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ポスター発表リスト Poster Presentation List
発表時間(A) October 29 (Tue) 16:00-17:00 & October 30 (Wed) 17:00-18:00(B) October 29 (Tue) 17:00-18:00 & October 30 (Wed) 16:00-17:00
発表番号

⽒名

No.
Name
01 計算化学（分⼦計算）

所属

演題名

発表時間

Affiliation

Title of Presentation

Poster Time

Hepatitis C Virus Drug Resistance Mechanism:(A)

Computational Chemistry (Molecular Modeling)
P01-01✫YAXUAN WANG

Kagoshima University

Docking and Molecular Dynamics Study of
P01-02✫

P01-03

Yoshiki Yugami

Chisato Kanai

the department of science, Osaka

NS5A-Drug Complex
Generation of Structural Ensemble of Linear

Prefecture University

Diubiquitin Based on PCS Experiments

INTAGE Healthcare Inc.

Investigation of the utility of steered MD in the

(B)

(A)

prediction of binding affinity: a case study of
P01-04✫

Ao Kikuchi

Yokohama City University

HSP90
Cross-reactivity of T cell receptors against HCoV

(B)

through three-dimensional structure prediction
P01-05✫

Shoya Hamaue

Daiichi Sankyo Co., Ltd.

Prediction Method for Protein-Bound(A)

Conformation of Macrocycles
P01-06✫

P01-07✫

P01-08✫

P01-09Department of Applied Physics,

Enhanced Prediction of Antigen-Antibody

Graduate School of Engineering,Complex Structures through Aggressive

Nagoya University
Takashi Yoshidome Department of Applied Physics,

Structural Refinement by AlphaFold2
Deep-Learning model for Predicting the

Seiya Tanaka

Hirotada Kaneshiro

Shinji Iida

Graduate School of Engineering,

Replacement of Water Molecule upon Ligand

Tohoku University
Department of Systems

Binding
Comprehensive docking simulations using

Informatics, Graduate School ofAlphaFold2-based human olfactory rec

Systems Informatics, Kobe

eptors for odor prediction

University
Kitasato University

Generative Model for Protein Structural

(B)

(A)

(B)

(A)

Ensembles Enhanced by Molecular Dynamics
P01-10✫

P01-11✫

Toshiaki UEDA

Takunori Yasuda

Graduate School of Science andSimulation Data
Epicatechin n -mers (n ≥ 5) adopt more compact

Technology, Shinshu University

conformations than catechin n -mers

Institute of Life and Environmental The Computational Study on the Secondary
Sciences, University of Tsukuba

(B)

(A)

Structure Formation of Nascent Peptides Inside
the Ribosome Tunnel with Biomolecular
Environments Mimicking Model

P01-12✫

Kei TerakuraInstitute of Science Tokyo

Kinetic Analysis of Membrane Permeation(B)

Process of Cyclic Peptides Using Markov State
Models with Molecular Dynamics Simulations
P01-13

Osamu Ichihara

Schrödinger KK

Predicting Lysine Reactivity: Insights from

(A)

Constant-pH MD Simulations and Experimental
P01-14✫

Yuki Yasumitsu

Institute of Science Tokyo

Correlation
Protein Tertiary Structure Prediction with Fine-(B)

tuned AlphaFold2 for Ligand Virtual Screening
P01-15✫

Yoshitaka Tadokoro KINDAI University

Dynamic Relationship Between the Entrance to(A)

the Ligand Binding Site and the Dimer Interface
P01-16✫

Ekishin Yanagi

The University of Tokyo

in MAO-B
Conformational study of macrocyclic peptides insolvent by MD simulations to improve theirmembrane permeability(B)P01-17✫

P01-18✫

Yudai Kobayashi

Hiroaki Oheda

Department of Biomedical Data

High-precision and Efficient Prediction ofIntelligence, Graduate School ofIntermolecular Interaction Energies Using Deep

Medicine, Kyoto University

Learning on Quantum Chemical Calculation Data

Yokohama City Univ.

Mechanisms of Type-51 R-body conformational

(A)

(B)

changes revealed by in silico methods
P01-19✫Kenji Yamagishi

Nihon University

Molecular simulation analysis for nucleic acids(A)

P01-20✫Samuel Toba

OpenEye, Cadence Molecular

Induced-Fit Posing (IFP): A new pose prediction(B)

Sciences

tool for hit to lead stage of drug discovery

P01-21✫

Keisuke Uchikawa

Institute of Science Tokyo

Generation of a suitable structure for SBDD by(A)

AlphaFold2 via Genetic Algorithm Parameter
Search02 計算化学（分⼦認識）
Computational Chemistry (Molecular Recognition)
P02-01✫

Hajime Sugiyama

Mitsubishi Chemical Corporation

Investigation of the Allosteric Binding Sites of(A)

ERK2 by Metadynamics Simulation
P02-02✫

Kairi Furui

Institute of Science Tokyo

PairMap: An Intermediate Insertion Approach to(B)

Improve Accuracy in Relative Free Energy
Perturbation Calculations of Distant Compound
P02-03✫Siyun Wang

Graduate school, Osaka university

Transformations
Fragment Molecular Orbital Calculations for(A)

Zinc-Containing smHDAC8
P02-04✫

Suzu Itami

Kindai University

Interaction Analysis between pHLA and TCR(B)using MD Simulation and Fragment MolecularP02-05✫

Junya Yamagishi

Preferred Networks

Orbital Calculation
NNP-based Force Field Optimization to Improve(A)

RBFEP Performance
P02-06✫WENXING HUTokyo Institute of Technology

SpatialPPI 2.0: Enhancing Protein-Protein(B)

Interaction Prediction Through Distance Matrix
Analysis Using Link Regression in Graph
P02-07✫

Xingmei Ouyang

RIKEN

Attention Networks
Development of RIKEN Natural Products(A)

Depository Database
P02-08✫Tomohiro Sato

RIKEN

Machine learning based prediction of quantum(B)mechanical interaction energy between aminoacid residues using fragment molecular orbital
method
P02-09✫

Yurika Ikegami

University of Tsukuba Graduate

Computational assessment of the binding mode

Schoolof Verteporfin, an inhibitor targeting the YAP-

Toray Industries, Inc.

Case studies of deep learning-based molecular(A)

TEAD protein-protein interaction
P02-10✫Kazuya Osumi(B)

docking program in medicinal chemistryP02-11✫

Kodai Igarashi

Institute of Science Tokyo

Binding Affinity Prediction Through(A)

Unsupervised Learning of Protein-Ligand MD
Trajectories
P02-12✫

Hirofumi Watanabe

WithMetis Co., Ltd.

Preprocessing of FMO calculations and practical(B)

visualization of interaction energies for drug
designP02-13✫

Ryosuke Kita

Kyushu university

Prediction of quantum mechanical interactions
between the ligand and each amino acid residue
in protein-ligand complexes(A)03 データサイエンス
Data Science
P03-01✫

Daiki Sakai

Yamada Lab, Tokyo Institute ofDisease Prediction from Small Sample Gut(A)

Technology School of Life Science Microbiome Data
P03-02✫

Zi Wangand Technology
IPR, Osaka Univ.

Biological Age Prediction Using a Deep NeuralP03-03✫

Kouki Maebara

Nagoya City University

A deep learning model for predicting chemical-(B)

Network Based on Steroid Metabolic Pathways(A)

induced rat hepatocellular necrosis using
P03-04✫

Shogo Nakamura

Tokyo Institute of Technology

transcriptome data.
Reaction-Aware Molecular Optimization Using(B)

Conditional Transformer and Reinforcement
P03-05✫

Seiji Matsuoka

RIKEN

Learning
Computational determination of SMARTS(A)

molecular query containment relationships
P03-06✫

Takayuki Serizawa

Daiichi Sankyo Co., Ltd.

Development of New data analysis platform for(B)

medicinal chemist in Daiichi Sankyo
P03-07✫

Kei Yoshida

Hitachi, Ltd.

Data augmentation method of chimeric protein(A)

sequences for fine-tuning of protein language
P03-08✫

Yuya Koide

Yokohama National University

models
Predicting Chemical Roles Using Natural(B)

Language Processing on Database Descriptions
P03-09✫Supakorn

Osaka University

Pongpakdee
P03-10✫Hiroyuki Hakamata

Large-scale single nucleus RNA-seq analysis of(A)

Lewy body diseases subtypes
Daiichi Sankyo Co., Ltd.

SAR analysis and visualization utilizing a(B)fragment-based approach: Application to a
public data analysis of Targeted Protein
DegraderP03-11✫

Jianan Li

Institute of Science Tokyo

CycPeptMP: Development of Membrane(A)

Permeability Prediction Model of Cyclic Peptides
with Multi-Level Molecular Features and Data
Augmentation
P03-12✫

Keisuke Hirota

Institute of Science Tokyo

Deep learning-based enzyme screening to(B)identify orphan enzyme genes
P03-13

SAID BYADI

HOKKAIDO UNIVERISTY

Data-driven design of visible-light(A)

photoswitches using structural features
P03-14

Kikuko Kamisaka

RIKEN

Recent Developments of FMODB in 2024: Efforts(B)

Towards Utilization of FMO data
P03-15✫

Miwa Sato

Hitachi, Ltd

Development of the data management system to(A)acquire the strategic data for AI
P03-16
P03-17✫

Yi-An Chen
Koh Sakano

National Institutes of Biomedical

Enhancing Biological Insights with TargetMine:

Innovation, Health and Nutrition

Integration of Genomic Region Annotations

Institute of Science Tokyo

Natural product-like compound generation with

(B)
(A)

chemical language models
P03-18✫Tsubasa Nagae

Yokohama City University

Development of an Integrated Machine Learning(B)

Model Incorporating Compound-Protein
Information for Design and Prediction of SmallMolecule Modulators of PPIs
P03-19✫Toru Nishino

FUJIFILM Corporation

REALM: Region-Empowered Antibody Language(A)

Model for Antibody Property Prediction
P03-20✫

Daiki Koge

Niigata University

Generalized Molecular Representation for Drug

(B)

Discovery via Molecular Graph Latent Diffusion
P03-21✫

Toshiyuki Ohfusa

Astellas Pharma Inc.

Autoencoder
Data utilization and DX talent development on
in-house KNIME platform(A)P03-22✫

Takanobu Araki

Astellas Pharma Inc.

P03-23✫

Hirokazu Nishimura Mitsubishi Tanabe Pharma

Astellas's Digital Transformation for Small(B)

Molecule Drug Discovery Research

P03-24✫

Nagisa Matsuo

Unraveling Microbiome Complexity: A

Corporation

Knowledge Graph Approach to Functional

Kanazawa University

Interpretation in Drug Discovery
Age prediction from DNA methylation data using

(A)

(B)

machine learning
P03-25✫Hiromitsu

The Noguchi InstituteShimoyama

Exchange System for Glycan Textual Notations(A)

Development to Integrate Various Glycan
Databases and Improve Search Accuracy

P03-26

Noriaki Okimoto

RIKEN

Drug discovery study integrating compound(B)generative AI and molecular docking
P03-27✫

Masato Sakagami

Kanazawa University

Spike separation of high-gamma power in ECoG

(A)

using peak detection
P03-28✫Masafumi Saito

Kanazawa University

Estimation of transmission routes of the COVID-(B)

19 BA.1.1.2 variant using McAN and 3D graphP03-29✫Shuto Hayashi

Institute of Science Tokyovisualization
A framework for enhanced de novo protein(A)

design using deep learning and bayesianP03-30✫

Yusuke SAKAI

RIKEN

optimization
Directional Graph Modelling for Solution Design(B)and Experiment Automation
04 量⼦構造⽣命科学
Quantum-Structural Life Sscience
P04-01✫

Ruri Mihata

Osaka University

Analysis of Kinase Binding Specificity of(A)

Staurosporine using the Fragment Molecular
P04-02✫Shuhei Miyakawa

Osaka University

Orbital Method
Dynamical Interaction Energy Analysis of(B)

Elastase in Each Reaction State: Insights from
Molecular Dynamics and Fragment Molecular
P04-03Jun Koseki

National Institute of Advanced

Orbital Calculations
Development of the Cryptic Site searching(A)

Industrial Science and Technology method with Mixed-solvent molecular dynamics
and Topological data analyses methods
P04-04✫

Katsuki Sato

Department of Chemistry, Tokyo

Analysis of HS-AFM images of proteins

University of Science

combining MD simulation and machine learning

Mitsubishi Tanabe Pharma

Multi-Task Deep Learning using Graph

Corporation

Convolutional Networks for Predicting the(B)

05 ADME・毒性
ADME/toxicity
P05-01✫

Harutoshi Kato(A)

Unbound Fraction in Human, Mouse, and Rat
P05-02✫

Yuki Doi

Mitsubishi Tanabe Pharma

PlasmaEnhancing the Reliability of Machine Learning

Corporation

Predictions through Quantitative Evaluation of(B)

the Applicability Domain: A Case Study of MultiTask Prediction Model of Unbound Fraction in
P05-03Masataka Kuroda

National Institutes of Biomedical

Human, Mouse, and Rat Plasma
Development of tools to enhance the extracting

Innovation, Health and Nutrition

process of ADME activity information from the(A)

Common Technical Document (CTD)
P05-04✫

P05-05✫ELPRI EKA

Institute for Protein Research,

Improving the performance of prediction models

PERMADI

Osaka University, Japanfor small datasets of cytochrome P450 inhibition

Sumie Tajima

HULINKS Inc.

with deep learning
Addressing Common Metabolism Problems in

(B)

(A)

Drug Discovery with in Silico Methods
P05-06✫

Ryoko Terada

Institute for protein research of

In silico prediction of total clearance, volume of

Osaka University

distribution, and half-life with deep learning(B)P05-07✫

Yuki Umemori

Axcekead Tokyo West Partners

Unbound Fraction Optimized Method for(A)

Predicting Human Pharmacokinetic Clearance:
Advanced Allometric Scaling Method andMachine Learning Approach
06 バイオインフォマティックス
Bioinformatics
P06-01✫

Jiaxin Li

The University of Tokyo

Cell State Analysis of Immune Cells in the Tumor(A)

Microenvironment with Deep Learning
P06-02✫JUNSOO SONG

Institute for Protein Research,

A Novel Endometrial Cancer Patient

Osaka University

Stratification Considering ARID1A Protein(B)

Expression and Function with Effective Use of
P06-03✫

Xingran Wang

Institute for Protein Research,Multi-omics Data
Single-Cell Transcriptome Analysis Reveals

Osaka University

Roles of GABA Receptors in the Connectivity of(A)

Dorsal-Ventral Motor Neurons in C. elegans
P06-04✫

P06-05✫

Yulong Gou

Chaowen Ou

Osaka University, Insitute for

Impact of Intramolecular Hydrogen Bonds on

Protein Research

Permeability Glycoprotein MediatedTokyo Institute of Technology

Transportation
Improved Method of Predicting Protein Allosteric

(B)

(A)

Site Based on Atomistic Bond-to-bondP06-06✫

Yuki Kobayashi

Kyoto University

Interaction by Using GNN
Development of RNA velocity method using(B)

numerical integration of ordinary differential
P06-07✫

Akira Shinohara

Tokyo Institute of Technology

equationsCompound Retrosynthesis Analysis Using(A)

Consensus Estimate
P06-08✫

Kohei Hoashi

Tokyo Institute of Technology

Development of docking simulation with high-(B)

speed graph neural network scoring function
P06-09Mao Tanabe

National Institutes of Biomedical

Investigation of the trends and the potential in

Innovation, Health and Nutrition

drug development for rare and intractable(A)

diseases based on the KEGG NETWORK
P06-10✫Ryuhi Sato

Tokyo Institue of techno

Prediction of medium components for bacteria

P06-11✫

Koki Hattori

Chiba University

Elucidation of Stabilization Mechanisms of(B)using deep Learning(A)

Intrabodies Based on Statistical
Thermodynamics
07 創薬応⽤
Drug Discovery Application
P07-01✫

Toshiaki Watanabe

DAIICHI SANKYO CO., LTD.

Development of Pre-Fragment-Based MMP(A)

AnalysisP07-02✫

Yasunobu

Osaka University

Yamashita
P07-03

Kiyoshi Hasegawa

Discovery of a new histone deacetylase 8(B)inhibitor using machine learning-aided drug
TECHNOPRO R&D company

screening
Open Source Program Github and Its Application(A)

in Drug Discovery
P07-04✫

Hiroto Terada

Graduate School of Science, Osaka Development of accurate in silico screening
Metropolitan University(B)

protocol based on protein structural fluctuationand drug binding mode

P07-05✫Masatake Sugita

Institute of Science Tokyo

Development of Prediction Models for(A)

Membrane Permeability of Cyclic Peptides using
3D Descriptors obtained from Molecular
Dynamics Simulations and 2D Descriptors
P07-06✫

Yuki Murakami

Yokohama City University

De novo PROTAC linker design to enhance cell
membrane permeability based on a data-driven
method(B)

P07-07

Yunoshin Tamura

Preferred Networks

Scaling up Binding Free Energy Calculations:(A)

Integrating Free Energy Perturbation (FEP) andActive Learning to Prioritize Compound Designs
P07-08✫

Koichi Oki

Nagoya University

A Dirichlet diffusion model for generation of(B)

high-quality antimicrobial peptide sequences
P07-09✫Daiki Ishimoto

Laboratory of Chemistry andDevelopment of a Massive Fluorogenic Probe

Biology, Graduate School of

Library Based on Bayesian Optimization toward

Pharmaceutical Sciences, The

the Discovery of Novel Biomarker Enzymes(A)

University of Tokyo
P07-10✫

P07-11✫

P07-12✫

Ziwei Zhou

Naoko Konami

Mio Yokoyama

Institute for Protein Research,

Virtual validation and the efficient learning

Osaka University

methods exploration in federated learning (FL)

Graduate School and School offor drug development research
Structure and Interaction Analysis of Nucleic

Pharmaceutical Sciences, Osaka

Acid Encapsulated ssPalm Lipid Nanoparticles

University
Kumamoto University

by Multiscale Simulation.
Natural-Product Screening Toward Discovery of

(B)

(A)

(B)

Anti-Aging Glutaminase-1 Inhibitors. An
Electronic-Structure Informatics Study
P07-13✫Masami Sako

Tokyo Institute of Technology

DiffInt: Integrating Explicit Hydrogen Bond(A)

Modeling into Diffusion Models for StructureP07-14✫

Keisuke Yanagisawa Tokyo Institute of Technology

Based Drug Design
QUBO Problem Formulation of Fragment-Based(B)

Protein‒Compound Flexible Docking
P07-15✫

Kaho Akaki

Institute of Science Tokyo

Acquisition of Bias Information for Protein-(A)

Ligand Docking by Mixed-Solvent Molecular
P07-16✫

Shimizu Masayoshi

Institute of Science Tokyo

Dynamics
Development of a compound pre-screening(B)method based on docking of fragments
P07-17✫

P07-18✫

Yuma Iwashita

Taiyo Toita

Laboratory of Medical Molecular

Report on Participation in the Tox24 Challenge:

Analysis, Meiji Pharmaceutical

Construction of a High-Accuracy QSAR

University

Predictive Model for Transthyretin Activity

Yokohama City University

Lead generation of a V-ATPase inhibitor using

(A)

(B)

molecular generative AI
P07-19✫

Yifan Hu

Biortus Biosciences Co. Ltd

Exploring the Power of Structural Biology on(A)Degrader Discovery
P07-20Kentaro Kawai

Setsunan University

Constructing a machine learning model for(B)

discriminating Urotensin-II receptor inhibitors
P07-21✫

Apakorn Kengkanna Institute of Science Tokyoand its application
Reaction-conditioned variational autoencoder(A)

model for catalyst generation and catalytic
P07-22✫

KOSUKE

Daiichi Sankyo Co., Ltd.

MINAGAWAP07-23✫

Sora Suzuki

performance prediction
Drug discovery research utilizing BROOD: A(B)

Fragment Replacement and Molecular Design
toolInternational Graduate Program for A small molecule inhibitor that binds to the(A)

Agricultural and Biological Science unstable state of its target kinase DYRK1A

P07-24

Yoshirou Kimura

Selection

demonstrates slowly dissociation from the

MOLSIS Inc.

complex
Correlation Analysis of Excipient Modulated(B)

Viscosity of Monoclonal Antibody and MolecularSurface Patch Properties
P07-25✫

Takuya Tsutaoka

FUJIFILM Corporation

Predicting Antibody Stability pH Values from(A)

Amino Acid Sequences: Leveraging Protein
Language Models for Formulation Optimization
P07-26

Okimasa Okada

Mitsubishi Tanabe Pharma

Development of a Platform for Crystal Structure

Corporation

Prediction of Drug Molecules(B)P07-27✫

Takashi Ikegami

MOLSIS Inc.

Automated molecular modeling and property

P07-28✫

Tomoki Yonezawa

Keio University

Validation of the reproducibility of hit-to-

P07-29✫Genki Kudo

University of Tsukuba

Automated Hit-to-Lead Optimization Using the(A)assessment for ADCs
(B)

candidate using ChemTS(A)

SINCHO Protocol and ChemTS
P07-30✫

Yuka Matsumoto

Fujifilm Corporation

Application of Amino-Acid Mapping: Activity(B)

Prediction for Drug Discovery
P07-31✫Jinzhe Zhang

Preferred Networks Inc

Efficient Single Step Synthesizable Molecular(A)

Design using Wasserstein Autoencoder
P07-32✫MARTIN -Hokkaido University

Quantitative Assessment of Protein−Ligand(B)

Activity Prediction from 3D Docking Poses for
P07-33✫

Tomoya Saito

Institute of Science Tokyo

Urate Transporter 1
Development of an efficient compound 3D(A)

conformer search system based on relative
P07-34✫

Koshiro Aoki

Institute of Science Tokyo

position of fragments
Molecular Properties Prediction by Contrastive(B)

Learning Using Graph Neural Network
08 臨床インフォマティクス
Clinical Application
P08-01✫

Hiroki Adachi

Chugai Pharmaceutical Co., Ltd.

Predicting clinical laboratory test result related(A)

to urine tests in patients with type 2 diabetes
mellitus with renal complications using clinicalP08-02✫

Shoki Hoshikawa

Faculty of Pharmaceutical

trial data
Machine learning models for predicting cross-

Sciences, Setsunan University

reactivity of beta-lactam antibiotic allergy

Mie University

Modular photostable fluorescent DNA blocks for(B)

09 分⼦ロボティクス
Molecular Robotics
P09-01

Ryota Sugie(A)

tracking collective movements of motor proteinsP09-02✫

Ryosuke Iinuma

JSR Life Sciences CorporationSize-Selective Capturing of Exosomes Using(B)

DNA Tripods
P09-03✫
P09-04✫

Chung Wing Chan
Hisashi Tadakuma

Graduate School of Science, Kyoto Anisotropic Swarming of DNA Modified
University

Microtubules Under UV Light

ShanghaiTech University

De novo protein design of suitable binders for

(A)
(B)

DNA origami-based devices
P09-05✫

Rinka Aoki

Graduate School of Engineering,

Over the Membrane: Study of Nucleic Acid

Tohoku University

Sequence Transfer Using Cholesterol-Modified

P09-06

Yuri Kobayashi

Mie university

DNA
Multi-reconfigurable DNA nanolattice guided by

P09-07

Ryoya Sakaguchi

Mie University

Construction of dual-responsive circular DNA

(A)

(B)

a combination of external stimuli(A)

origami nanoactuator
P09-08✫

Koichi Tanimoto

Kansai University

Evaluation of anticancer activity and(B)

investigation of cellular uptake mechanism of
drug-loaded DNA Origami dendrimers forapplication to drug delivery system
10 健康科学
Health Sciences
P10-01✫

Yunosuke Matsuda

Bathclin Corporation

Decision-making model to enhance subjective
well-being through individualized lifestylemodifications based on counterfactual
explanation(A)11 その他
Others
P11-01✫

Kosuke Takeuchi

DAIICHI SANKYO CO., LTD.

Evaluation of Data-Driven Drug Discovery(A)

Approaches: Utilizing Redmine Ticket
Management System for Tracking and Analyzing
P11-02✫

Chihiro Higuchi

National Institutes of Biomedical

Activities
Japanese Food Ontology Development(B)

Development of a model to predict the severity(B)

Innovation, Health and Nutrition(NIBIOHN)
P11-03✫Canceled

P11-04✫

Kiyohiro Toyofuku

Kyushu Universityof systemic lupus erythematosus using LIFE
P11-05✫

Kaito Sasaki

Kyushu University

Study data
Exploring unexpected factors related to(A)glaucoma onset in diabetes patients using LIFE
P11-06✫Hiromu Matsumoto

Kyushu Unviersity

Study data
Survival Analysis of Chronic Kidney Disease(B)

Using Multi-Regional Data from the LIFE Study
P11-07

Kensei Orita

Kyushu University

Data-driven search for diseases whose patient(A)numbers are associated with weather variability
P11-08✫

Ryoji Abe

Tokyo Institute of Technology

using LIFE study data
Analysis of interactions between fatty acid(B)

membranes with pH-dependent phasestructures and nucleic acid monomers using
P11-09✫Yuhui Zhang

Tokyo Institute of Technology

Molecular Dynamics simulation
A Virtual Reality Platform for Molecular(A)

Dynamics Based on Unity Engine
P11-10Kohtaro Yuta

In Silico Data,Ltd.

From Computer-Assisted Routine/RepeatedʻAutomationʼ to AI-Assisted Future-Oriented
ʻAutonomous (Intelligent/Creative)ʼ: Division
and Impact of ʻAutomationʼ and ʻAutonomousʼ in
Research Contents(B)