002/adfm.202202322.
(3) Y. Sato, T. Sakamoto, M. Takinoue. (2020)Sci. Adv., 6, eaba3471. DOI: 10.1126/sciadv.aba3471.

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P06 創薬応用 Drug Discovery Application

P06-01
Miné Tools Common:
Open Platform to Accelerate Drug Discovery
Masataka KURODA*, 1, 2, Chihiro HIGUCHI1, Mari Nogami ITOH1, 3, Chioko NAGAO4,
Kenji MIZUGUCHI1, 4, Yayoi NATSUME-KITATANI1, 5
1
Artificial Intelligence Center for Health and Biomedical Research, National Institutes ofBiomedical Innovation, Health and Nutrition, 2Discovery Technology Laboratories, Mitsubishi
Tanabe Pharma Corporation, 3Department of Pharmaceutical Science, Osaka University,
4
Institute for Protein Research, Osaka University, 5Institute of Advanced Medical Sciences,
Tokushima University( * E-mail: m-kuroda@nibiohn.go.jp )Miné Tools Common (Miné) is a platform that allows users to easily experience various analysis programs andprediction models developed in the “Construction of a Drug Target Discovery Platform” project based on the frameworkof the “Public/Private R&D Investment Strategic Expansion PrograM” (PRISM) program. Miné was opened to the
public this spring and is currently accepting applications from potential users. Medical data and existing knowledge
have been collected so far for the purpose of data-driven discovery of novel drug targets (proteins and other biologicalmolecules on which drugs act) in the two target diseases; 1) idiopathic pulmonary fibrosis (IPF) which is intractabledisease with a poor prognosis, 2) lung cancer. These include data such as medical texts and academic papers, andparticipating institutions had developed AI tools such as natural language processing programs that automatically extract
medical and biological terminology from such data, and novel analysis programs for drug target search from the data.
Miné is based on Garuda, which is developed by the Systems Biology Institute, a non-profit organization, and each AI
tool is mounted as its own form, called as “Gadget”. Miné also allows Gadget-to-Gadget integration, whereby results
calculated by one Gadget can be used as input for another Gadget. Currently, fifteen Gadgets are included in Miné, andnew some Gadgets will be added and become available. Miné's structure, current status, operation methods, and futureplans will be introduced.
https://www.nibiohn.go.jp/mine/― 234 ―

P06 創薬応用 Drug Discovery Application

P06-02
Application of AI-AAM, an in silico scaffold hopping method, to thedrugs for rare and intractable diseases
Mao TANABE*, 1, Ryuichi SAKATE1, Jun NAKABAYASHI2, Kyosuke TSUMURA2,
Shino OHIRA2, Kaoru IWATO2, Tomonori KIMURA1
1
Center for Intractable Diseases and ImmunoGenomics Research, National Institutes ofBiomedical Innovation, Health and Nutrition, 2Analysis Technology Center, FUJIFILM corporation( * E-mail: mtanabe@nibiohn.go.jp )In the field of rare and intractable diseases (RIDs), new drug development is difficult and drug repositioning (DR) is a key
method to improve this situation [1]. To obtain improved alternatives to a drug used in a RID, we developed a new method for finding
DR candidates utilizing virtual screening, which integrates amino acid interaction mapping into scaffold-hopping (AI-AAM) [2].
Previously, using a spleen associated tyrosine kinase (SYK) inhibitor as a reference to evaluate the method, we successfully obtained
a candidate compound with different scaﬄod which was experimentally validated to preserve the pharmacological activity [3].
In this study, we applied this method to five drugs registered in both DDrare [4] and DUD-E [5], as references, and obtained 144
DR-candidate compounds with diverse structures [6]. Among these, 31 compounds were known to target the same proteins as theirreference compounds, and other 113 compounds were known to target different proteins. We found that, for the former compounds,
AI-AAM could dominantly select functionally more similar compounds among the compounds with the same targets; for example,
more agonists were identified than antagonists using an agonist as reference. Thus, these selected compounds may represent
reasonable alternatives to their reference compounds. Moreover, the latter compounds were presumed to bind to the targets of theirreferences as well. These new “compound-target” interactions might also be an example of the successful scaffold-hopping and theinformation provides DR candidates that could be utilized for future drug development.
[1] R. Sakate & T. Kimura. Drug repositioning trends in rare and intractable diseases. Drug discovery today: 7:1789-1795, 2022.
[2] K. Tsumura., et al. , A newly developed method based on AI-oriented amino acid interaction mapping (AI-AAM) for efficientvirtual scaffold hopping, CBI Annual Meeting 2018, P2-02.
[3] J. Nakabayashi., et al. , AI-AAM. Lead-to-lead scaffold hopping for drug repositioning and drug rescue, CBI Annual Meeting
2022, P8-14.
[4] DDrare: Database of Drug Development for Rare Diseases,
https://ddrare.nibiohn.go.jp/[5] M. M. Mysinger., et al. , Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking. J
Med Chem. 55: 6582-6594. 2012[6] M. Tanabe., et al. , A novel in silico scaffold-hopping method for drug repositioning in rare and intractable diseases. Bioarchive,
2023
doi: https://doi.org/10.1101/2023.07.03.547598― 235 ―

P06 創薬応用 Drug Discovery Application

P06-03Development of a model for predicting binding poses of ROR γ
inhibitors using machine learning with ligand
coordinate profile
Kentaro KAWAI*, Daiki FUJIOKA, Yukiko KARUO, Atsushi TARUI, Kazuyuki SATO, Masaaki OMOTE
Faculty of Pharmaceutical Sciences, Setsunan University( * E-mail: kentaro.kawai@pharm.setsunan.ac.jp )Purpose: Although scoring functions of docking programs are used to predict and evaluate binding poses, they are
not always useful in predicting binding poses. Therefore, in this study, we develop and validate a prediction model of
RMSD value, which represents the deviation from the co-crystal structure, for RORγ inhibitors.
Methods: RORγ co-crystal structures registered in PDB were obtained, and co-crystal structures were selectedby clustering based on ligand structures. A docking program was used to generate binding poses for each co-crystal
structure, which were used as training data. Two descriptors of binding poses were used: PLIP, which is an existing
method, and LCP, which was devised in our laboratory. The objective variable was the RMSD value, which represents
the difference in distance from the co-crystal structure. Four machine learning methods were used: k-Nearest Neighbors,
Random Forest, lightGBM, and Support Vector Machine (SVM). 5-cross-validation was used to construct and predictmachine learning models. Root Mean Squared Error (RMSE) and coefficient of determination (R²) were calculated as
evaluation indices. Comparative evaluation of models was performed for each descriptor and machine learning method.
Results and Discussion: The results of the comparative evaluation by cross-validation showed that the SVM
model using the descriptor LCP had the highest accuracy. The results of external validation showed that the modelwith descriptor PLIP and machine learning method SVM outperformed the accuracy of the docking program whencomparing top1 success rate.

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P06 創薬応用 Drug Discovery Application

P06-04Development of a web based de novo design tool for
medicinal chemists
Yusuke TATENO*, Keiji OGURA, Hiromi NAKAI, Kousuke MAEDA, Kazunari HATTORI
Laboratory for Medicinal Chemistry Research, SHIONOGI & CO., LTD.

( * E-mail: yusuke.tateno@shionogi.co.jp )AI technologies such as machine learning and generative model is attracting attention to shorten the time requiredfor drug discovery. However, these technologies require specialized knowledge such as coding, and medicinal chemists
have not widely utilized them. Therefore, the development of tools that allow medicinal chemists to easily access thesetechnologies is in demand.
We developed a web based de novo design tool for medicinal chemists. It enables them to expand their idea
compound into thousands of analogs by generative models and to prioritize analogs by machine learning models (activity,
ADMET).
Here, we describe each function of this tool and examples of how the tool has been applied in drug discovery
programs in Shionogi. Use of the tool in several programs has led to identification of new chemotypes and resolution ofprogram issues (ex, activity, multi parameter optimization (MPO)). Additionally, feedback from medicinal chemists has
indicated that the tool is being effectively utilized to expand their ideas and prioritize idea analogs as expected, while
also indicated that there are challenges in the data processing and analysis of the output data. We also describe the future
prospect in this aspect.

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P06 創薬応用 Drug Discovery Application

P06-05Web-based system for detecting SAR transfer events from SAR
progression data of different targets
Atsushi YOSHIMORI*, 1, Yuichi HORITA2, Toru TANOUE2, Jürgen BAJORATH3
Institute for Theoretical Medicine, Inc., 2Infogram, Inc., 3Department of Life Science Informatics,
B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische FriedrichWilhelms-Universität1( * E-mail: yoshimori@itmol.com )Structure-activity relationship (SAR) transfer refers to the presence of similar potency progression of analogues
series (AS) with activity against different targets. Hence, the SAR captured by a given AS can be perceived as beingtransferred to another. Such SAR transfer series consist of analogues with different core structures and pairwise
corresponding substituents. Compounds in SAR transfer series are ordered according to increasing potency and alignedwith respect to common substituents.
The main practical goal of detecting SAR transfer events for compound optimization is increasing the activity of an
AS of interest by replacing a substituent of the currently most potent analogue with a new substituent from a transfer AS
implicated in a potential potency increase.
For the systematic detection of SAR transfer events, we previously reported two methods called AS alignment [1]and DeepAS [2]. The AS alignment approach employs the Needleman-Wunsch dynamic programming algorithm toautomatically search for and align query and target AS and identify candidate analogues for the transfer of substituents.
For example, we successfully designed new MMP-1 inhibitors from AS of KIF11 inhibitors using AS alignment[3]. Furthermore, DeepAS uses a deep chemical language model to suggest new substituents for input AS withoutalignments. For compound optimization, both methods can be used in a complementary manner.
Herein, we report a new web-based SAR transfer detection system (SAR-Trans) that was developed to provide andintuitive interactive user interface for medicinal chemists to enable SAR transfer studies and compound optimization.
The SAR-Trans system includes the following operations: (i) Input AS: Structures comprising an AS are drawn bya chemical editor or generated from SMILES. (ii) Save and Load AS: Input AS can be saved and loaded. (iii) Selectand Search: The user can select the AS alignment and/and DeepAS methods and carry out the calculations. (iv) AS
Alignment: Alignment results are visualized and target information and links to literature references are provided. (v)DeepAS: New AS are presented in a ranking or hierarchical representation. A detailed exemplary application will bepresented.
References:[1] Yoshimori A, Bajorath J, Computational method for the systematic alignment of analogue series with structureactivity relationship transfer potential across different targets, Euro J Med Chem 2022; 239:114558.
[2] Yoshimori A, Bajorath J, DeepAS – Chemical language model for the extension of active analogue series, Bioorg
Med Chem 2022; 66:116808.
[3] Umedera K, Yoshimori A, Bajorath J, Nakamura H, Design of MMP‑1 inhibitors via SAR transfer and experimental
validation. Sci Rep 2022, 12, 20915.

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P06 創薬応用 Drug Discovery Application

P06-06
Identifying suitable AlphaFold2 protein structure models for improved
structure-based virtual screening
Keisuke UCHIKAWA*, Kairi FURUI, Masahito OHUE
Department of Computer Science, School of Computing, Tokyo Institute of Technology( * E-mail: uchikawa.k.ac@m.titech.ac.jp )Structure-based virtual screening (SBVS), which utilizes protein conformational information, has gained significant
attention in recent years due to its potential to discover highly novel drug candidate compounds more effectively
than other methods. However, a major challenge lies in the variability of screening accuracy depending on theconformation of the target protein. Therefore, in this study, we considered utilizing the predicted three-dimensional
structure information using AlphaFold2, a representative method for protein structure prediction, to perform structurebased virtual screening. In particular, we comprehensively examined which predicted structures improve the accuracyof virtual screening using the DUD-E diverse subset. Additionally, we investigated the performance of SBVS for
predicted structures of proteins that AlphaFold2 did not train on, and verified that SBVS can be applied to proteins withunknown structures by utilizing predicted structures. As a result, it was demonstrated that using outlier structures basedon the RMSD of residues around the ligand, without using template structures, is optimal. Moreover, the screening
performance for each target protein was equivalent to or slightly lower than the PDB structures, but some proteins
showed better screening performance than the PDB structures. Additionally, the screening performance for targets that
AlphaFold2 did not train on showed a similar trend. This study suggests that the application range of structure-based
virtual screening can be significantly expanded by utilizing predicted three-dimensional structures.

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P06 創薬応用 Drug Discovery Application

P06-07The construction of vNAR library by cDNA display system forapplication to drug discovery
Hiroki ANZAI*, Susumu MITSUYAMA, Shuichi ASAKAWA
Laboratory of Aquatic Molecular Biology and Biotechnology, Department of Aquatic Bioscience,
Graduate School of Agricultural and Life Sciences, The University of Tokyo( * E-mail: anzai-hiroki763@g.ecc.u-tokyo.ac.jp )In recent years, single-domain antibodies (sdAbs) have attracted attention as next-generation antibodies. The sdAb issingle variable domain of the heavy chain antibody, and the sdAb has high antigen specificity and affinity like that of aconventional IgG antibody. The variable domain of Heavy chain of Heavy chain antibody (VHH) derived from camelids
and the single variable new antigen receptor domain antibody fragment (vNAR) derived from sharks have been
discovered as representative sdAbs. In addition, it has been reported that VHH and vNAR are highly structural stability
by refolding and can be also produced using bacteria as a recombinant protein [1][2]. We selected the vNAR because
cartilaginous fish were the first organisms to acquire the gene cluster for the antibody-based immune system.
One of the methods for developing antibodies and aptamers is in vitro display technologies such as phage display,
Ribosome display, and mRNA display. In particular, cDNA display is a molecule covalently conjugated a polypeptide
and the cDNA that encodes it via DNA linker modified with puromycin [3]. cDNA display molecules are synthesizedunder cell-free translation system, and cDNA display system enables to high throughput screening functional
biopolymers from randomized huge library (library size: ~1013 molecules/mL) [4].
We previously sequenced the IgNAR gene sequence of the brownbanded bamboo shark (Chiloscyllium punctatum )[5]. Hence, we first extracted and purified total RNA from the lymphocyte in the blood of the brownbanded bamboo
shark, and then reverse transcription and PCR were performed. We constructed the DNA full length library for cDNA
display using the prepared DNA naïve library that encodes vNAR region (Fig. 1). Lastly, we analyzed the efficiency
of cDNA display formation by SDS-PAGE and sequence diversity (library size) using next-generation sequencer. We
report on these results.
[1] Ellen, R. G., et al., Front Immunol., 2017, 8, 865.
[2] Mirna, B-F., et al., Sci Rep., 2023, 13, 1, 3596.
[3] Yamaguchi, J., et al., Nucleic Acids Res., 2009, 37, 16, e108.
[4] Anzai, H., et al., ACS Med Chem Lett., 2021, 12, 9, 1427-1434.
[5] De Silva, DPN., et al., Fish Shellfish Immunol., 2019, 84, 370-376.

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P06 創薬応用 Drug Discovery Application

P06-08Chemical Space Analysis for Cytochrome
P450–Induced Orphan Nuclear Receptors Ligands
Kazuma KAITOH*, Yoshihiro YAMANISHI
Graduate School of Informatics, Nagoya University( * E-mail: kaitoh@i.nagoya-u.ac.jp )Keywords: Pregnane X receptor, Docking simulation, Chemical space, Machine learning
Cytochrome P450 (CYP) monooxygenase superfamily plays an important role of phase I drug metabolism. CYPs
have involved in the metabolism of various xenobiotics, and many of CYPs have low substrate specificities. Therefore,
CYP expression affects a homeostasis through the change in the kinetics of xenobiotics including drugs and the amounts
of its metabolites. Pregnane X receptor (PXR) and constitutive androstane receptor (CAR) are a type of nuclear receptor
superfamily. They induce various CYP expression by dimer formation with retinoid X receptor[1]. Many approved drugs
induce CYP expression via PXR and CAR activations. It is important for drug discovery to obtain information about
interaction between PXR/CAR and drugs.
In this study, we attempt to reveal the characteristics of chemical space for PXR/CAR activation ligands. PXR andCAR ligands were obtained from ChEMBL[2]. Then, docking simulations between PXR/CAR and their ligands were
executed by Autodock Vina[3]. We compared the binding free energies obtained from docking simulations with pEC50
that was the logarithm values for EC50. It indicates that both PXR and CAR ligands with low binding energies tend to
be not high pEC50. The clustering by k-means was executed for PXR ligands and many ligands on the cluster with highbinding free energy and pEC50 had hexafluoropropanol group.
The ligands were represented in Morgan fingerprint and the chemical space consisting of the ligands was visualized
by principal component analysis (PCA) and uniform manifold approximation and projection (UMAP). Both PCA and
UMAP visualization, PXR ligands were distributed throughput the chemical space. On the other hand, ligands with highbinding free energy and high pEC50 were relatively concentrated on a part of the chemical space. The distribution of
CAR ligands was also concentrated in specific regions. The search for PXR/CAR ligands located on the sparse chemical
space is expected to contribute to the development of PXR/CAR–ligand interaction prediction method.
[1] Honkakoski, P.; et. al. , Drug-activated Nuclear Receptors CAR and PXR. Ann. Med. , 2003, 35 , 172-182.
[2] https://www.ebi.ac.uk/chembl/[3] Eberhardt, J.; et. al. , AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings. J.
Chem. Inf. Model. , 2021, 61 , 3891–3898.

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P06 創薬応用 Drug Discovery Application

P06-09Development of novel fluorescent indicators to discover new
RNA-targeting small molecules in FID assay driven by large-scaleprofiles of RNA-indicator interactions
Ryosuke NAGASAWA*, 1, 2, Kazumitsu ONIZUKA1, 2, Ryohei IWATA1, 2,
Kaoru Richard KOMATSU3, 4, Emi MIYASHITA3, 4, Sayaka DANTSUJI4,
Hirotaka MURASE1, 2, Mamiko OZAWA1, Hirohide SAITO3, Fumi NAGATSUGI1, 2
IMRAM, Tohoku Univ., 2Graduate School of Science, Tohoku Univ., 3CiRA, Kyoto Univ., 4xFOREST
Therapeutics

1( * E-mail: nagasawa.ryosuke.p4@dc.tohoku.ac.jp )RNA-targeting small molecules have been attracting attention as new therapeutic drugs for intractable diseases, andthe development of excellent methods for discovering them has been highly desired. Fluorescent indicator displacement(FID) assay 1,2) is one of the screening methods to effectively identify RNA-binding small molecules (Fig. A). In thisassay, a fluorescent probe is used as an indicator to screen test compounds that bind to a target RNA. However, thesimilarity in binding modes of indicators limits the number and binding type of hit compounds. Therefore, it is essential
to diversify fluorescent indicators to obtain hit compounds without missing them. This study aims to achieve two
objectives: 1) developing fluorescent indicators with different binding modes from known indicators, and 2) discoveringnovel RNA-binding small molecules by FID assay with the new indicators. To develop a new fluorescent indicator, we
designed thiazole orange (TO)-conjugated RNA-binding molecules. The large-scale data of RNA binding selectivity
was obtained by utilizing microarray technology 3) that we have developed recently. The large-scale data makes it easierto select target RNA-indicator combinations for FID assay.
The binding selectivity of the newly-synthesized indicators was verified by fluorescence titration experiments andrevealed that the binding selectivity of RNA-binding molecules is generally retained. Furthermore, we conducted FID
assays using disease-related pre-miRNAs and the new indicators and successfully identified new hit compounds thatwere not detected with the known TO-based indicator. In this presentation, we will report detailed data on the novel
indicators and results of the FID assays.
1) K. Nakatani, et al. J. Am. Chem. Soc. 2010, 132 , 3660.
2) S. Nishizawa, et al. Chem. Commun. 2019, 55 , 3183.
3) H. Saito, et al. Nat. Commun. 2020, 11 , 6275.

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P06 創薬応用 Drug Discovery Application

P06-10
Structural basis of the inhibition of the Keap1-Nrf2 interaction by
novel PPI inhibitors discovered by machine learning
Koujin KOJIMA*, 1, Hanako ISHIDA1, Ayaka HARADA1, Tomoki YONEZAWA1,
Yugo SHIMIZU1, 2, Kazuyoshi IKEDA1, 2, Mariko YOKOGAWA1, Masanori OSAWA1
Graduate School of Pharmaceutical Sciences, Keio University, 2RIKEN

1( * E-mail: koujin.saitama.1998@keio.jp )Nrf2 is a transcription factor related to the antioxidative responses. In the absence of the oxidative stress, Nrf2 binds
to its cytoplasmic repressor Keap1 using two Keap1-binding motifs of Nrf2: DLG and ETGE, each of which interacts
with a Kelch domain of Keap1. In particular, the DLG-Kelch interaction causes ubiquitination and thus degradation of
Nrf2, resulting in suppression of the transcriptional activity of Nrf2. In the presence of the oxidative stress, the DLGKelch interaction is impaired, which suppresses the Nrf2 degradation. Therefore, the transcriptional activity of Nrf2 isincreased, leading to the antioxidative action. Since chronic dysfunction of Nrf2 as well as excessive oxidative stresscauses many diseases such as diabetes and neurodegenerative diseases, inhibition of the DLG-Kelch interaction is
expected to be a new therapeutic approach to the Nrf2 activation.
In order to discover novel Keap1-Nrf2 inhibitors , we established an AI model by machine learning, and then, we
performed a virtual screening from our original library containing mid-sized molecules, called DLiP (Ikeda et al. 2023).
Compounds predicted to be active in this virtual screening were subjected to a TR-FRET assay, indicating that the IC50
values of ca. 50 μM even for the most potent compound, KP-016 (Shimizu et al, 2021). To improve their inhibitory
activities, we tried to obtain structural information on the interaction of these compounds with the Kelch domain by
NMR. We have analyzed direct interaction with the Kelch domain for KP-016 (Figure 1A), and its 12 derivatives.
We prepared 15N-labaled Kelch domain and observed 1H-15N HSQC NMR spectrum, which shows the correlation
peaks between 1H and 15N chemical shifts of each amide group of each residue. The assignments were previously
reported (BMRB Entry: 18353). Then, we observed the same spectra of 15N-Kelch domain upon sequential additions of
KP-016 or its 12 derivatives.
First, three derivatives missing carboxy group exhibited no spectral change, indicating that these did not interact
with the Kelch domain, and that the carboxy group is required for the binding. Other 10 compounds induced NMR
spectral changes of the Kelch domain. The distribution of the affected residues demonstrates that these bind to thecentral cavity and surrounding surface of the Kelch domain, which corresponds to the binding site of the DLG motif.
Spectral comparison between those with substitution of p -chlorophenyl group suggests that this group is inserted to thecentral cavity. Supposing p -chlorophenyl group binds to the central cavity, Arg380 was postulated as a counterpart ofthe carboxy group of the compounds. Taken together, we searched for docking poses consistent with these experimental
results (Figure 1B), which could be structural basis for further drug design.
Figure. 1 A) The binding mode of the Kelch domain and KP016. B) The docking model of KP016.

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P07 ADME・毒性 ADMET

P07-01
In silico regression modelling to predict the transport inhibitory activityof Breast Cancer Resistant Protein
Kaoru TAKADERA*, 1, 2, Reiko WATANABE1, Kenji MIZUGUCHI1
1

Mizuguchi Laboratory, Institute for Protein Research, Osaka University, 2Graduate School and
School of Pharmaceutical Sciences, Osaka University( * E-mail: u852998k@ecs.osaka-u.ac.jp )Computational technology and in silico tools aid reduction of drug discovery costs, followed by rational design ofdrugs that show drug-likeness and have suitable absorption, distribution, metabolism, excretion, and toxicity (ADMET).
Breast Cancer Resistant Protein (BCRP/ABCG2) is one of the ABC transporters and works as efflux transportersof various compounds including drugs in the blood-brain barrier, kidney, liver, and intestine. Inhibition of BCRP is
considered one of the important pharmacokinetic parameters in terms of drug-drug interactions because it raises theplasma concentration of drugs. Although several classification models to distinguish inhibitors from non-inhibitors wereconstructed previously, they cannot deal with different activity thresholds, typically chosen based on the target, project
objectives and drug discovery stage. Additionally, in recent years, there has been a demand for the construction of
explainable AI that enables users to understand why the AI made the decisions.
In this study, we aimed to construct regression models that can predict IC50 values of BCRP and interpret thepredicted results and apply them to drug discovery. Experimental data were obtained from the ChEMBL database and apublished paper. After the data was curated carefully, we got a high-quality dataset of 1248 compounds with IC50 values
for BCRP inhibition. Machine learning (ML) models were built using Python, we constructed the models showing 0.86
of the coefficient of determination (R2) in the test set. In addition, Shapley Additive Explanations (SHAP) was used
to explore important features to interpret prediction. Although the prediction model was constructed with sufficientaccuracy, it became clear that difficult-to-interpret descriptors limited the explanation of the model prediction. To
overcome this problem, a new set of ML models using only substructure-based fingerprints and a tree-based algorithm
were constructed, which were able to be interpreted, with an R2 of 0.90. Furthermore, several substructures wereidentified as key features of the new model based on SHAP values. These models contribute to accelerate and lowercost compound screening in the early stages of drug discovery, as well as aid in the exclusion of potential future dropout candidates.

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P07 ADME・毒性 ADMET

P07-02
Automation of building ML prediction models using in-house data anddevelopment of prediction result response applications
Kan SHIRAISHI*, 1, Takayuki SERIZAWA1, Ryo KUNIMOTO2
Medicinal Chemistry Research Laboratories, R&D Division, Daiichi Sankyo Co., Ltd., 2Discovery
Intelligence Research Laboratories, R&D Division, Daiichi Sankyo Co., Ltd.

1

( * E-mail: shiraishi.kan.ae@daiichisankyo.co.jp )The drug development process has become more challenging, time-consuming, and costly compared to the past.
Drug discovery for new targets, especially those that can be intervened with low-molecular-weight compounds, has
become more difficult due to their depletion.
To improve productivity in drug research, “Data-Driven Drug Discovery” (D4) using big data and Artificial
intelligence (AI) is essential.[1] Daiichi Sankyo Co., Ltd. is developing Quantitative Structure-Activity Relationship(QSAR) models to predict the properties of small molecule compounds, with a focus on Absorption, Distribution,
Metabolism, Excretion, and Toxicity (ADMET) characteristics, which play a crucial role in drug design. ADMET
profiling can reduce the risk of setbacks in drug development, but in vitro screenings can be expensive. To address theseissues, D4 activities are being explored.
However, creating QSAR models requires specialized knowledge, and manual tasks within the machine learning
process are time-consuming. To overcome these challenges, we introduced "exaChem", an AI component set developedin collaboration with ExaWizards Inc.[2] An exaChem simplifies data preprocessing and integration, enabling
researchers with limited data science knowledge to achieve high accuracy in QSAR predictions.
In this presentation, Daiichi Sankyo showcases their efforts in enabling Auto Machine Learning (ML) for QSAR
predictions using ExaChem, covering automation from data collection to model creation and introducing applications
for accessing and interpreting prediction results.
[1] Ryo Kunimoto, Jürgen Bajorath, Kazumasa Aoki, From traditional to data-driven medicinal chemistry: A case study,
Drug Discovery Today, Volume 27, Issue 8, Pages 2065-2070, 2022.
[2] exaChem, https://exawizards.com/exabase/usecase/009― 245 ―

P07 ADME・毒性 ADMET

P07-03Novel in silico model to predict the risk of covalent binding andinvestigation of important substructures using by Message
Passing Neural Network
Yuki UMEMORI*, Koichi HANDA, Saki YOSHIMURA, Michiharu KAGEYAMA,
Takeshi IIJIMA
Toxicology & DMPK Research Department, TEIJIN PHARMA LIMITED( * E-mail: y.umemori@teijin.co.jp )Predicting whether a compound can cause drug-induced liver injury (DILI) is difficult due to the complexity of its
mechanism. The production of reactive metabolites is one of the major causes of DILI, particularly idiosyncratic DILI.
The cysteine trapping assay is one of the methods to detect reactive metabolites which bind to microsomes covalently.
However, it is cumbersome to use 35S isotope-labeled cysteine for this assay. Therefore, we constructed an in silicoclassification model for predicting a positive/negative outcome in the cysteine trapping assay to accelerate the drug
discovery process. In this study, we collected 475 compounds (436 in-house compounds and 39 publicly available
drugs). Using a Message Passing Neural Network (MPNN) and Random Forest (RF) with extended connectivity
fingerprint (ECFP) 4, we built machine learning models to predict the toxicity of compounds. The 5-fold crossvalidation (CV) and hold-out test were evaluated in random- and time- split trials. Additionally, we investigated thesubstructures that contributed to positive results in the cysteine trapping assay through the framework of the MPNN
model. In the random-split dataset, the AUC-ROC of MPNN and RF were 0.698 and 0.811 in the 5-fold CV, and 0.742
and 0.819 in the hold-out test, respectively. In the time-split dataset, AUC-ROC of MPNN and RF were 0.729 and 0.617
in the 5-fold CV, and 0.625 and 0.559 in the hold-out test, restrictively. This result suggests that the MPNN model has ahigher predictivity than RF in the time-split dataset. Hence, we concluded that the in silico MPNN classification modelfor the cysteine trapping assay had better predictive power. Furthermore, most of the substructures that contributed
positively to the cysteine trapping assay were consistent with previous reports such as propranolol, verapamil, andimipramine. This is the first successful implementation of a machine learning model that can determine the outcomeof the cysteine trapping assay, namely accurately predicting idiosyncratic DILI. We believe that this can contribute to
mitigating DILI risk for reactive metabolites at the early stages of drug discovery.

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P07 ADME・毒性 ADMET

P07-04Development of the classification model to predict the potential ofchemical reactivity to cysteine using the combination of two
machine learning algorithms
Koji JOJIMA*, Yumi AKAHORI
Chemicals Assessment and Research Center, Chemicals Evaluation and Research Institute,Japan( * E-mail: jojima.kj@outlook.jp )Covalent binding to protein is one of molecular initiating events that cause a variety of toxicity such as skinsensitization. Nucleophilic thiol group in cysteine is main target of electrophilic chemicals to form covalent binding toprotein. Therefore, prediction of the potential of chemical reactivity to cysteine provides useful information for toxicity
of chemicals. In this study, we attempted to develop in silico classification models to predict the potential of chemical
reactivity to cysteine.
For the model construction, as a parameter of chemical reactivity to cysteine, cysteine percent depletion (Cys %dep) values measured in Direct Peptide Reactivity Assay (DPRA) were collected from publications and the dataset
containing 211 chemicals was created. Chemicals in the dataset were classified “High Reactivity” (Cys % dep > 23%)or “Low Reactivity” (Cys % dep ≤ 23%). This dataset was divided into training, validation and test datasets, which
include 131, 40 and 40 chemicals, respectively. Random Forest (RF) and Graph Convolutional Network (GCN) wereadopted as classification algorithm to build the prediction model. Descriptors were obtained from results of calculated
physicochemical properties by RDKit and semi-empirical quantum chemical calculation with PM6 by Gaussian16. The
training dataset was used for learning process, and the validation dataset was used for avoiding over-fitting. External
validations of both RF-based and GCN-based models were conducted by using the test dataset.
As a result, RF-based model showed higher specificity (0.913 in the test dataset) compared with GCN-based model,on the other hand, GCN-based model showed higher sensitivity (0.882 in the test dataset) in all of training, validationand test datasets. The combination model that adopted consensus of two models showed higher accuracy (0.912 in thetest dataset) compared with adopting prediction results of each model solely.
In conclusion, we could develop the high-performance classification model to predict the potential of chemical
reactivity to cysteine using the combination of two machine learning algorithms.

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P07 ADME・毒性 ADMET

P07-05
Performance Evaluation for Algal  Acute and Chronic Toxicity Prediction of
QSAR Model“KATE2020 ver. 4.1”
Yuto ITAMI*, Koichi OHNO, Masao HAMADA, Midori GOTO, Sato RIE, Shigeto ODA,
Hiroshi YAMAMOTO
Health and Environmental Risk Division, National Institute for Environmental Studies( * E-mail: itami.yuto@nies.go.jp )Many of the hundreds of thousands of organic chemicals currently in the market lack the toxicity informationnecessary for ecological risk assessment, and there are growing expectations for the use of alternative methods to
toxicity testing such as quantitative structure-activity relationships (QSAR). The Health and Environmental Risk
Division of the National Institute for Environmental Studies has developed the "KAshinhou Tool for Ecotoxicity”(KATE), which predicts acute and chronic toxicity in aquatic organisms by classification based on chemical structure
and linear regression of octanol/water partition coefficients. Performance evaluation results of the latest version of
KATE2020 (ver. 4.1) regarding algal acute and chronic toxicity prediction is reported in this study.
Toxicity data on organic chemicals not included in KATE2020 were collected from OECD’s SIDS as a test set
for performance evaluation. Data on some organism species not used in KATE2020 (e.g., Desmodesmus pannonicus,etc.) were employed, and data within the standard test duration ±24 hours were also employed. Only one data with thehighest reliability rank (Klimisch code) was adopted if multiple toxicity data for one chemical exist in SIDS. In the casewhere there were multiple data with the same reliability rank for one chemical, the geometric mean value was used. The
total number of test set data is 141 for acute and 100 for chronic.
For comparison with KATE2020, ECOSAR (ver. 2.2) of US EPA and TIMES (ver. 2.30) of Bourgas University’sLaboratory of Mathematical Chemistry were used, which are QSAR models similar to KATE2020 that predict toxicity
with classification and linear regression. The toxicity values of the test set chemicals were predicted using these models
and then the rate at which the difference between the predicted and measured toxicity values fell within one order ofmagnitude was calculated for each model. Only predictions within applicability domain (AD) in KATE2020 and TIMES
and those without alert flag in ECOSAR were used.
KATE2020 predicted 47% of test set chemicals within AD for acute and 44% for chronic. The percentage was 94%
in ECOSAR and 3% in TIMES for acute, and 92% in ECOSAR for chronic (TIMES has no predictive model for chronic
toxicity).
The percentages of the difference between predicted and measured toxicity values that fell within one order ofmagnitude were 73% in KATE2020, 66% in ECOSAR, and 100% in TIMES for acute, and for chronic, 68%, 63% for
KATE2020 and ECOSAR, respectively. KATE2020 could predict fewer test set chemicals within AD than ECOSAR,
which does not judge AD, but the prediction accuracy was higher than that of ECOSAR for algal acute and chronic
toxicity if the predicted values within AD were obtained. TIMES, which thought to have tighter AD than KATE2020,
predicted only a few test set chemicals within AD, but all of which fell within one order of magnitude.

― 248 ―

P07 ADME・毒性 ADMET

P07-06
An Attempt to Modify Training Datasets Containing Inequality
Signs for Machine Learning
Hitoshi KAWASHIMA*, 1, Masataka KURODA1, 3, Reiko WATANABE1, 2,
Kenji MIZUGUCHI1, 2
1
Artificial Intelligence Center for Health and Biomedical Research, National Institutes ofBiomedical Innovation, Health and Nutrition, 2Laboratory for Computational Biology, Institute forProtein Research, Osaka University, 3Discovery Technology Laboratories, Mitsubishi Tanabe
Pharma Corporation( * E-mail: hkawashi@nibiohn.go.jp )Most measurement data are treated with inequality signs added to a certain threshold value due to the detection limitof the measuring instrument, measurement method, or other reasons. Such data are rarely a problem when creatingclassification models, but when creating regression models, it can be a headache to know how to handle these values.
Therefore, we attempted a new method of dataset modification to generate predictive models with higher accuracy.
It is assumed that a value with an inequality sign has a true value between that value and a certain value, and thefinal goal is to find the value within that interval to increase the prediction performance.
Using the solubility data registered in PubChem, values with inequality signs were removed from this data. The
threshold value was set to 0.01. Values below 0.01 were considered “<0.01” and compounds with these values are called
as “interested compounds” here. These values were set to be varied within the training set in the interval from 0.01 to
0.0001.
Initially, the prediction model was recreated by replacing these values with randomly determined values within theinterval. In the metric calculation using test set, if the predicted value of an interested compound was less then 0.01,
the value was replaced with the true value in order to recognize that such a case was correct. Then the predicted values
for the training set were calculated with this prediction model, and if the predicted value for the interested compoundis greater than or equal to 0.01, the value was reassigned. Prediction model was built with this new training set andmetric for the test set was calculated as described above. If the metric value was better than that at the previous round,the training set was updated. If worse, we used a Monte Carlo method to decide whether to reject the new training set or
update. This was performed dozens of times.
As a comparison, prediction models were created with the original data and inequality values fixed at 0.01, 0.001,
0.0001, 0.00001, 0.000001, and 0.0000001, respectively, to evaluate the prediction performance. The results showed thebest balance between the number of correct predictions below 0.01 and R2 compared to the dataset with fixed values.
This method is currently being applied to the ADME prediction model building in AMEDʼs “Development of a
Next-generation Drug Discovery AI through Industry-academia Collaboration (DAIIA)” project.

― 249 ―

P07 ADME・毒性 ADMET

P07-07
Physiologically based pharmacokinetic model to explain multiple peaks
in plasma concentration profile of an anticancer drug regorafenib
Fukino KONO*, 1, Yudai ARARAGI1, Yusuke MASUO1, Ken-ichi FUJITA2, Yukio KATO1
1

Faculty of Pharmacy, Kanazawa University, 2School of Pharmacy, Showa University( * E-mail: fukino3528@stu.kanazawa-u.ac.jp )Regorafenib is an oral multikinase inhibitor for the treatment of colorectal and other cancers. We have previously
reported the pharmacokinetics of regorafenib and its active metabolites (M2 and M5) in colorectal cancer patients,
showing multiple peaks of regorafenib in plasma after repeated (once a day) oral administration (Clin Pharmacol Ther.
108, 586, 2020). Such multiple peaks may be explained by the enterohepatic circulation although the mechanism
for the regorafenib case is still unknown. The purpose of the present study was to construct physiologically based
pharmacokinetic (PBPK) model to explain the multiple peaks in plasma regorafenib concentration by incorporating
enterohepatic circulation. Plasma concentration of regorafenib in mice exhibited double peaks after oral administrationof regorafenib (10 mg/kg). Therefore, we first attempted to construct the PBPK model in mice by performing various
pharmacokinetic studies. To identify compounds potentially involved in enterohepatic circulation, bile was collected
for 180 min after intravenous administration of regorafenib (1 mg/kg), and regorafenib, its active metabolites, andregorafenib glucuronide (M7) were measured. Approximately 18% of the dose was recovered in the bile as M7, whereas
regorafenib and M2 were only 1.6 and 0.2%, respectively, M5 being not detected, suggesting that regorafenib was
mainly recovered as its glucuronide form in the bile. To directly investigate the deconjugation of M7 to regorafenibin the body, M7 (10 mg/kg) was orally administered, and regorafenib was detected in plasma, the time showing its
peak concentration (Tmax) being around 12 h. On the other hand, Tmax of regorafenib after oral administration ofregorafenib itself was ~2 h, suggesting that the delayed Tmax was caused by deconjugation of M7 and subsequent
gastrointestinal absorption of regorafenib. Regorafenib was detected during the incubation of M7 with mouse small
intestinal homogenates, demonstrating the deconjugation of M7 to regorafenib possibly in small intestinal lumens. ThePBPK model of regorafenib and M7 was then constructed by incorporating the enterohepatic circulation of M7 andregorafenib. To explain the delayed Tmax of regorafenib deconjugated from M7, the small intestine compartment was
divided into three sections. Parameter optimization was performed by a nonlinear least square method using plasma
concentration profiles of regorafenib and M7 after oral administration of regorafenib and M7. Partition of regorafenib toseveral large organs was measured by in vivo study and also incorporated. The optimized model can successfully explain
the plasma concentration profile of regorafenib and M7. Thus, regorafenib orally absorbed may be mainly excreted into
the bile as glucuronide form which may undergo enterohepatic circulation and contribute to the long-lasting regorafenib
exposure to systemic circulation. Extrapolation of the model to humans is currently under investigation.

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P07 ADME・毒性 ADMET

P07-08
Exploring Chemical Structural Insights of ADME Properties via
Interpretable Deep Learning
Shoma ITO*, 1, Takuto KOYAMA1, Hiroaki IWATA1, Shigeyuki MATSUMOTO1,
Yasushi OKUNO1, 2
1

Graduate School of Medicine, Kyoto University , 2RIKEN( * E-mail: ito.shoma.45x@st.kyoto-u.ac.jp )In recent years, many computational methods have developed to evaluate the pharmacokinetic properties of drugcandidate compounds. These methods have significantly contributed to drug discovery. However, despite these advancesin computational drug discovery technologies, challenges persist in progressing from basic research to clinical study.
This study aims to visualize and interpret how much the substructure of a compound contributes to ADME properties.
In this way, it is expected to capture the essence in the structural optimization of drug candidates and help in drug
development.
We constructed comprehensive ADME-AI models for precise prediction of pharmacokinetic parameters throughGraph Convolutional Neural Networks [1] utilizing the ADME dataset from Fang et al. [2]. These models havedemonstrated exceptional predictive performance. Furthermore, they have effectively delineated crucial substructureslinked to specific ADME parameter predictions through the application of integrated gradients. These results facilitate
the comprehension of the role of chemical structures in ADME parameters, thus assisting in the interpretation ofundesirable side effects and the identification of critical substructures important for drug design.
[1] https://github.com/elix-tech/kmol[2] F ang, Cheng, et al. "Prospective Validation of Machine Learning Algorithms for Absorption, Distribution,
Metabolism, and Excretion Prediction: An Industrial Perspective." Journal of Chemical Information and Modeling(2023)― 251 ―

P07 ADME・毒性 ADMET

P07-09Establishment of in silico prediction model for skin sensitization aiming
for practical application
Kei KINOSHITA*, 1, Kaori AMBE1, Takashi YAMADA2, Takao ASHIKAGA2,
Masahiro TOHKIN1
1

Department of Regulatory Science, Graduate School of Pharmaceutical Sciences, Nagoya City
University, 2Division of Risk Assessment, National Institute of Health Science( * E-mail: c182014@ed.nagoya-cu.ac.jp )Skin sensitization to chemicals had been evaluated by the animal tests, such as Local Lymph Node Assay(LLNA). However, the European Union (EU) banned animal testing for the safety of cosmetic ingredients, and newsafety evaluation approaches, such as in vitro and in silico , are developed. Recently, qualitative estimation methods
using a combination of several alternative test methods were listed in Organization for Economic Co-operation andDevelopment (OECD) guideline, but the quantitative estimation method has not been listed regardless of its highnecessity for risk assessment [1]. In our study, we tried to establish the machine learning regression model for predicting
LLNA EC3 value, a quantitative indicator in skin sensitization, for the safety assessment.
For model construction and internal validation, safety test data of 154 substances from the Defined Approach
Guideline at OECD(No.497) [1] were used and safety test data of 72 substances from the new Cosmetic Europe
database [2] were used as external validation. We used three alternative test data related to the adverse outcome pathwayof skin sensitization, chemical properties, molecular descriptors, and alert information, as the explanatory variables. We
constructed a 3-fold cross-validation model by Support Vector Regression (SVR). The applicability domain (AD) was
defined by the k-nearest neighbor and three alternative test data results.
From results of the 300 times trial by changing the data splitting pattern, R squared (R 2) average values of theinternal validation were 0.465±0.145, and the highest R2 value was 0.626. We classified predicted values of the bestmodel and the actual LLNA test values based on the range of LLNA EC3 value of the Globally Harmonized Systemof Classification and Labelling of Chemicals (GHS) as three categories (1A: EC3 ≦ 2%, 1B: 2%<EC3 ≦ 100%, ns:
100%<) and examined consistencies of each classification to evaluate the performance of the prediction model. The
accuracy was 74.5% for internal validation and 44.7% for external validation. These results indicated that we succeeded
to develop the quantitative skin sensitization prediction model that implemented AD setting and external validation,
which are necessary for safety assessment.
[1] OECD Guideline No. 497 (ANNEX2) (2021)[2] Hoffmann, et al ., Regul Toxicol Pharmacol ., 131, 105169 (2022)― 252 ―

P08 量子構造生命科学 / Computer-Aided Drug Design Quantum-Structural Life Science / Computer-Aided Drug Design

P08-01
Structure-guided de novo drug design using deep generative modeling:
Discovery of a new lead series in only 3 DMTA cycles
Victoire M. L. CACHOUX*, 1, Anna KRIUKOVA1, Pascaline JACQUEMARD2,
Anthony MARTINEZ2, Stephane SAUTET2, Alexis DENIS1, Eric BOURSIER2,Maud JUSOT1, Nicolas DEVAUX1, Christopher HOUSSEMAN1, Nicolas MARTIN1,
Stephanie LABOUILLE1, Brian ATWOOD1, Yann GASTON-MATHE1, Nicolas DO HUU1,
Quentin PERRON1, Yann LAMOTTE2, Brice HOFFMANN1
1

Iktos, 2Oncodesign( * E-mail: victoire.cachoux@iktos.com )The use of artificial intelligence (AI) in chemistry has the potential for a transformative impact on the field of drug
design and discovery. We combined structural information with generative chemistry and QSAR models to develop
a robust 3D structure-based generative AI pipeline for early stage hit discovery projects, which identifies moleculesthat optimize interactions in the binding pocket, relevant to activity or other properties. This pipeline generates novel
chemical matter and is not restricted to what can be screened via traditional methods such as virtual library screening.
In addition to docking, tools such as MM-GBSA were also deployed for better binding affinity evaluation. We used thispipeline in a structure-enabled project as a proof of concept. We successfully targeted PIM1 kinase using a structurebased generative modeling approach. New molecules were proposed, synthesized, and tested and served as a proof ofconcept of this approach. After synthesizing and testing only 12 molecules from 5 diverse scaffolds, 4 had activity lessthan 5 μM, and 2 had activity less than 1 μM. These two molecules represented two different scaffolds and binding
modes in the same pocket. All synthesized compounds showed good preliminary ADME properties, and all had FTO(Freedom to Operate) for intellectual property purposes. This success validates our approach and demonstrates therobustness of our pipeline. Current and future work includes generation and synthesis of more molecules with thesescaffolds, with improved activity and ADME properties.

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P08-02An Approach to Hit Compound Discovery in Shionogi:
Ultra-Large Scale Virtual Screening of Synthesizable Compound Libraries
Shota UEHARA*, Yusuke TATENO, Hiromi NAKAI, Kazunari HATTORI
Medicinal Chemistry Reserach Labolatory, Shionogi & Co., Ltd.

( * E-mail: shota.uehara@shionogi.co.jp )The identification of an active chemical matter to a biological target of interest (i.e. hit compound discovery) is a
first and essential step of the small molecule drug discovery project. Molecular docking based virtual screening become
an efficient and practical approach to identify hit compounds in terms of cost and speed. In addition, increasing numberof target structures has been revealed by Cryo-EM and Alphafold2, thus making virtual screening a versatile and widely
applicable method for hit compound identification.
Although virtual screening can handle any kinds of compounds whether it is “real” or “virtual”, we conventionallyuse “real” compound libraries whether commercial or enterprise. The drug-like chemical space has been estimated at1060 for all molecules obeying Lipinski’s rule-of-five for oral bioavailability [1], that is far too large compared to typicalcommercial or enterprise library at 105-107. In recent years, the limitations of "real" libraries are being overcome by thedevelopment of "virtual" libraries consisting of 108-1010 compounds that can be synthesized [2]. These libraries offer
the opportunity to identify potential therapeutic agents that can be easily synthesized and tested for activity. Moleculardocking of such chemical libraries, so-called ultra-large scale virtual screening, has also resulted in surprisingly high hit
rates and the identification of novel chemical series with high affinity [3].
Here, we describe our workflow of ultra-large scale virtual screening for hit discovery in Shionogi. We built an inhouse virtual compound library composed of over 10 billion (1010) synthesizable small molecules. The compounds ofthe virtual library were designed to be synthesized by combinations of well-established chemical reactions using inhouse library compounds as reagents. Active learning based iterative molecular docking algorithm enables the screeningof billion scale library within 2 weeks and the following absolute binding free energy (ABFE) calculation enrichpotential active compounds for synthesis. We are now applying the workflow to ongoing projects to identify diverse andnovel active compounds from vast virtual chemical space.
[1] Bohacek, R. S.; McMartin, C.; Guida, W. C. The art and practice of structure-based drug design: A molecularmodeling perspective Med. Res. Rev. 1996, 16, 3– 50.
[2] Grygorenko, O. O.; Radchenko, D. S.; Dziuba, I.; Chuprina, A.; Gubina, K. E.; Moroz, Y. S. Generating Multibillion
Chemical Space of Readily Accessible Screening Compounds. iScience 2020, 23 (11), 101681– 101681.
[3] Lyu, J.; Wang, S.; Balius, T. E.; Singh, I.; Levit, A.; Moroz, Y. S.; O’Meara, M. J.; Che, T.; Algaa, E.; Tolmachova,
K.; Tolmachev, A. A.; Shoichet, B. K.; Roth, B. L.; Irwin, J. J. Ultra-Large Library Docking for Discovering New
Chemotypes. Nature 2019, 566, 224– 229.

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P08-03
Solvation Gibbs energies of cyclodextrins
Masao FUJISAWA*, 1, Tomonori OHATA2, Hiroyuki TSUTSUMI 2, Hirohito IKEDA2
Dept. of Biotechnological Science, Kindai University, 2Faculty of Pharmaceutical Science,
Fukuoka University

1( * E-mail: fujisawa@waka.kindai.ac.jp )Cyclodextrins (CyDs) are nonreducing cyclic sugars consisting of a ring of glucose residues linked by a-1,4 bonds.
CyDs can accommodate various kinds of guest molecules in its internal cavity, recognizing the difference in structureof guest molecules in aqueous solution. They have a torus structure, which is also known as a bottomless bucket orcrown. CyDs are relatively readily available as alpha-CyD (six glucose residues), beta-CyD (seven glucose residues),
and gamma-CyD (eight glucose residues). The water solubility is known to differ depending on the size of the innerdiameter of the molecule and substituents. Gamma-CyD has the greatest water solubility, followed by alpha-CyD, betaCyD, in that order. Multiple methods have been used to determine the solvation Gibbs energy and its relationship tosolubility. The Gibbs energies were calculated based on geometry optimization and frequency calculations by using
Density Functional Theory (DFT)in both the gas phase and water. The Bennett acceptance ratio method was also usedwith molecular dynamics. Gamma-CyD had the most stabile solvation Gibbs energy, followed by those for alpha-CyD,
beta-CyD, in that order. Both methods reproduced the experimental values. Solvation Gibbs energy obtained by using
energy representation (ER) method are will be discussed on the day of the annual meeting.

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P08-04In silico protein design based on genetic algorithm
Yoshirou KIMURA*
Life Science Dept., MOLSIS Inc.

( * E-mail: kimura.yoshirou@molsis.co.jp )Designing protein sequences with desired physical properties is one of the important goals in protein engineering.
Such technologies are applied in fields such as enzyme engineering and drug discovery. The primary structure spaceof proteins is enormous even when limited to the 20 natural amino acids. For example, with only 6 residues, there are
206 = 64,000,000 combinations possible. Exploring such a vast space experimentally is very expensive and time
consuming.
Therefore, we attempted to develop a function to optimize the sequence of proteins in silico by genetic algorithm
using the development environment on MOE, a molecular modeling and simulation software [1]. This genetic algorithm
performs optimization by iteratively hybridizing relatively good sequences from a generated population and mutating
randomly selected residues with a certain probability. To test the performance of this function, we compared thethermostability score of the B1 immunoglobulin-binding domain of streptococcal protein G with the existing functionof MOE, “Sample Sequence”, to generate random sequences. As a result, an improvement in sampling efficiency
was confirmed. In this presentation, we will introduce the details of this function and the results of comparison withexperimental results.
[1] Molecular Operating Environment (MOE), 2022.02; Chemical Computing Group ULC, 1010 Sherbrooke St. West,
Suite #910, Montreal, QC, Canada, H3A 2R7, 2023.

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P08-05
Molecular design method of cyclic peptide inhibitors with cell
membrane permeability, and its application to the development of
MDMX-p53 inhibitor
Kyosuke TSUMURA*, 1, Mai MIZUNO-KANEKO2, Ichihiko HASHIMOTO1,
Kenta MIYAHARA1, Masahiro KOCHI1, Noriyuki OHASHI1, Koo SUZUKI1,
Takashi TAMURA2
1

Analysis Technology Center, FUJIFILM Corporation, 2Synthetic Organic Chemistry Laboratories,
FUJIFILM Corporation( * E-mail: kyosuke.tsumura@fujifilm.com )Cyclic peptides are being investigated as potential intracellular protein-protein interaction (PPI) inhibitors. The
development of an in vitro peptide screening method has made it possible to select cyclic peptides with high affinity to atarget protein. On the other hand, the implementation of cell membrane permeability into about 10-residue peptides has
been studied in various ways, but only cyclosporin A is known as a pharmaceutical product.
In this study [1], in order to find the conditions necessary for cell membrane permeability of cyclic peptides with
about 10 residues, we synthesized several cyclic peptides and investigated their cell membrane permeability (MDCKII), polar surface area (EPSA), and conformation in chloroform solution as a conformation in the cell membrane(NMR). We found that the cell membrane permeability becomes equivalent to that of cyclosporin A when the polar
surface area and the steric shape factor r-value determined from the conformation in chloroform solution are below acertain threshold value (Fig.). Applying this condition to a known 10-residue cyclic peptide MDMX-p53 inhibitor withhigh affinity but low cell membrane permeability, we investigated the improvement of cell membrane permeability
by maintaining affinity. As a molecular design method, we developed a scheme where the following four steps arerepeated until the desired peptides are acquired: (1) design of cyclic peptide candidates that meet both target binding
and the cell membrane permeability condition by simulation, (2) synthesis of the designed cyclic peptide candidates,(3) measurement of affinity and cell membrane permeability of them, and (4) mechanism elucidation by EPSA, NMR,
and simulation. We would like to discuss how to design cyclic peptides for intracellular PPI inhibition by introducing
empirical data of the above scheme.
[1] Mai Mizuno-Kaneko et al., ACS Medicinal Chemistry Letters, in press.

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P08-06
Improvement of ligand binding affinity prediction based on fair dataset
partitioning by bias reduction
Nozomu YAMAZAKI*, Takashi ISHIDA
Department of Computer Science, School of Computing, Tokyo Institute of Technology( * E-mail: yamazaki@cb.cs.titech.ac.jp )Absolute binding affinity prediction of protein-ligand complexes plays an important role in drug discovery. Models
that predict the binding affinity from the 3D structure as input, such as deep learning models using 3D convolutional
neural networks and graph neural networks, have been developed in recent years. And many of them use known 3D
structures and their binding affinities for training their models. However, it has been recently revealed that in suchdatasets, there is a bias that structurally similar ligands tend to have similar binding affinities, even when combined withdifferent proteins. Therefore, when simple random data partitioning is used, the binding affinity can be predicted fromthe ligand structure alone. The same can be said for protein structures. In this study, we proposed a data partitioning
method and tried to improve the accuracy of the ligand affinity prediction by creating a dataset that does not lead toperformance by just learning the bias in the dataset. Furthermore, we evaluated the proposed data partitioning methodby training and predicting models using the proposed data partitioning. The results show that the prediction accuracyof the proposed partitioning method based on ligand structure alone is much lower than that of random partitioning,suggesting that the prediction accuracy of existing prediction models is highly dependent on the ligand bias. We also
created an unbiased training dataset by removing similar ligands or proteins that have similar binding affinities andtrained a new model.

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P08-07
Physiologically based pharmacokinetic model describing
disposition after repeated oral ingestion of ergothioneine,a food-derived amino acid with neurogenesis activity
Yudai ARARAGI*, 1, Takahiro ISHIMOTO1, Satoshi MATSUMOTO2, Makoto SUZUKI2, Yukio KATO1
Faculty of Pharmacy, Kanazawa University, 2L·S Corporation Co. Ltd.

1

( * E-mail: y.a.swim.k@stu.kanazawa-u.ac.jp )An antioxidant amino acid ergothioneine (ERGO) is not synthesized in mammals, but ingested from daily food andorally absorbed by its specific transporter OCTN1/SLC22A4. Despite being a hydrophilic property, orally ingested
ERGO is distributed to various organs including the brain, and is stably maintained in the body due to OCTN1-mediatedrenal reabsorption. We have previously reported that repeated oral administration of ERGO enhances cognitive function innormal mice, and that ingestion of ERGO-containing food for 12 weeks improves verbal memory in humans. However,
the reason why repeated administration for such a long period is required for ERGO-induced cognitive enhancement
remains unknown. It is important to reveal the critical factors for the brain distribution and onset of pharmacologicaleffects of ERGO because ERGO is currently used as health food to improve brain function. In the present study, we
performed pharmacokinetic (PK) studies of ERGO in mice to establish its physiologically based pharmacokinetic (PBPK)
model describing the disposition after repeated oral administration. A deuterium-labeled ERGO (d9-ERGO) was used
to distinguish it from ERGO present in the body. The d9-ERGO administered intravenously (1-10 mg/kg) was rapidly
distributed to the liver, adipose, muscle, and skin, whereas the brain distribution was relatively slower. Cumulative
urinary excretion of d9-ERGO for 4 h was also examined and found to be at most ~1% of doses at the lowest dose, butbecame much higher at the higher doses, suggesting saturation of renal reabsorption. These results demonstrate thatERGO is efficiently distributed to the body and minimally excreted into the urine, requiring a long period to reach asteady state. After oral administration (10 mg/kg), on the other hand, ERGO concentration in each organ was lower
than that after intravenous administration, suggesting a first-pass effect after oral administration. Our previous PK
study suggested that ERGO was efficiently absorbed in the small intestine and highly taken up by the liver in a bloodflow-limited condition. Thus, orally absorbed ERGO may be highly taken up by the liver and thereby distributed to thewhole body. To develop the PBPK model the nonlinear disposition of ERGO was described by Michaelis Menten-typesaturable uptake in various organs. The PBPK model was constructed by simultaneously fitting to all the PK data using
a nonlinear least-squares method. The simulation based on the PBPK model has finally proposed that saturation of renal
reabsorption may potentially lead to nonlinear PK, and that distribution to the brain after oral administration was slow
due to efficient hepatic uptake and limited membrane permeability in the brain. Further incorporation of ERGO-induced
neurogenesis and cognitive enhancement into the model may be needed to clarify the critical factors for pharmacologicalactivity by constructing PBPK/PD model.

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P08-08
Evaluation of the ability of 3D-RISM theory to identify the correctbinding mode of small molecule ligands
Masatake SUGITA*
Department of Computer Science, School of Computing, Tokyo Institute of Technology( * E-mail: sugita@bi.c.titech.ac.jp )In docking calculations, interactions of a ligand molecule with a protein are evaluated based on empirical scorefunctions. The function for solvation has a particularly strong empirical aspect. This is because of the difficulty in
explicitly treating water molecules in docking calculations, even though the solvation free energy strongly depends on
the position and orientation of surrounding water molecules.
On one hand, it has been suggested that the 3D-RISM theory, a statistical mechanics theory of liquids, can be used
to predict the distribution of ions and small molecule fragments by considering their interactions with the surrounding
solvents.[1,2] However, since 3D-RISM theory also yields distributions that appear to be false-positive binding sites, it
is unclear whether it can accurately predict the binding sites of small molecules with multiple fragments.
In this study, we generated multiple decoy binding modes of inhibitors at the binding site of MDM2 by means of
AutoDock Vina, and predicted their binding affinities using 3D-RISM to confirm whether the correct binding mode canbe identified.
[1] N. Yoshida et al., J. Am. Chem. Soc., 128, 12042 (2006)[2] M. Sugita et al., J. Chem. Theory Comput., 16, 2864 (2020)― 260 ―

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P08-09Design of cyclic peptides targeting protein-protein interactions
using AlphaFold
Takatsugu KOSUGI, Masahito OHUE*
School of Computing, Tokyo Institute of Technology( * E-mail: ohue@c.titech.ac.jp )Protein-protein interactions (PPIs) play a role in many biological and biochemical processes, such as signaltransduction and metabolism in cellular activities. More than 930,000 human PPIs have been identified. Since the early
2000s, PPIs have attracted considerable attention as potential drug targets for human diseases. However, it is difficult todesign drugs for PPIs based on conventional rules, such as Lipinski’s rule of five or QED, because their physicochemical
characteristics are very different from those of conventional drug targets, resulting in challenges in PPI drug discovery.
In recent years, linear peptides with more flexible backbone, and more specificity than small molecules. Therefore they
have attracted attention as a modality for targeting PPIs. However, peptides have low membrane permeability and low
stability in vivo, which remains a challenge. By contrast, cyclic peptides can be designed for membrane permeability,
targeting intracellular PPIs, which are difficult with linear peptides and antibodies, and are more resistant to peptidases
and other digestive enzymes due to the lack of termini. Furthermore, the restriction of the cyclic structure makes it
easier to fold stably without secondary structures. In these respects, cyclic peptides are a more effective PPI drugtargeting modality than linear peptides and have advantages in terms of membrane permeability and stability. Thus,
cyclic peptides are a promising modality for targeting PPIs, but it is difficult to predict the structure of a target proteincyclic peptide complex or to design a cyclic peptide sequence that binds to the target protein using computational
methods. Recently, AlphaFold with a cyclic offset has enabled the prediction of the structure of cyclic peptides, thereby
enabling de novo cyclic peptide designs. We
developed a specific protein-cyclic peptide complex
offset and implemented it in ColabFold to predict the
RMSD of the cyclic peptide region in head-to-tailcyclic peptide complexes prediction with highaccuracy. Furthermore, applying AfDesign peptide
binder hallucination to the cyclic peptide complex
offset enabled de novo binder hallucination of cyclic
peptides, with MDM2 as the target protein. A total of
88% of the designed sequences had lower normalizedbinding energies than that of the native peptides.
Furthermore, 27% of all sequences had pLDDT > 90.
Furthermore, the alignment of the designed
sequences and drawing of sequence logos showedthat the positions of the aromatic amino acids
essential for binding to the native peptides wereconsistent. Furthermore, the method was applied to
12 other protein-peptide complexes and one proteinprotein complex. In this study, we showed that our
method could be used to design putative cyclic
peptides targeting PPI using AfDesign binder
hallucinations with cyclic peptide complex offsets
for proteins and cyclic peptides.

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P08-10PROTAC molecular linker design using fragment linking method
Ryota ISHIZAWA*, Masahito OHUE
Department of Computer Science, School of Computing, Tokyo Institute of Technology( * E-mail: ishizawa@li.c.titech.ac.jp )Proteolysis targeting chimera (PROTAC), which uses the ubiquitin-proteasome system to degrade target proteins,is a new modality that can target proteins that cannot be targeted by conventional molecular targeting drugs. PROTAC
is heterobifunctional small molecule consisting of two ligands joined by a linker: one ligand recruits and binds a target
protein while the other recruits and binds an E3 ligase. It is known that the length and composition of the PROTAC
linker affects the physical properties and target specificity of PROTAC. However, methods for designing optimal linkers
have not been established, and computational techniques for linker design are expected to be developed. De novo linker
design methods based on machine learning and other methods have been tried, but they suffer from a lack of training
data and the generation of unstable functional groups. Therefore, we focused on the fragment linking method, which
is another linker generation task. Fragment linking is a strategy for designing a linker that connects two fragments,
which is a type of fragment-based drug design (FBDD). In this study, we designed a linker for PROTAC by applying
the fragment linking method, which generates a linker connecting two fragments. To evaluate the performance of thedesigned linker, we modeled a ternary complex consisting of a protein target, PROTAC, and E3 ligase and comparedwith a simple linker.

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P08-11
Efficient enzyme discovery from microbial gene databases andmolecular surface features of 3D structures
Shigeru SAKURAI*, 1, Akiho Harima MORIMOTO1, Takuya YODA1,
Masahito HOSOKAWA1, 2, Soichiro TSUDA1
1

bitBiome, Inc., 2Department of Life Science and Medical Bioscience, Waseda University( * E-mail: shigeru.sakurai@bitbiome.co.jp )In bioprocesses that utilize biocatalysts for industrial applications, it is common to employ non-natural organic
compounds as substrates, distinct from those within living organisms. Therefore, for the development of such novel
processes, the understanding of natural catalytic mechanisms within living organisms alone is insufficient; it requires theutilization of both “diverse enzyme sequence database” as a source of genetic resource and “search tool capable ofclassifying enzymes by substrate type” for efficient exploration.
The environmental microbiomes, composed of microorganisms adapted to diverse environments, serves as a genetic
resource enabling the construction of a diverse enzyme sequence database for the discovery of novel biocatalytic
enzymes. Leveraging a novel microbial single-cell genome sequencing method called bit-MAP®, we have independently
sampled and analyzed microbial samples inhabiting diverse environments throughout Japan. This effort has resulted in
the creation of a microbial gene database called bit-GEM, encompassing over 1.2 billion microbial genetic sequences.
Furthermore, we developed an in-silico enzyme screening method, bit-QED, that help us find enzymes of interest
quickly and efficiently. bit-QED combines conventional bioinformatics sequence analysis with our proprietary “surface
feature analysis” (Figure below). Because proteins(enzymes) interact with organic compounds
on molecular surfaces, we hypothesized thatclassifying each enzyme based on surface featuresnear the substrate binding pocket could facilitate
the classification of candidate enzymes according
to their responsive substrates. Therefore, we
calculated five crucial molecular surface features(e.g., surface shape, surface charge, hydrophobicity,hydrogen bonding, and so on) as voxel information,
followed by clustering analysis.
Leveraging the method, we have successfully
explored a diverse array of enzymes in bit-GEM
for industrial applications including pharmaceuticalintermediate synthesis and food processing. In thispresentation, we will show cases of transaminases;
a class of enzymes catalyzing reactions between
amino acids and α-keto acids.

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P08-12Development of a 3D-structure-based drug response model to help
annotating kinase mutations for precision medicine
Kosuke MARUYAMA*, 1, 2, Takashi NAKAOKU1, Mitsugu ARAKI3, Shigenari NUKAGA1,
Yasushi OKUNO3, Takashi KOHNO1Division of Genome Biology, National Cancer Center Research Institute, 2Department of Cancer
Cellular Signaling, Graduate School of Medicine, The University of Tokyo, 3Graduate School of
Medicine, Kyoto University

1( * E-mail: komaruya@ncc.go.jp )Introduction
Data of efficacy of tyrosine kinase inhibitors (TKIs) for tumors with driver oncogene alterations have been
accumulated. However, secondary mutations causing TKI resistances often occur during therapy. This study is aimed toelucidate mechanisms of drug resistance based on three-dimensional structure analysis and support drug selection for
individual mutations.
MethodRET was selected as the model molecule. Secondary mutation data was collected from previous studies. Ba/F3 celllines expressing RET fusion proteins with and without secondary mutations were examined for sensitivity to several
RET TKIs. Molecular dynamics (MD) simulation was performed starting from the x-ray cocrystal structures including
recently solved ones, to assess accommodation capacity and interaction between protein and ligands. Furthermore,clustering of the simulation structures was performed to improve the accuracy of the analysis.
Results
Ba/F3 cell lines expressing RET fusion proteins with and without secondary mutations were established. In vitro
and in silico assay have been performed for RET kinase protein harboring mutations and multiple drugs, totaling morethan 150 combinations examined. The effect of cavity change by mutation has been evaluated, and alterations in binding
stability due to interaction changes are being examined using MD simulations.

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P08-13
Evaluation of binding properties between metabolic glutamate
receptor 5 and ligand using fragment molecular orbital method
Daiki ODAJIMA*, 1, Taro IMAHIRO1, Shuhei MIYAKAWA1, Daisuke SAKAI1,
Yuma HANDA1, Takayuki FURUISHI1, Etsuo YONEMOCHI1,
Midori TAKIMOTO-KAMIMURA2, 3, Kaori FUKUZAWA1, 3
School of Pharmacy and Pharmaceutical Sciences, Hoshi University, 2CBI research institute,3
School of Pharmaceutical Sciences, Osaka University

1( * E-mail: s191069@hoshi.ac.jp )Metabolic glutamate receptor 5 (m-Glu5), a type of GPCR, is a promising drug target because of its wide distribution
in the central nervous system. In this study, we evaluated the ligand-binding properties of m-Glu5 using the fragment
molecular orbital (FMO) method.
A total of 11 negative allosteric modulators with different inhibitory activity value (IC50) were used as ligands.
Using X-ray crystal structures (PDBID:6FFI) as templates, docking simulations were performed on the integrated
computational chemistry system MOE (CCG) followed by FMO calculations at the MP2/6-31G* level. The binding
energies and inter-fragment interaction energies were calculated and pair interaction energy decomposition analysis
were performed. Based on the characteristics of the obtained interaction energies, new compounds were designed byintroducing substituents into the ligand that could acquire new interactions with surrounding amino acid residues andcrystal water, and similar FMO calculations were performed.
As a result, a correlation was obtained between the 11 ligands and their respective IC50 values (R 2=0.48), indicatingthat the ligands are stabilized mainly through hydrogen bonding with Ser969 and dispersion interactions with thesurrounding residues. Next, observation of the ligand binding site suggested that Ser654 and Ser658 could form
interactions with the functional groups attached to the ligand, as shown in the figure. The addition of several functionalgroups to the ligands revealed that the introduction of methanol and ethanol groups with hydroxy groups showed morestable binding energies than the known compounds. This is because the hydroxy group not only form a new hydrogenbond with the above Ser residues but also has less steric hindrance with other surrounding amino acid residues. In the
future, we plan to investigate the introduction of various substituents and design compounds with stronger binding
properties.

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P08-14Docking prediction of cyclic peptide-protein complex by AlphaFold
Multimer with cyclic offset
Keinoshin TOGASHI*, Takatsugu KOSUGI, Masahito OHUE
Department of Computer Science, School of Computing, Tokyo Institute of Technology( * E-mail: togashi@li.c.titech.ac.jp )Cyclic peptide drugs, which combine the benefits of both small and large molecule therapeutics, have garneredconsiderable interest in recent years. Several of these drugs have been developed, each showcasing diverse mechanismsof action, including inhibiting protein-protein interactions. Alongside these developments, advancements in experimental
techniques have expanded our understanding of cyclic peptides' activities and their target proteins. Despite this progress,
the comprehensive analysis of complex structures through experimental methods remains a challenge due to significant
costs and technical hurdles. Consequently, the development of computational approaches for predicting these complex
structures has become imperative, facilitating the process of rational drug design.
In addressing this, we explored the feasibility of applying the recently introduced ʻcyclic offsetʼ method from
AlphaFold2, designed for predicting single cyclic peptide structures, to AlphaFold Multimer. Our aim was to enhancethe docking prediction of cyclic peptide-protein complex structures. Our study encompassed an in-depth analysis of
cyclic peptide properties, including their cyclization patterns, and examined the relationship between pLDDT and
prediction accuracy. Furthermore, we compared our proposed approach with the conventional docking-based method,
AutoDock CrankPep.
Our findings show that predicting intricate structures in interactions between cyclic peptide drugs can be successfully
achieved using a deep learning-based model, with accuracy comparable to, or even surpassing, that of the conventional
docking-based method, AutoDock CrankPep. Moreover, our deep learning-based model demonstrates remarkable speed
compared to traditional docking-based methods, conferring a substantial advantage.
This has led to the successful application of the cyclic offset approach, initially designed for standard cyclic peptides,
which has effectively demonstrated the capability to predict diverse cyclic peptide-protein complex docking structureswith high accuracy.
Furthermore, the observed correlation between the model's reliability predictions (pLDDT) and the evaluation ofpredicted structures introduces promising criteria for dependable predictions. This development underscores the power
and versatility of our approach, marking a significant advancement in the field of cyclic peptide structure prediction anddrug design.

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P08-15
Application of Parallel Cascade Selection Molecular Dynamics forgenerating virtual screening models
Takumi HIRAO*, 1, Genki KUDO2, Yoshino RYUNOSUKE3, 4, Takatsugu HIROKAWA3, 4
Master’s Program in Medical Sciences, Graduate School of Comprehensive Human Sciences,
University of Tsukuba, 2Physics Department, Graduate School of Pure and Applied Sciences,
University of Tsukuba, 3Division of Biomedical Science, Faculty of Medicine, University ofTsukuba, 4Transborder Medical Research Center, University of Tsukuba

1( * E-mail: t.hirao.tsukuba@gmail.com )Structure-Based Virtual screening (SBVS, VS) is a method to evaluate small molecule compound libraries basedon structural information of target proteins and select compounds with high potential to bind to target proteins [1]. 3Dtarget protein structures, such as experimental structures or homology models, are used for the VS, and the accuracy of
VS is highly dependent on these structures. Recently, with the advent of AlphaFold2 (AF2) [2], the potential number of
3D protein structures available for VS has increased rapidly. However, previous studies have reported that the predictedstructure by AF2 is often unsuitable for docking simulations [3]. Although some advanced docking methods withmolecular dynamics simulations, such as ensemble docking [4] or Induced Fit Docking [5], are proposed to improve theprotein structure considering the flexibility of the receptor, these methods have limitations in automatically obtaining
optimal VS models from a large structural space. In this study, we proposed a refinement method for virtual screening
models from various initial structures of the target proteins using Parallel Cascade Selection Molecular Dynamics(PaCS-MD) that is a conformational sampling method with repetition of multiple MD and the selection of appropriate
configuration [6]. In our protocol, we attempted to incorporate as an objective function of PaCS-MD the increase in the
AUC value, which indicates the better the discrimination ability between the active and decoy compounds. In addition,
we will discuss performance differences between the protein structures with holo and apo states in PaCS-MD.
[1] Zhu H, et al. A Comprehensive Survey of Prospective Structure-Based Virtual Screening for Early Drug Discovery
in the Past Fifteen Years. Int J Mol Sci. 15;23(24):15961, (2022).
[2] Jumper J, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 596(7873):583-589, (2021).
[3] Scardino V, et al. How good are AlphaFold models for docking-based virtual screening? iScience. 26(1):105920,(2022).
[4] Amaro RE, et al. Ensemble Docking in Drug Discovery. Biophys J. 114(10):2271-2278, (2018).
[5] Miller EB, et al. Reliable and Accurate Solution to the Induced Fit Docking Problem for Protein-Ligand Binding. J
Chem Theory Comput. 17(4):2630-2639, (2021).
[6] Harada R, et al. Parallel Cascade Selection Molecular Dynamics (PaCS-MD) to generate conformational transition
pathway. J Chem Phys. 21;139(3):035103, (2013).

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P08 量子構造生命科学 / Computer-Aided Drug Design Quantum-Structural Life Science / Computer-Aided Drug Design

P08-16
Cryo-EM structures of mitochondrial ABC transporter ABCB10 in apo
and biliverdin-bound form
Jack YAN*, Yifan HU*, Jiaquan WU
CryoEM department, Wuxi Biortus Biosciences Co., Ltd.

( * E-mail: yifan.hu@wuxibiortus.com )ABCB10 is a member of ABC transporter superfamily that locates in the inner membrane of human mitochondria,with the NBDs within the mitochondrial matrix. It was first identified by screening genes regulated during erythroid
differentiation by the transcription factor GATA-1 and named ABC-me (ABC-mitochondrial erythroid). It was foundlater to play crucial roles in antioxidative stress and stabilization of the iron transporter mitoferrin-1. ABCB10 isessential for hemoglobin synthesis and erythropoiesis and ABCB10 knocked out mice die at day 12.5 of gestation.
ABCB10 deletion in liver protects obese mice from insulin resistance, steatosis and hyperglycemia. Multidrug
resistance-like 1 (Mdl1), the yeast homolog of ABCB10, was identified to transport peptides with molecular massesof ~600 to 2100 daltons. It is therefore hypothesized that ABCB10 is a peptide transporter. Recently, it was found thatthe substrate of ABCB10 is biliverdin (BV). However, whether biliverdin can interact with ABCB10 directly and how
ABCB10 exports biliverdin out of the mitochondria is unclear. Here we report the cryo-EM structures of ABCB10 in
apo (ABCB10-apo) and biliverdin-bound form (ABCB10-BV) at 3.67 Å and 2.85 Å resolution, respectively. ABCB10apo adopts a wide-open conformation and may thus represent the apo form structure. ABCB10-BV forms a closed
conformation and biliverdin situates in a hydrophobic pocket in one protomer and bridges the interaction throughhydrogen bonds with the opposing one. We also identify cholesterols sandwiched by BVs and discuss the export
dynamics based on these structural and biochemical observations.

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P09 バイオインフォマティクス Bioinformatics

P09-01
Integrated network analysis combining EV’s proteomics andserum/urine metabolomics to explore IPF-specific pathways
Mari Nogami ITOH*, 1, 2, Yoshito TAKEDA3, Jun ADACHI4, Masataka KURODA1, 5,
Yayoi Natsume KITATANI1, 6, Atsushi KUMANOGOH3, Kenji MIZUGUCHI1, 7
1
AI Center for Health & Biomedical research, Bioinformatics Project, National Institute of
Biomedical Innovation, Health and Nutrition, 2Pharmaceutical Sciences, Osaka University
Graduate School of Pharmaceutical Sciences, 3Department of Respiratory Medicine and Clinical
Immunology, Osaka University Graduate School of Medicine,, 4Proteomics Project, National
Institute of Biomedical Innovation, Health and Nutrition, 5Discovery Technology Laboratories,
Mitsubishi Tanabe Pharma Corporation, 6Institute of Advanced Medical Sciences, Tokushima
University, 7Computational Biology, Institute for Protein Research, Osaka University( * E-mail: mari@nibiohn.go.jp )IPF is a poor prognosis interstitial pneumonia with chronic progressing fibrosis in which fibroblasts andmyofibroblasts contribute to the accumulation of extracellular matrix (ECM) proteins, gradually compromising thelung's gas exchange capacity. Two antifibrotic drugs are currently being used, but there is an urgent need to create more
fundamental treatments, which will require an understanding of the mechanisms underlying the disease progression.
For the purpose of constructing a strategy to create new drugs using patient information, we obtained proteomic data in
EVs isolated and purified from serum using the PS affinity method for approximately 600 interstitial pneumonia cohorts
including IPF at Osaka University, as well as the medical information has been collected closer date of blood collection
for each case, then a machine-readable structured database has been constructed. At last year's conference, we reported
that we found several proteins co-occurred with typical IPF clinical data using unique algorism, so named subsetbinding, and further analysis revealed the upstream molecules and drugs that regulate the expression of these proteins.
In the present study, we confirmed that multiple proteins in EVs co-occurring with these IPF data are highly expressedin the fibrotic areas of patients' lungs, and found that these proteins form a characteristic network including carbohydrate
metabolism and lipid metabolism by pathway analysis. Furthermore, we performed a comprehensive analysis of
metabolites in blood and urine of IPF patients, and attempted to integrate the analysis with protein expression data inEVs. As a result, we found that the above-mentioned network is a characteristic metabolic pathway associated with thepathogenesis of IPF, as evidenced by the changes of metabolites in patient blood or especially in urine.

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P09 バイオインフォマティクス Bioinformatics

P09-02
Identification of upstream factors that regulate susceptibility to
drug-induced mitochondrial toxicity in NAFLD
Kazuma HAMADA*, Takashi MIZUMABiopharmaceutics and Molecular Toxicology Unit, Faculty of Pharmaceutical Sciences, Teikyo
Heisei University( * E-mail: k.hamada@thu.ac.jp )Mitochondrial toxicity serves as a central mechanism in drug-induced liver injury (DILI). In our previous studies, we
explored the mechanisms underlying the increased susceptibility to DILI in patients with nonalcoholic fatty liver disease(NAFLD) and found that the susceptibility to drug-induced mitochondrial permeability transition (mPT) is enhanced ina mouse model of NAFLD.
The objective of this study was to elucidate the upstream factors that regulate the liver mitochondrial proteome
in NAFLD. A spontaneous genetic model of NAFLD, db/db mice, was used. Liver mitochondria were isolatedby differential centrifugation, and subsequent analysis was carried out using data-independent acquisition mass
spectrometry. Additionally, upstream analysis was performed on the molecules that showed significant changes in
NAFLD.
We have elucidated the proteomic differences between NAFLD and non-NAFLD mitochondria. As a result, wehave identified significant insights into the regulatory mechanism of mPT susceptibility. The mPT is a phenomenon inwhich the inner membrane permeability is enhanced non-specifically due to channel openings at the interface between
the inner and outer mitochondrial membranes, leading to swelling and membrane damage. This process has garneredattention as a mechanism for release of mitochondrial damage associated molecular patterns, which are known to induce
inflammation and immune responses. Furthermore, our research has also successfully identified upstream factors that
contribute to the control of proteomic changes in NAFLD liver mitochondria. These identified factors hold the potential
to serve as compelling biomarkers for predicting drug-induced mitochondrial toxicity susceptibility in the context of
NAFLD patients.

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P09 バイオインフォマティクス Bioinformatics

P09-03Development of a large-scale database for microbiome
and phenotypic data
Yi-An CHEN*, 1, Attayeb MOHSEN1, Hitoshi KAWASHIMA1, Jonguk PARK1, 3,
Kenji MIZUGUCHI1, 2
1
Artificial Intelligence Center for Health and Biomedical Research, National Institutes ofBiomedical Innovation, Health and Nutrition, 2Institute for Protein Research, Osaka University,
3
Wellness Science Labs, Meiji Holdings Co., Ltd.

( * E-mail: chenyian@nibiohn.go.jp )Recent studies have shown that gut bacteria significantly impact our health in many aspects, including obesity,
inflammation, infectious diseases, and cancer. A database that collates large-scale microbiome data and various
phenotypic data such as diet, nutrient intake, and physical activity is necessary to understand what factors correlate to
gut microbiome composition. The development and construction of the database include data processing and storage (16SrRNA gene amplicon sequencing, shotgun metagenome sequencing, and else). To consistently acquire a large quantityof sequencing data, we have developed a comprehensive pipeline that processes the raw data and generates microbiome
composition and other parameters, such as diversity metrics. Regarding data storage, we have developed MANTA, an
open-source integrated analysis platform that stores the data and enables visualization and correlation analysis. Here, we
present the technical aspects of creating a large-scale database for microbiome and phenotypic data, including the dataprocessing pipelines and some newly developed features of MANTA. The architecture was applied to the constructionof NIBIOHN JMD (Japan Microbiome Database).

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P09 バイオインフォマティクス Bioinformatics

P09-04
A Graph Based Method of Pattern Mining for Spatial
Transcriptome Data
Ayaka MAE*, 1, Shigeto SENO1, Hironori SHIGETA1, Yu-Chen LIU2, Sangeun KIM3,
Daisuke OKUZAKI2, Ryoichi MORI3, Hideo MATSUDA1
1
Graduate School of Information Science and Technology, Osaka University, 2WPI Immunology
Frontier Research Center, Osaka University, 3Graduate School of Biomedical Sciences, Nagasaki
University( * E-mail: ayaka-mae@ist.osaka-u.ac.jp )Gene expression analysis technology has been rapidly developing in recent years, and spatial transcriptome analysis
is one of the technologies that has attracted particular attention. Cells and tissues change their states in time and space,
and to understand the mechanisms of such changes, it is necessary to understand not only the characteristics of cells
but also their spatial locations. Spatial transcriptome analysis can measure and analyze gene expression levels in a cell
population while keeping spatial information such as cell location and adjacency. On the other hand, many unsolved
problems remain in spatial transcriptome analysis, and it is desirable to develop various analysis methods according to
the needs. One is a method to compare tissue section data before and after time-series changes and detect remarkablechanges of patterns. Currently, biological knowledge has been used to classify cell types in tissue section data manually,
but it is desirable to reduce the required prior knowledge as much as possible and promote data-driven analysis in the
future.
In this study, we propose a method of expression pattern mining using graph structure for spatial transcriptome
data. Targeting spot-type spatial transcriptome (Visium) data, our proposed method first constructs a graph structure byconsidering spots as nodes and connecting their relations as edges. The nodes and edges are further classified according
to the types of the cellular population and the cell-cell interaction, and the graph is condensed. Then, graph comparison
or pattern mining is performed by applying a network motif search algorithm. This method allows for the identificationof target areas to be compared and the detection of remarkable patterns of gene expressions. Our method was applied tothe spatial transcriptome data of mouse tissue sections, in which the skin wounding and healing process was observed.

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P09 バイオインフォマティクス Bioinformatics

P09-05Using Inter/Intra-Ensemble Variability for Exploring Dynamics andHeterogeneity of Protein Conformations
Takashi AMISAKI*
Department of Biological Regulation, Faculty of Medicine, Tottori University( * E-mail: amisaki@tottori-u.ac.jp )Protein conformation is directly related to protein function. More than 200,000 structures of proteins, on the basis of
structures deposited in Protein Data Bank (PDB), have been determined experimentally. Many comparative studies of
X-ray structures have been conducted to elucidate relevant conformational changes, dynamics, and heterogeneity.
For comparative studies of protein conformations, the author developed a mixed-model method for estimating
two covariance matrices that represent inter- and intra-ensemble variability in the Cartesian coordinates space. The
method is based on the theory of a random effects model (REM), which is a simple case of mixed models or hierarchicalmodels. The method (REM, hereafter) aligns all target structures simultaneously to estimate two covariance matrices,which represent inter- and intra-ensemble variability. For a study of single protein structures, an ensemble can begrouped, for example, by ligands, and the inter-ensemble variability represents the conformational changes by ligands.
For a study of a family of enzymes, the structures of each enzyme may form an ensemble. In this case, if any motion isshared between enzymes, it should appear in the intra-ensemble variability.
In this study, to confirm the applicability and effectiveness of the method, REM was applied to two protein families.
The conformational dynamics and/or heterogeneity of the proteins were evaluated using the REM-estimated covariancematrices. One of the two examples is the application to Cytochrome P450 family 2 (CYP2) enzymes. The overallarchitecture is preserved among the enzymes, whereas the size of the active-site cavity varies between the enzymes.
The motion of the region from helix F to G is considered to play an important role in the substrate uptake and releaseprocess. Using REM, a total of 162 structures of 2A6, 2B6, 2C8, 2C9, 2D6, and 2E1 enzymes, each comprised of 407
CA atoms, were superimposed to estimate the covariance matrices. The matrices were eigen-decomposed to obtaindominant principal components. The following figure shows the relation between the coordinate in the first principal
component (PC1) and openedness of each structure, for the three estimates. Openedness was determined according
to descriptions, if any, given in references, otherwise denoted as Unknown. Pooled variability was estimated using
traditional superposition without grouping. The results imply PC1's for the pooled and inter-enzyme variability
describes the cavity size, while PC1 for the intra-enzyme variability captures the open/close motion. This was anillustrative example of obtaining a factor-adjusted variability, indicating the effectiveness of the developed method,
REM.

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P09 バイオインフォマティクス Bioinformatics

P09-06
AbXtract™ in Orion-Antibody Discovery in the CloudChiharu KONDA*, 1, Suhani NAGPAL1, Jesper SOERENSON1, M Frank ERASMUS2OpenEye, Cadence Molecular Sciences, 2Specifica, Inc1( * E-mail: ckonda@eyesopen.com )Antibody discovery approaches that only rely upon random colony picking are often insufficient in their explorationof the underlying diversity. By leveraging Next Generation Sequencing (NGS) and its subsequent analysis, researchers
can more comprehensively explore the complete selection diversity, including underlying patterns in the sequence
dataset.
OpenEye’s Orion® Antibody Discovery suite includes AbXtractTM, an antibody discovery module developed bySpecifica. AbXtract uses important heuristics and machine learning to help antibody engineers and bioinformaticians
prioritize leads from discovery campaigns. By retaining a comprehensive overview of the selected output withprocessed NGS outputs with AbXtractTM, the user can now easily query a dataset of highly functional binders withsimilar or distinct epitopes which exhibit favorable developability properties to more readily uncover potential antibody
therapeutics.
Our research establishes NGS guidelines for antibody drug discovery, highlighting the benefits of this technology forincreasing the variety of HCDR3 clusters, the number of high affinity antibodies, the total number of antibodies that canrecognize various epitopes, and lead prioritization. Although this study was limited to the selection of antibodies against
SARS-CoV-2, we believe the NGS analyses described will provide valuable resonance in other campaigns, whether invitro or in vivo.

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P09 バイオインフォマティクス Bioinformatics

P09-07
A New Workflow Automating
Data Analysis in Gene Expression Screens Across Assay Formats
Produces Consistent Results at Scale and Efficiency
Marina KAWAI*, 1, Juan FLOREZ2, Romain CHASSEFEYRE3, Gabriela Nass KOVACS2,
Xavier MARECHAL3, Stephan HEYSE2, Eric PERRET3, Stephan STEIGELE2
1

Genedata KK, 2Genedata AG, 3Evotec (France) SAS( * E-mail: marina.kawai@genedata.com )Developing RNA-targeted drugs holds the promise for a dramatic increase in therapeutically addressable biologicalprocesses that are far beyond the reach of protein-targeted drugs, such as pre-mRNA splicing or modulation of gene
expression by noncoding RNAs. Quantitative reverse transcription PCR (RT-qPCR) is a recent, scalable technique
for small molecule screening, allowing determination of up- or down-regulation of gene expression induced by amolecule under test. Evotec has developed accelerated experimental and data analysis workflows that allow RT-qPCR
assays to be run at the scale of HTS. In conjunction with the process automation, we present here a highly scalable andhighly automated data analysis workflow. We show how it can be used for the rapid characterization of large sets ofmolecules by induced expression changes, identification of cytotoxic compounds, and in-vitro estimation of therapeutic
windows from shifts in concentration-response curves. This workflow has been implemented in Genedata Screener. It
enables Evotec to streamline data processing from such experiments by a seamless integration with standard HTS data
analysis processes - starting from compound logistics to one-click reporting of final results, significantly contributing toshortened cycle time. We will discuss the advantages and scientific novelties of our system at the poster presentation.

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P09 バイオインフォマティクス Bioinformatics

P09-08
Application of a doc2vec-based machine learning method toward
more reliable prediction of human protein-protein interactions
Satsuki UETA*, 1, Yoichi MURAKAMI2
1

Graduate School of Informatics, Tokyo University of Information Sciences, 2 Department ofInformatics,Tokyo University of Information Sciences( * E-mail: g22002us@edu.tuis.ac.jp )Reliable information about protein-protein interactions (PPIs) is required for a deeper understanding of biologicalprocesses, pathways, and functions. However, experimentally identifying complete PPI networks in cells and organisms
remains a challenging task. Recently, distributed representations of protein sequences, i.e., converting protein sequences
into fixed-length vectors using word embeddings or document embeddings, have been applied in various researches
and their effectiveness in natural language processing techniques has been reported. In the field of PPI prediction,these embedding methods have also been applied and its effectiveness has been reported. In this work, we applied an
unsupervised document embedding technique (doc2vec) to represent protein sequences as fixed-length low-dimensional
vectors. A random forest (RF) classifier using these doc2vec-based vectors was effectively trained on an imbalanced
training dataset of human PPIs with a positive to negative sample ratio of 1:10. As a result, our method achieved average
AUC of 0.84 and average MCC of 0.45 in performance evaluations using a 1:10 imbalanced independent test dataset.
In addition, the potential ability of our proposed method to classify positive and negative PPIs was demonstrated in
the performance evaluation assuming unknown proteins. Our proposed method suggests that the doc2vec encoding
technique effectively captures protein sequence contextual information related to the corresponding protein-protein
interactions. We hope that our study will contribute useful predictors to accelerate the discovery of novel human PPIs.

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P09 バイオインフォマティクス Bioinformatics

P09-09Toward the development of an information infrastructure to realize
a medical examination room with genome information
Minae KAWASHIMA*, 1, 10, Soichi OGISHIMA2, 10, Natsuko YAMAMOTO3, 10,
Tomohiro KURODA6, 10, Masayoshi TSUTSUMI4, 10, Katsushi TOKUNAGA7, 10,
Kaori MUTO8, 10, Takayuki YOSHINO9, 10, Issei IMOTO5, 10, Kazuto KATO3, 10,
Member GENOMIC MEDICINE EXECUTIVE COMMITTEE10
Database Center for Life Science (DBCLS), Research Organization of Information and Systems(ROIS), 2INGEM/ToMMo, Tohoku University, 3Graduate School of Medicine, Osaka University,
4
Japan Registered Clinical Laboratories Association, 5Research Institute, Aichi Cancer Center,6
Hospital, Kyoto University, 7Genome Medicine Science Project, National Center for Global
Health and Medicine, 8The institute of medical science, The University of Tokyo, 9Hospital East,
National Cancer Center, 10Genomic Medicine Executive Committee, Japan Usability Medical
Information Promote Conference (JUMP)
1( * E-mail: minaex@dbcls.rois.ac.jp )The Law for the Promotion of Genome Medicine, a law to promote genome medicine in an appropriate, fair, and justmanner, was passed by the 211th ordinary Diet session on June 9 and came into effect on June 16, 2023. The Act sets
forth the principle of realizing world-class genome medicine and ensuring that the public can widely enjoy the benefitsof such medicine. It also mandates that the government formulate a basic plan and clearly states that there will be no
unfair discrimination based on genetic information. It is necessary to establish a research and medical system that meetsthe principles of the Act.
In order for the public to widely benefit from genomic medicine, i.e., to realize an examination room that can provide
medical care using genomic information, an information infrastructure is needed to interpret the results of genetic tests
ordered from the examination room based on evidence and to support appropriate diagnosis, treatment, and preventive
medicine. To this end, it is necessary to accumulate the set of variants and clinical phenotypes, systematically elucidate
clinical significance through functional analysis of variants of unknown significance (VUS), etc., and build consensus
among experts to evaluate and determine the certainty of the pathogenicity. In order to formulate evidence for Japanesepatients, it is necessary to establish an information infrastructure to utilize large-scale clinical and genomic data in thereal world. In this presentation, we propose an information infrastructure to realize an examination room that can utilize
genomic information widely throughout Japan.

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P09 バイオインフォマティクス Bioinformatics

P09-10
MGeND: A genetic variant database for genomic medicine
Yosuke KAWAI*, 1, Kazuko UENO1, Mayumi KAMADA2, Kenjiro KOSAKI3,
Yasushi OKUNO2, Katsushi TOKUNAGA1
National Center for Global Health and Medicine, 2Graduate School of Medicine, Kyoto University,3
Keio University School of Medicine

1( * E-mail: ykawai@ri.ncgm.go.jp )Understanding genetic diseases relies on genetic information. This information not only aids in predicting disease
outcomes but also in tailoring treatments for individuals. The advancement of modern technologies, in particular the
next generation sequencing, has made the genome analysis the important tool in clinical practice. However, making
sense of the many variants found in genome analysis is challenging. To address this challenge, the Medical Genomics
Japan Variant Database (MGeND) was created. Itʼs a freely accessible resource that compiles variant data from clinical
trials, laboratory tests, and published sources. This database has collected over 200,000 variants associated with rarediseases, complex diseases, and cancers. To date, disease variant databases have been constructed based on research
conducted in the United States and Europe, resulting in a relative paucity of variant data for Asian populations. In
MGeND, efforts have been made to bridge this gap by incorporating results from studies and clinical tests conductedin Japan, as well as by registering variants specific to Japanese populations reported in publications and other sources.
As a result, MGeND contains distinctive variants not found in databases such as ClinVar. In this study, we undertook acomprehensive analysis of the variants cataloged in MGeND to date. This analysis included a comparative analysis withother databases as well as a detailed examination of the frequency distribution intrinsic to the registered data. Based onthese results, we discuss the practical utility of this database and its possible future directions.

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P10 臨床インフォマティクス Clinical Informatics

P10-01
A study on the relationship between "autonomous research"and large-scale generative AI
Kohtaro YUTA*
In Silico Data,Ltd.

( * E-mail: k-yuta@insilicodata.com )Currently, we are transitioning from an era centered around “machines + computers,” with a focus on automatedresearch, to a new "information" era where vast amounts of data are generated and highly utilized. In this shift, largescale generative AI models capable of learning from massive datasets have been introduced and made publicly available.
These large-scale language models (LLMs) operate at a level far beyond conventional network-based AI, performing
tasks at high speed and with exceptional accuracy, surpassing the capabilities of deep learning, among others. They
not only handle routine tasks for researchers such as data analysis, summarizing text, and providing insights but also
generate text based on specified content. They can instantly create works that are considered typical of creative humanendeavors at the highest level, including novels, poems, haikus, and more, following the inputted protocols. This
demonstrates that LLMs can perform creative and intellectual activities that were previously deemed impossible forcomputers.
The functionalities and achievements of the above-mentioned generative AI cover most of the tasks related to the“autonomous research” proposed by me. Now, I primarily use ChatGPT, other generative AI models, particularly thosedealing with images and photos, have the potential to make significant contributions not only in exhibitions and contests
but also in medical image analysis.
Reporting on Yuta's latest application of ChatGPT:
Yuta utilized ChatGPT for the most recent application, demonstrating the potential of this technology. Peer reviewing
abstracts for academic conferences requires advanced knowledge and information that researchers can provide.
Reviewers also need substantial experience and accomplishments in their respective research fields, making it one of thetypical tasks associated with Yutaʼs proposed “autonomous research.”
For the CBI 2023 conference, Yuta used ChatGPT to assess the abstractsʼ suitability for presentation at theconference. Yuta handled a total of 21 abstracts. Yuta issued a protocol to ChatGPT, instructing it to determine whether
the abstracts were suitable for presentation at the CBI conference. ChatGPT provided judgments for 20 of the abstracts,
and Yuta reviewed the results. One abstract encountered an error with ChatGPT, so Yuta manually checked it. The final
judgments were made based on ChatGPT's conclusions and assessment text, with the entire process taking less than anhour.
If Yuta were to perform this task using the normal process, it would treat approximately four to five abstracts perday, taking about four days to complete. This result highlights a significant improvement in work efficiency achieved
with ChatGPT's assistance. Further details will be presented in a poster.

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P10 臨床インフォマティクス Clinical Informatics

P10-02
Predicting indications of existing drugs for different diseases
Naofumi SEIRA*Life science Dept., MOLSIS Inc.

( * E-mail: seira.naofumi@molsis.co.jp )Traditional drug discovery requires a great deal of investment in time and resources. Despite these investments,
the probability of success in new drug developments is very low. Therefore, innovative, more efficient, and less costly
strategies are urgently needed for new drug developments. Drug repositioning, a strategy for finding new indications
for existing drugs, is more efficient and less costly than traditional strategies because it leverages existing toxicity andsafety information from already approved drugs.
Therefore, we aimed to find new indications for existing drugs using DISGENET plus which is a discovery platform
for human disease genomics [1]. DISGENET plus contains a comprehensive database of genes associated with humandiseases integrated from authoritative databases and the scientific literature. We focused on Alzheimer’s disease, forwhich there are few effective remedies among all human diseases. First, we performed the network analysis to examine
the genes that are strongly associated with Alzheimer’s disease using DISGENET plus scores which were calculated
based on the number of publications and the number of independent databases that reported the association. Next, we
examined the diseases that were strongly associated with genes related to Alzheimer’s disease using DISGENET plus
scores as an index. Finally, we looked at approved drugs that target the selected diseases. In this communication, we will
present the results of such analysis as an innovative strategy for drug repurposing.
[1] DISGENET plus, MedBioInformatics Solutions S.L., https://www.disgenetplus.com/ (accessed 2023-08-02).

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P10 臨床インフォマティクス Clinical Informatics

P10-03
Automatic biomarker discovery for idiopathic pulmonary fibrosis bycorrelation analysis of serum extracellular vesicles proteomic dataand clinical data
Yohey KAMIJO*, 1, 2, Mari N ITOH3, Yoshito TAKEDA4, Masataka KURODA3,
Jun ADACHI5, Yayoi NATSUME-KITATANI3, Kenji MIZUGUCHI3,
Atsushi KUMANOGOH4, Yoshihiro YAMANISHI1, 2
Department of Complex Systems Science, Graduate School of Informatics, Nagoya University,2
Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems
Engineering, Kyushu Institute of Technology, 3AI Center for Health & Biomedical research,
Bioinformatics Project, National Institutes of Biomedical Innovation, Health and Nutrition,
4
Department of Respiratory Medicine and Clinical Immunology, Graduate School of Medicine,
Osaka University, 5Laboratory of Proteome Research, National Institutes of Biomedical
Innovation, Health and Nutrition1( * E-mail: kamijo.yohey.w0@f.mail.nagoya-u.ac.jp )Idiopathic pulmonary fibrosis (IPF) is one of the diseases collectively known as interstitial pneumonias (IPs), in
which extracellular matrix such as collagen accumulates in the interstitium of the lungs. IPF is a chronic, progressive,and poor prognosis disease that causes irreversible loss of lung structure such as honeycomb change. Discriminating
IPF from other IPs is important for appropriate treatment. The diagnosis of IPF is difficult even for specialists, timeconsuming, and bronchoalveolar lavage (BAL) or biopsy can be physically demanding on the patient. Therefore, IPF
biomarkers obtained in a simple and non-invasive manner are required.
In this study we propose a novel machine learning method, which we call multiclass sparse canonical correlation
analysis (MSCCA), to identify biomarkers from two heterogeneous data. We applied MSCCA to serum extracellular
vesicles (EVs) proteomic data constituted with 2416 proteins by data-independent acquisition (DIA) and clinicaldata constituted with 2402 items of lung status as confirmed by images and symptoms from 507 IPs patients and 39
healthy control using the diagnostic group as prior knowledge. In MSCCA, proteins that were strongly correlated with
IPF groups-specific clinical examination items were obtained. Selected proteins had significantly greater quantitativevalues in the IPF group than the other two groups. The newly identified proteins outperformed the previously reported
biomarkers [1] in terms of accuracy in the IPF classification. The proteins identified in this study can be measured easily
and non-invasively and are expected to be useful as IPF biomarkers.
[1] White, E. S, et al. Plasma Surfactant Protein-D, Matrix Metalloproteinase-7, and Osteopontin Index Distinguishes
Idiopathic Pulmonary Fibrosis from Other Idiopathic Interstitial Pneumonias. Am. J. Respir. Crit. Care Med. 2016,
194 (10), 1242–1251.

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P10 臨床インフォマティクス Clinical Informatics

P10-04
Establishment of the precise early prediction models of
low-birth-weight for term and preterm birth groups based on
genetic and environmental factors
Satoshi MIZUNO*, 1, Satoshi NAGAIE1, Gen TAMIYA1, Shinichi KURIYAMA1,
Taku OBARA1, Mami ISHIKURO1, Hiroshi TANAKA2, Kengo KINOSHITA1,
Junichi SUGAWARA1, Masayuki YAMAMOTO1, Nobuo YAEGASHI1, Soichi OGISHIMA1
Tohoku University, 2Tokyo Medical and Dental University

1( * E-mail: samizuno@med.tohoku.ac.jp )Background
Multifactorial diseases, such as hypertension and diabetes, are known to consist of multiple subgroups, and tocontribute of both genetic and environmental factors for disease development. Establishing quantitative early prediction
models is one of the goals of precision medicine. In the past studies, many disease prediction models have beendeveloped, however few studies have been conducted to develop early disease prediction models based on stratification
using both environmental and genetic factors. In this study, we challenged the development of early prediction models
of LBW for an application of prediction model based on environmental and genetic factors in preterm and term groups,
and clarified influential variables for LBW prediction.
Methods
We selected 22,711 neonates, their 21,581 mothers and 8,593 fathers from the Tohoku Medical Megabank Project
Birth and Three-Generation cohort study. To establish early prediction models of LBW for preterm birth and term birthgroups, we trained machine learning models using genetic and environmental factors of lifestyles. We then clarified
influential environmental and genetic factors for predicting LBW in the term and preterm groups.
Results
We identified 2,327 (10.22%) LBW neonates consisting of 1,077 preterm births and 1,248 term births. Our early
prediction models archived the area under curve 0.96 and 0.95 for term LBW and preterm LBW models, respectively.
We revealed that environmental factors regarding eating habits and genetic features related to fetal growth were
influential for predicting LBW in the term LBW model. On the other hand, we identified that genomic features related
to regulation of inflammation and infection reactions are influential genetic factors for prediction in the preterm LBWmodel.
Conclusions
We developed precise early prediction models of LBW based on lifestyle factors in the term birth group andgenetic factors in the preterm birth group. Because of its accuracy, we believe that our prediction models are one of themost notable applications of early prediction models for multifactorial diseases. Based on our prediction models, we
identified parental genetic and maternal environmental factors during pregnancy influencing LBW prediction, which aremajor targets for understanding the LBW to address serious burdens on newborns' health throughout life.

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P10 臨床インフォマティクス Clinical Informatics

P10-05
Exploring the mechanism of action of Scutellaria baicalensis for treating
pulmonary fibrosis using network pharmacology and molecular docking
Okubo RYOSUKE*, Yu JING
Kampo Medicine Pharmacology Research Laboratory, Graduate School of Pharmaceutical
Sciences, Yokohama University of Pharmacy( * E-mail: 161020_ma@yok.hamayaku.ac.jp )BackgroundPulmonary fibrosis (PF) is a chronic progressive lung disease characterized by recurring damage to alveolar
epithelial cells and abnormalities in the repair and healing processes. This ultimately leads to gradual deterioration oflung and respiratory failure. Recent studies have demonstrated that baicalin, a constituent of Scutellaria baicalensis
commonly used in traditional Chinese medicine and Japanese Kampo, exhibits antibacterial and anti-inflammatory
effects. It has been found effective in addressing conditions as pneumonia, pulmonary fibrosis, and upper respiratory
tract infections. The mechanism of action for S. baicalensis involves the inhibition of inflammation-related pathways
like Akt and NF-κB, along with the suppression of cell growth factors such as VEGF. However, S. baicalensis contains a
multitude of different components, suggesting a multi-target effect that goes beyond baicalin alone. Therefore, this study
adopted techniques form network pharmacology and molecular docking to explore the potential pharmacological effects
and cellular signaling pathways of the "multi-component-multi-target system" of S. baicalensis on PF.
Methods
The effective components of S. baicalensis and their corresponding molecular targets were extracted from the
Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, while PF-related genes were collectedfrom the GeneCards database. Overlapping components were identified as potential therapeutic targets. Subsequently,
Metascape software was utilized for enrichment analysis. Finally, Molecular Operating Environment (MOE) was
employed for molecular docking to confirm the binding affinity between S. baicalensis components and therapeutictargets.
Results
A total of 112 genes associated with the effective components of S.baicalensis and 4104 genes related to PF wereidentified. Among these, 74 genes (AR, TP53, AKT1, RELA, TNF, MAPK14, VEGF) were pinpointed as candidate
therapeutic targets. Enrichment analysis indicated the involvement of the survival signal “PI3-Kinase/Akt” pathway
and the apoptosis-associated "p53 signaling pathway" in these therapeutic targets. Furthermore, molecular docking
results revealed a strong affinity between multiple components (Neobaicalein, Salvigenin, Supraene, Stigmasterol,
Epiberberine) of S. baicalensis and multiple therapeutic targets, including AKT1, VEGF, TP53, and RELA.
Conclusion
Our study suggests that various components of S. baicalensis could potentially be effective for PF by activating
survival and apoptosis signaling pathways through multiple target genes. The bioinformatics approach employed inthis crude drug study could serve as a reference for understanding the pharmacological action of other crude drugs andcontribute to further efforts in drug repositioning and drug discovery involving crude drugs compounds.

― 283 ―

P10 臨床インフォマティクス Clinical Informatics

P10-06Development of Biobank Network for Research and Development ofPrecision Medicine in Japan
Soichi OGISHIMA*, 1, Yoshinori MURAKAMI2, Yuichi GOTO3, Yoshihiro ASANO4,
Mizuki MORITA5, Masayuki YOSHIDA6, Manabu MUTO7, Toshihiro TANAKA6,
Hiroyuki NISHIYAMA8, Hiroshi MATSUOKA9, Tomoki KOSHO10, Hiroshi NISHIHARA11,
Hiroki NAKAE12, Hiroshi YOKOTA1, Kinuko OHNEDA1, Kenichi NOGUCHI1,
Satoshi NAGAIE1, Fuji NAGAMI1, Toshihisa TAKAGI13
INGEM/ToMMo, Tohoku University, 2The Institute of Medical Science, The University of Tokyo,
National Center for Global Health and Medicine, 4National Cerebral and Cardiovascular Center,5
Okayama University, 6Tokyo Medical and Dental University, 7Kyoto University, 8Tsukuba
University, 9Kobe University, 10Shinshu University School of Medicine, 11Keio University, 12CIBER,
13
Toyama International University
1

3( * E-mail: ogishima@megabank.tohoku.ac.jp )Biospeimen and data from quality-controlled biobanks are key for research and development in genomic medicine.
In Japan, there exists over 50 biobanks, which stores over 1,210,000 biospecimen provided by over 730,000 doners.
Biobanks hold quality-controlled biospeimen and data, but research institutions have had difficulty finding where tofind the biospeimen and data they need for their own genomic medicine research and development. To address thisissue, we established a Japanese biobank network connecting the three major biobanks (Tohoku Medical Megabankproject, Biobank Japan, National Center Biobank Network and the university hospital’s biobanks (Kyoto University,
Tokyo Medical and Dental University, Tsukuba University, Okayama University, Kobe University, Shinshu University)
in Japan. Our biobank network aims at research and development of operational support for promotion of utilizationof biospecimen and data stored in biobank toward realization of genomic medicine. For this aim, we have developed
a biobank cross­search system on biospecimen and data stored in our biobank network. Research institutions can findbiospeimen and data they need for their own genomic medicine research and development. To implement a crosssearch service for biospecimens and data, we standardized the metadata of biobank holdings of biospecimen and data.
The metadata was standardized by Minimum Information About BIobank data Sharing (MIABIS) in accordance withthe biobank data model of the European biobank network BBMRI-ERIC. Now our biobank network stores 1,043,880
biospecimen and 321,112 molecular data provided by 583,773 donors. We also setup the web-based coordination systemof fast access to biospecimen and data to meet the requests by academic/commercial users using the biobank crosssearch system. Utilization across various biobanks will be promoted by our biobank cross­search system and supportof matching between academic/commercial users, and researches and developments toward realization of genomic
medicine will be expected to be accelerated.

― 284 ―

P10 臨床インフォマティクス Clinical Informatics

P10-07
The relationship between Sessile serrated adenoma/polypand intestinal bacteria
Hibiki MORI*, 1, Sayaka MIZUTANI1, Satoshi SHIBA2, Shinichi YACHIDA3,
Takuji YAMADA4
Yamada lab, Tokyo Institute of Technology School of Life Science and Technology, 2National
Cancer Center, 3Graduate School of Medicine, Faculty of Medicine, Osaka University, 4Tokyo
Institute of Technology School of Life Science and Technology

1( * E-mail: mori.h.at@m.titech.ac.jp )Background
Sessile serrated adenoma/polyp (SSAP) is one of the precancerous lesions of sporadic colorectal cancer. The
carcinogenic pathway from SSAP to colorectal cancer is called "serrated pathway", which is peculiar to SSAP.
PurposePrevious studies have suggested a relationship between the development of colorectal cancer and intestinalbacteria. However, the relationship between SSAP and intestinal bacteria has not yet been fully clarified. Therefore,
as a first step to clarify the relationship, this study aims to investigate the differences in intestinal bacteria and their
metabolites between SSAP patients and healthy subjects, and to elucidate the involvement of intestinal bacteria in SSAP
pathogenesis.
Materials and Methods
Stool samples from 81 healthy subjects and 33 SSAP patients were used in this study. The samples were obtained at
the National Cancer Center. DNA was extracted from the samples, and metagenome sequences (78 healthy subjects and
33 SSAP patients) and metabolome data (81 healthy subjects and 32 SSAP patients) were obtained. Metabolome data
were acquired in collaboration with Keio University.
After quality control was performed on the obtained metagenome sequences, taxon assignment and functional
profiling were performed.
The results of this study were compared between two groups, SSAP and healthy subjects, to identify the intestinalbacteria and metabolites that were significantly more abundant in each group.
Results
Metagenome analysis revealed that Bacteria X was abundant in SSAP patients. This bacterial species has also beenreported to be significantly increased in patients with early colorectal cancer. Metabolome analysis also revealed thatmetabolite Y and Z were increased in SSAP patients.

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P10 臨床インフォマティクス Clinical Informatics

P10-08
Elucidation of Mortality Triggers Using Temporal Predictive Models
Tatsuki YAMAMOTO*, 1, Yuzuha TSUJI2, Minoru SAKURAGI2, Yuji OKAMOTO2,
Mayumi KAMADA2, Yasushi OKUNO2
1

Faculuty of Medicine Human Health Sciences, Kyoto University, 2Graduate School of Medicine,
Kyoto University( * E-mail: yamamoto.tatsuki.25d@st.kyoto-u.ac.jp )Background
Recent years have witnessed an active exploration into prognostic predictions and factor analysis of patients basedon medical big data, such as health check-up and clinical data. The temporal changes in patients have been predictedusing time-series clinical data. However, the triggers leading to a patient’s death remain ambiguous. This study aims to
elucidate these triggers by analyzing the temporal changes of factors believed to influence prognosis, providing insights
for improving patient treatment strategies.
DatasetElectronic Health Record (EHR) data of 9,614 cancer patients who were confirmed deceased between January 2006
and December 2019 at Kyoto University Hospital were utilized. The EHR data used for analysis spanned from one
year prior to the time of death. Out of the EHR, we selected 86 features that were tested on over half of the patientsfor our analysis. To ensure consistent time intervals within the EHR data, a moving average with a 5-day window was
employed to create uniform time-series data.
MethodUsing the uniformly spaced temporal EHR data, a predictive model for patient mortality at each time point wasconstructed and its accuracy was verified through cross-validation. The model employed Light GBM. For patients
whose deaths were accurately predicted, the SHAP (shapley additive explanation) method was used to quantify thecontribution of each test item. Furthermore, by aligning the SHAP values of the mortality prediction model at each time
point, the temporal changes in contribution were derived. Based on the temporal shifts in the contribution of each test
value, potential triggers leading to patient death were investigated.
Results & Discussion
Cross-validation results indicated that the model’s generalization performance (AUROC) ranged between 0.65 and
0.82, confirming the model’s validity. Temporal changes in the contribution of each factor using SHAP revealed such as:(1) The contribution of C-reactive protein (CRP) remained high over the long term but increased dramatically justbefore death.
(2) The contribution of blood urea nitrogen (BUN) was not observed over the long term but saw a sharp increase
before death.
Based on these findings, both CRP and BUN can be considered significant factors contributing to mortality triggers.
The insights gained from this study are expected to shed light on the pathophysiological mechanisms in terminal cancer
patients and contribute to enhancing treatment strategies.

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DeepLearning専用ワークステーション

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「G-Works4.0」をインストール

Weights＆Biasesを標準搭載「G-Works」はDeep Learning に使用される主要なフレームワークを、各世代の NVIDIA GPU に最適化してビルドしたソフトウェアの集合体です。
これらは Docker イメージと違い、コンテナ化されていない OS ネイティブなバイナリファイル群として構成されています。

Weights & Biasesは機械学習の実験条件、途中経過や結果のデータを保存、可視化、レポート化するための Python ツールで、開発者による記録作業の手間を大 幅 に 軽 減し、開 発 工 程 の 効 率 化 をもたらします。G - W o r k s 4 . 0 に 標 準搭載します。

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