AHeDD2019/IPAB2019 Joint Symposium
Trends in Artificial Intelligence and Molecular Simulation for Accelerating e-Drug Discovery
November 27 (Wed.) - 29 (Fri.), 2019 ／ Tonomachi King Skyfront Area, Kawasaki, Japan

Contents
General Information

2

Opening Address

3

Program at a Glance

4

Time Table

5

Access9

Floor Map

12

Information for Participants

14

Committee

16

AHeDD History

17

IPAB History

18

Plenary Talk 1

19

Plenary Talk 2

23

Session 1

27

Session 2

33

Session 3

39

Session 4

45

Flash Talk Session

51

AMED BINDS Project Special Sessions

67

Sponsors

75

General Information

AHeDD2019/IPAB2019 Joint Symposium

Trends in Artificial Intelligence and Molecular Simulation for Accelerating e-Drug Discovery

PeriodNovember 27 (Wed.) - 29 (Fri.), 2019

Venue
Tonomachi King Skyfront Area, Kawasaki, Japan・The Kawasaki Life Science & Enviromental Research Center (LiSE)
3-25-13 Tonomachi, Kawasaki-ku, Kawasaki City, Kanagawa Japan 210-0821
・PeptiDream Inc.
3-25-23 Tonomachi, Kawasaki-ku, Kawasaki City, Kanagawa Japan 210-0821

Symposium Chair
Yutaka Akiyama(Tokyo Institute of Technology)

Organization
Main organizer
Certified NPO Initiative for Parallel Bioinformatics (IPAB)
Co-organizers
Middle-Molecule IT-based Drug Discovery Laboratory (MIDL), Tokyo Institute of Technology
Kawasaki City
Kawasaki Institute of Industrial Promotion
Innovation Initiative for Middle-Molecule Drug Discovery (IMD²)
Cooperation
Tokyo Institute of Technology
Endorsement
Research Organization for Information Science and Technology (RIST)
Information Processing Society of Japan
Biophysical Society of Japan
Chem-Bio Informatics Society (CBI)Japanese Society for Bioinformatics (JSBi)
The Kanagawa Shimbun
The Nikkan Kogyo Shimbun, Ltd.
The Science News Ltd.

2

Opening Address

AHeDD2019/IPAB 2019 Joint Symposium Chair
Yutaka Akiyama (Tokyo Institute of Technology)
It is our great pleasure to announce that AHeDD2019/IPAB2019 Joint
Symposium will be held at Tonomachi King Skyfront area (LiSE andPeptiDream Inc.), Kawasaki City, Japan, during November 27-29, 2019.
The Kawasaki INnovation Gateway (KING) Skyfront is the flagshipscience and technology innovation hub of Kawasaki City. It is a 40 hectare area located in frontof Tokyo International Airport, and was launched in 2013 as a base for research, industrial, andgovernmental sectors to work together to devise solutions to global issues in the life sciences andenvironment. We are really happy to welcome all of you to this new innovation area dedicated tonovel scientific research.
The AHeDD2019/IPAB2019 international symposium will be held jointly as 10th AHeDD
symposium and 18th IPAB symposium. In 2004, the scientists of Korea, China, and Japan working
in the fields of Computer-Aided Drug Design formally agreed with to establish a meeting called
Asia Hub for e-Drug Discovery (AHeDD). The 1st AHeDD meeting was held in 2005 at Jeju Islandof Korea. Then we continued to have meetings on 2006 (Seoul), 2007 (Shanghai), 2008 (Tokyo
jointly with IPAB2008), 2009 (Busan with KBSI-CBI), 2010 (Seoul), 2014 (Chengdu), 2017
(Melbourne with RACI), 2018 (KIST, Gangneung), and 2019 (King Skyfront, Kawasaki City). On
page 16 of this symposium booklet, you can see the brief history of AHeDD meeting series.
On the other hand, IPAB, Initiative for Parallel Bioinformatics was founded on 1999, as a non-profit
organization to accelerate high-performance computing for bioinformatics and drug discovery
in Japan. On page 17, you can see the brief history of IPAB symposium series. IPAB has alsoorganized open competitions for computational drug discovery on 2014, 2015, 2016, and 2017.
We are looking forward to seeing you in AHeDD2019/IPAB2019 Joint Symposium, at Tonomachi
King Skyfront area, Kawasaki City, Japan, and having fruitful discussions on trends in artificial
intelligence and molecular simulation for accelerating e-drug discovery.

AHeDD2019/IPAB2019 Joint Symposium Chair,
Yutaka Akiyama

3

Program at a Glance4Time Table
Wednesday, 27th November 201917:30-20:00

Location : King Skyfront Tokyu REI Hotel, 1st floor

17:30-20:00
18:00-20:00

Registration
Reception Presentations from AHeDD representatives

Thursday, 28th November 201910:00-13:00

Location : LiSE, Conference room, 1st floor

10:00-10:20

Opening address
Yutaka Akiyama
Shengyong Yang
Kyoung Tai No

10:20-12:00

Professor, Department of Computer Science, Tokyo Institute of Technology, Japan
Professor, Department of Medicinal Chemistry, Sichuan University, China
Professor, Department of Biotechnology, Yonsei University, Korea

Session 1: Chemoinformatics and ADMET prediction (Chairs: Liu Hong, and Kyong Tai No)

10:20-10:45

In silico ADMET prediction and optimization
Yun Tang
Professor, School of Pharmacy, East China University of Science and Technology, China

10:45-11:10

Syetems chemo-informatics and fragment based lead discovery
Gyoonhee Han Professor, Department of Biotechnology, Yonsei University, Korea

11:10-11:35

Prediction of membrane permeability and plasma protein binding of cyclic peptides
Yutaka Akiyama Professor, Department of Computer Science, Tokyo Institute of Technology, Japan

11:35-12:00

A proteochemometric approach for beta-lactam antibiotic response prediction in Staphylococcus aureus
Jae Hong Shin
Senior Scientist, Standigm Inc., Korea

12:00-13:00

Lunch

13:00-13:20

Move to PeptiDream (5 min. on foot)13:30-18:20

Location : PeptiDream, Auditorium, 1st floor

13:30-14:30

Plenary Talk 1 (Chair: Yutaka Akiyama)

Constrained peptides in drug discovery and development - various therapeutic applications offeredby PeptiDream
Keiichi Masuya Executive Vice President, PeptiDream Inc., Japan

14:30-14:50

Coffee Break

14:50-16:30

Session 2: Structures of Key Drug Targets(Chairs: Takatsugu Hirokawa, and Yun Tang)

14:50-15:15

CryoEM analysis of the GPCR neurotensin receptor 1-G protein complex for the future engineeringof biased allosteric modulator
Hideaki E. Kato Associate Professor, Komaba Institute for Science, The University of Tokyo, Japan

15:15-15:40

Discovery and development of novel CCR5 antagonists
Hong Liu
Professor, Shanghai Institute of Materia Medica, CAS, China

15:40-16:05

Molecular modeling study for identification of binding site of AIMP2-DX2 inhibitor
Yuno Lee
Senior Scientist, Korea Research Institute of Chemical Technology (KRICT), Korea

5

Time Table
16:05-16:30

Computer aided drug discovery with natural compounds
Kyoung Tai No
Professor, Department of Biotechnology, Yonsei University / Director, BMDRC, Korea

16:30-16:40

Break

16:40-18:20

Session 3: Artificial Intelligence for Drug Discovery(Chairs: Weiliang Zhu, and Gyoonhee Han)

16:40-17:05

Drug discovery with artificial intelligence: advances and challenges
Shengyong Yang Professor, Department of Medicinal Chemistry, Sichuan University, China

17:05-17:30

Multiple approaches to discover human DDX3 inhibitors for cancer therapeutics
Keun Woo Lee
Professor, Department of Biochemistry, Gyeongsang National University, Korea

17:30-17:55

Visualizations within chemogenomic active learning reveal how protein descriptors are used andunderlying truths about chemogenomic and single target QSAR model utility
J. B. Brown
Junior Associate Professor, Graduate School of Medicine, Kyoto University, Japan

17:55-18:20

Bioinformatics-Facilitated Therapeutic Target discovery
Feng Zhu
Professor, College of Pharmaceutical Sciences, Zhejiang University, China

18:20-18:35

Move to King Skyfront Tokyu REI Hotel (5 min. on foot)18:45-20:30

Location : King Skyfront Tokyu REI Hotel, Restaurant, 5th floor

18:45-20:30

BanquetFriday, 29th November 20198:30-17:45

Location : LiSE, Conference room, 1st floor

8:30- 9:00

Coffee and Donuts

9:00-10:30

Flash Talk Session: Presentations by Young Researchers in Asia-Pacific Regions(Chair: Masahito Ohue) (4 min + 1 min Q&A)

6

9:00-9:05

Systematic construction of the cosolvents sets for cosolvent MD (CMD) with the large-scale simulation
Keisuke Yanagisawa JSPS Research Fellow, Department of Biotechnology, The University of Tokyo, Japan

9:06-9:11

Investigation of protein-protein interactions and hot spot region between PD-1 and PD-L1
by fragment molecular orbital method
Hocheol Lim
Researcher, Bioinformatics & Molecular Design Research Center, Korea

9:12-9:17

Metagenome analysis implies bacterial community and gene category composition disorderin periodontal disease sites
Kazuki Izawa
Researcher, Department of Computer Science, Tokyo Institute of Technology, Japan

9:18-9:23

In silico drug discovery targeting Hippo pathway and YAP-TEAD protein protein interactions
for small molecules anti cancer agent
Jongwan Kim
Researcher, Bioinformatics & Molecular Design Research Center, Korea

Time Table
9:24-9:29

Discovery of leucyl-tRNA synthetase (LRS) PPi inhibitor for modulating mTORC1 pathway via
the chemical biology approach
Chulho Lee

9:30-9:35

Research Associate, Department of Biotechnology, Yonsei University, Korea

Novel computational approach for natural product (NP) research: development of natural compoundmolecular fingerprint (NC-MFP) for exploring new NP-based drugs
Myungwon Seo

9:36-9:41

Graduate Student, Department of Biotechnology, Yonsei University, Korea

Predicting membrane permeability for cyclic peptides
Yasushi Yoshikawa Specially Appointed Assistant Professor, Department of Computer Science, Tokyo Institute of Technology, Japan

9:42-9:47

Development of prediction model for onset of disease and risk factor analysis
Atsuyoshi Matsuda CEO, Logbii, Inc., Japan

9:48-9:53

GCMQA: a novel single-protein structure model quality assessment method using graph convolution
Rin Sato

9:54-9:59

Contest-based compound screening enables identification of chemically diverse inhibitors of target proteins
Shuntaro Chiba

10:00-10:05

Graduate Student, Department of Computer Science, Tokyo Institute of Technology, Japan

Researcher, MIH, RIKEN, Japan

Underestimated non-covalent interactions in protein data bankZhijian Xu

10:06-10:11

Associate Professor, Shanghai Institute of Materia Medica, CAS, China

Evaluation of a self-balanced force field for biomolecule simulations
Sungbo Hwang

10:12-10:17

Graduate Student, Department of Biotechnology, Yonsei University, Korea

Application of informatics approaches to compound library design for intractable target chemical spaces
Kazuyoshi Ikeda

10:18-10:23

Specially Appointed Associate Professor, Division of Physics for Life Functions, Keio University, Japan

Learning-to-rank for ligand-based virtual screening
Masahito Ohue

Assistant Professor, Department of Computer Science, Tokyo Institute of Technology, Japan

10:30-10:50

Coffee Break

10:50-12:30

Session 4: High-Performance Molecular Simulation

10:50-11:15

Development of the rapid QM/MM molecular dynamics techniques aimed at medium molecular drug discovery
Hiroaki Nishizawa

11:15-11:40

Professor, Shanghai Institute of Materia Medica, CAS, China

Next generation accelerated supercomputing: Cygnus system at University of Tsukuba
Taisuke Boku

12:05-12:30

Assistant Professor, Center for Computational Sciences, University of Tsukuba, Japan

New methods for highly efficient MD simulation
Weiliang Zhu

11:40-12:05

Director, Center for Computational Sciences, University of Tsukuba, Japan

In silico Drug Discovery using Molecular Modeling and Simulation
Takatsugu Hirokawa

12:30-13:30(Chairs: Keun Woo Lee, and Shengyong Yang)

Professor, University of Tsukuba / Team Leader, molprof, AIST, Japan

Lunch

7

Time Table
Friday, 29th November 2019
9:00-17:45

Location : LiSE, Conference room, 1st floor

13:30-17:30

AMED BINDS project special sessions

13:30-14:15

14:15-15:30

Plenary Talk 2(Chair: Takatsugu Hirokawa)
Perspectives of computational drug discovery: AMED-BINDS activities in Japan
Shigenori Tanaka
Professor, Department of Computational Science, Kobe University / AMED BINDS Project Officer, Japan

BINDS Achievements 1(Chair: Takatsugu Hirokawa)

14:15-14:40

In silico drug discovery based on structural informatics and FMO method
Teruki Honma
Group Leader, BDR, RIKEN, Japan

14:40-15:05

Molecular simulation studies on protein functions and improvement of the efficiency of cryo-EM data collection
by machine learning
Associate Professor, Interfaculty Initiative in Information Studies, The University of Tokyo, Japan
Tohru Terada

15:05-15:30

Structural modeling of the entire EhV-ATPase in multiple states using cryo-EM data and homology modeling
Yu Yamamori
Research Scientist, AIRC, AIST, Japan

15:30-15:50

Coffee Break

15:50-17:30

BINDS Achievements 2(Chair: Teruki Honma)

15:50-16:15

Finding a drug candidate regulating protein function at an allosteric site
Yuko Ito
Research Scientist, molprof, AIST, Japan

16:15-16:40

MD simulations and QM/MM analysis to gain insight into protein functions
Yoshitaka Moriwaki Assistant Professor, Department of Biotechnology, The University of Tokyo, Japan

16:40-17:05

Improving the virtual screening ability using machine learning
Masakazu Sekijima Unit Leader, ACDD / Associate Professor, IIR, Tokyo Institute of Technology, Japan

17:05-17:30

Combination of molecular dynamics simulations and small-angle X-ray scattering experiments
Mitsunori Ikeguchi
Professor, Graduate School of Medical Life Science, Yokohama City University, Japan

17:30-17:45

Closing Remarks

Saturday, 30th November 20198

9:00-10:30

Location : King Skyfront Tokyu REI Hotel, 1st floor

9:00-10:30

AHeDD Steering Committee Meeting

Access
From Haneda Airport9

Access
Venue
Tonomachi King Skyfront Area, Kawasaki, Japan・Kawasaki Life Science & Enviromental Research Center (LiSE)
3-25-13 Tonomachi, Kawasaki-ku, Kawasaki City, Kanagawa Japan 210-0821
・PeptiDream Inc.
3-25-23 Tonomachi, Kawasaki-ku, Kawasaki City, Kanagawa Japan 210-0821

10

Access
Direction From LiSE to PeptiDream Inc.（11/28）

FUJI
FILM

CIEA

A
Exit from the main entrance of LiSE and go straight.
Turn left at the end of the road.

B

Johnson & Johnson

Go straight and pass Johnson & Johnson on your left.
Keep going straight.

Peptidream Inc.

C
You will see Peptidream Inc. on your left.

11

Floor Map
Kawasaki Life Science & Environment research center (LiSE) Floor Map

InvitedSpeakers
Roomendinga inesSmokingAreautsideWC

Entrance

Staff Room

Coffeereak

Registration

Conferen e Room

Main Entrance

Restaurant＆
Cafe Area（MOTOYAEXPRESS Forte)

1st Floor

12Floor Map
PeptiDream Inc. Floor Map

Smoking
Area

Auditorium

WC

InvitedSpeakers
Room
Staff Room

1st Floor

Main
Entrance

13

Information for Participants
1. Registration● Registaration date
11/27 (Wed.) 17:30- 20:00(Kawasaki King Skyfront Tokyu REI Hotel, 1st Floor)● Information desk open hours
11/27 (Wed.) 17:30- 20:00(Kawasaki King Skyfront Tokyu REI Hotel, 1st Floor)

11/28 (Thu.) 10:00- 12:00(LiSE, Conference Room, 1st Floor)

11/29 (Fri.)

13:30- 18:20(Peptidream Inc., 1st Floor)

09:00- 17:30(LiSE, Conference Room, 1st Floor)● Advance registration
Open : 9/10 (Fri.), 2019
Close : 11/20 (Wed.) , 2019
Receive a name badge and a program booklet at the registration desk.
● On-site registration
Those who have not completed advance registration are required to register on-site at theregistration and information desk.
● Registration fee
Regular：35,000 JPY,

Student：15,000 JPY(including Welcome Reception, Banquet, Lunch, and not including accommodation)● Name badge
Please be sure to wear your name badge throughout the meeting.
Entry without the badge is NOT acceptable.
● Receipt
A receipt is attached to the name badge. If you need another receipt form, it will be issued in
exchange for the one attached to your name badge.

2. Services & Facilities● Baggage
The organizers can not keep any baggages and we are not responsible for lost or stolen valuables.
● LunchA Lunch box will be provided after Session1 (11/28), and Session 4 (11/29).
Please follow the instructions of the staff.

14

Information for Participants
3. Prohibited Items● Photography & recording
Photography and recording with camera, video, mobile phone and any device is NOT
allowed for the presentation materials.
Please note that official photo and video recording will be performed by the organizers.
● SmokingPlease smoke in a designated area.
● Mobile phone use
Please set your mobile phone on the silent mode or off, and make sure it will not makenoises during lectures/presentations.

4. Contact● During the meeting
Information Desk
11/27 (Wed.) 17:30- 20:00(at Kawasaki King Skyfront Tokyu REI Hotel, 1st Floor)

11/28 (Thu.) 10:00- 12:00(at LiSE, Conference Room, 1st Floor)

11/29 (Fri.)

13:30- 18:20(at PeptiDream Inc., 1st Floor)

09:00- 17:30(at LiSE, Conference Room, 1st Floor)● Before or after the meeting≪ Organization Secretariat ≫Certified NPO Initiative for Parallel Bioinformatics (IPAB)
IPAB Office, 2-2-15 Hamamatsucho, Minato-ku, Tokyo Japan 105-0013
Email: office@ipab.org≪ Management Secretariat ≫Ahead Biocomputing, Co. Ltd.
Kawasaki Frontier Bldg. 4F, 11-2 Ekimaehoncho, Kawasaki-ku, Kawasaki City, Kanagawa Japan 210-0007
Email: ahedd2019@ahead-biocomputing.co.jp

15Committee
AHeDD2019/IPAB2019 Symposium Chair
Yutaka Akiyama, Department of Computer Science, Tokyo Institute of Technology, Japan

AHeDD Board Members
Kyoung Tai No, Department of Biotechnology, Yonsei University, Republic of Korea
Keun Woo Lee, Department of Biochemistry, Gyeongsang National University, Republic of Korea
Shengyong Yang, Department of Medicinal Chemistry, Sichuan University, China
Yun Tang, School of Pharmacy, East China University of Science and Technology, China
Weiliang Zhu, Shanghai Institute of Materia Medica, China
Hualiang Jiang, Shanghai Institute of Materia Medica, China
Yutaka Akiyama, Department of Computer Science, Tokyo Institute of Technology, Japan
Takatsugu Hirokawa, University of Tsukuba/molprof AIST, Japan
Yasuteru Shigeta, University of Tsukuba, JapanDavid Winkler, Monash University, Australia
Jonathan Baell, Monash University, Australia
Vladimir Poroikov, Head of Department for Bioinformatics and Laboratory
for Structure-Function Based Drug Design, Russia
Dmitry I. Osolodkin, Head of Laboratory of Antivirals, Russia
Yu Zong Chen, National University of Singapore, Singapore

IPAB Board Members
Nobuyuki Tanaka(Early Reflections Co., Ltd., Vice President of IPAB)

Masahito Ohue

(Ahead Biocomputing, Co. Ltd. )Masakazu Sekijima (Tokyo Institute of Technology)
Takatsugu Hirokawa (National Institute of Advanced Industrial Science and Technology)

Local Organizing Committee Chair
Masahito Ohue, Tokyo Institute of Technology, Japan

Local Organizing Committee Members
Kazuki Izawa, Tokyo Institute of Technology, Japan
Fumio Kurayama, Tokyo Institute of Technology, Japan
Kanako Ozeki, Initiative for Parallel Bioinformatics, Japan
Naoki Sato, Tokyo Institute of Technology, Japan
Tomomi Tanaka, Tokyo Institute of Technology, Japan
Yasushi Yoshikawa, Tokyo Institute of Technology, Japan

16

AHeDD History
Asia Hub for e-Drug Discovery (AHeDD)

Date

Contents

Location

2002.7.12

Dr. Akiyama-Dr. No; Exchange e-DD Information

Seoul, Republic of Korea

2002.9.14

Dr. Akiyama, Attend BMDRC Symposium on“Protein Structure forDrug target”, discussion on research collaboration

Seoul, Republic of Korea

2003.9.17

Dr. Akiyama- Dr. No, Discussion on collaboration of e-DD Education

Seoul, Republic of Korea

2004.6.3

Dr. Jiang – Dr. No: Discussion on the foundation of e-DD
collaboration hub.

Seoul, Republic of Korea

2004.6.19-24

Dr. No, Visit DDDC, first draft on AHeDD, visit DD related Institutes

Shanghai, China

2004.8.24

Dr. Jiang Visit BMDRC, Attend Symposium, meeting with e-DD
Researchers

Seoul, Republic of Korea

2004.9.29-30

Dr. No; Visit CBRC, discuss on AHeDDTokyo, Japan

2004.11.24

Dr. Akiyama: Visit BMDRC, Seminar, meeting with Korean e-DD
Researchers

Seoul, Republic of Korea

2005.8.26

Sub session in 11th Asian Chemical Congress,“Cheminformatics
for Drug Discovery and Development”– AHeDD

Seoul, Republic of Korea

2005.8.27-29

AHeDD Jeju Workshop (1st)Jeju, Republic of Korea

2005.10.26-29

Korean Delegates attended CBRC2005 / ISCBB2005, Visit MEXTTokyo, Japan2006.5.22-24

AHeDD meeting (2nd)Seoul, Republic of Korea

2007.4.16-19

AHeDD 2007 Symposium (3rd)Shanghai, China

2008.2.2, 3.24

Virtual Lab. Preparing meeting

China /Republic of Korea

2008.10.16-17

AHeDD 2008 / IPAB 2008, Tokyo (4th)Tokyo, Japan2009.11.5-6

AHeDD 2009, Two sessions in CBI-KSBSB Joint Conference (5th)Busan, Republic of Korea

2010.12.18

AHeDD 2010, Yonsei University (6th)Seoul, Republic of Korea

2013.10.8-12

CMTPI-2013

Seoul, Republic of Korea

2014.11.11-12

AHeDD 2014 (7th)Chengdu, China

2017.7.26-27

AHeDD 2017 (8th)Melbourne, Australia

2018.9.26-29

AHeDD 2018 Symposium (9th)Gangneung,
Republic of Korea

2019.11.27-30

AHeDD 2019 / IPAB 2019 Joint Symposium (10th)

Kawasaki, Japan

17

IPAB History
Initiative for Parallel Bioinformatics (IPAB)
Initiative for Parallel Bioinformatics (IPAB) was founded in December 1999 and is a nonprofit organization. IPAB was certified as a "certified NPO organization Initiative for Parallel
Bioinformatics (IPAB)" on 7 November, 2003. IPAB is a group of individual researchers andprivate companies aiming at contributing to the research and industrialization of bioinformatics
and biomedical computing, by means of state-of-the-art information technologies, like asparallel processing, Grid technology, machine learning, artificial intelligence, etc.

18

Date

Title

Japanese Title

1st2000.12.1

Toward Bioinformatics in 21st Century

21 世紀のバイオインフォマティクスに向けて

2nd

2001.11.30

Bioinformatics and Business Models of Post-Sequence Era 21 世紀のバイオインフォマティクスとin 21st Century
ポストシーケンス時代のビジネスモデル

3rd

2002.11.29

Biogrid

バイオグリッドについて

4th

2003.11.28

Integration of Cellular Network Information

細胞ネットワーク情報の統合化

5th2004.12.3

IPAB 5th Anniversary

IPAB 創立５周年記念

6th2006.2.10

Exploring Potential of Bionanotechnology

バイオナノの可能性を探る

7th

2006.12.11

It is time for Bioinformatics!

今こそバイオインフォマティクス！

8th

2007.11.29

Next Generation Supercomputers and Bioinformatics

次世代スパコンとバイオインフォマティクス

9th

2008.10.16-17

AHeDD 2008 / IPAB 2008 Joint Symposium
Novel Acceleration Technologies for Drug Discovery,
Genome Analysis, and Clinical Informatics

IPAB2008 / AHeDD2008 合同シンポジウム
創薬・ゲノム・医療情報処理のアクセラレーション

10th

2009.11.27

IPAB 10th anniversary
next 10 years - Big data in medicine and biology -IPAB 創立１０周年記念
これからの１０年～医学・生物学の大量データ～

11th

2010.12.3

Advanced Biology Research opened by Supercomputing

スーパーコンピューティングが拓く先端的バイオ研究12th

2011.12.9

Massively parallel supercomputer and biocomputing

超並列スパコンとバイオ計算

13th

2012.12.7

Drug discovery, Clinical Application and Big Data

創薬・医療とビッグデータ

14th

2013.12.6

Exascale Computing and Drug Discovery Open Innovation

エクサスケール・コンピューティングと創薬オープンイノベーション

15th

2014.12.5

Infectious Diseases and Computer-Aided Drug Discovery

感染症とコンピュータ創薬

16th

2016.3.11

Biodata Analysis in IoT / Cloud era

IoT/ クラウド時代のバイオデータ解析

17th

2017.3.3

Machine Learning and Artificial Intelligence

機械学習と人工知能

18th

2019.11.27-29

AHeDD 2019 / IPAB 2019 Joint Symposium
Trends in Artificial Intelligence and Molecular Simulation
for Accelerating e-Drug Discovery

IT 創薬を加速する AI・シミュレーション技術の新潮流

Plenary Talk 1(Chair: Yutaka Akiyama)

19Constrained Peptides in Drug Discovery and Development
Various therapeutic applications offered by PeptiDream

Keiichi Masuya Ph.D.
Executive Vice President
PeptiDream Inc.

We have PDPS (Peptide Discovery Platform System) which is a revolutionary next-generation
hit finding platform for peptide drug discovery, for the discovery and development ofconstrained peptides, small molecules, and peptide-drug conjugate (PDC) therapeutics.
Constrained peptides represent highly valuable chemical matter for using in biologically target
validation, identifying hit itself, and also finding key pharmacophore for design of small
molecules.
During recent 3 to 4 years, we have steadily expanded the area of utilizing “peptide binders andhits” for various therapeutic applications and also different purposes.
We actually have more than 100 project portfolio which includes peptide therapeutics, small
molecule therapeutics, peptide-drug conjugate (PDC) therapeutics, such as BBB-penetrating
carrier peptides, carrier-peptides for RI therapeutics, and so on.
This presentation will summarize our approaches with several recent key topics.

20

Keiichi Masuya, Ph.D.
PeptiDream Inc.
Executive Vice President

Keiichi Masuya obtained Ph.D. in 1998 (Tokyo Institute of Technology), and Research
Fellowship of the Japan Society for the Promotion of Science for Young Scientists (1995-1998).
From 1998-2001, he worked at Mitsubishi Pharma.
He joined Novartis Pharma Japan in 2001, received an oncology President Award in 2004, thenmoved to Basel Headquarters of Novartis Pharma in 2005.
He received a VIVA award as a Novartis leading scientist in 2012. He managed several
development compounds in the field of oncology.
He joined PeptiDream Inc. in 2014, currently as Executive Vice President and Board Member,and also Executive Officer/Chief Science Advisor in PeptiStar Inc.

21

22

Plenary Talk 2(Chair: Takatsugu Hirokawa)

23

Perspectives of computational drug discovery: AMED-BINDS activities in Japan
Shigenori Tanaka*
Kobe University, Japan*tanaka2@kobe-u.ac.jp

The BINDS (Basis for Supporting Innovative Drug Discovery and Life Science Research)
program of AMED (Japan Agency for Medical Research and Development) has been runningsince April 2017. The objective of this program is to establish an innovative platform toexpedite the therapeutic applications of early-stage drug discovery and medical technology
advances by providing and sharing key technological infrastructure, including: synchrotron
facilities at SPring-8 and the Photon Factory, cyro-electron microscopes (EMs), chemicalcompound libraries, and next-generation DNA sequencers. Specialists in state-of-the art
technologies in the fields of structural biology, protein production, high-throughput screeningof chemical compounds, lead compound structural modifications and extensions, genome
analysis, and in-silico screening, will support and assist the life science and drug development
projects of researchers who submit their proposals to members of the BINDS program.
The in-silico analysis unit of BINDS contains nine research groups and its mission includes:(1) Supports on (a) predictions of 3D structure of protein, PPI (protein-protein interaction) andligand binding mode, (b) structural informatics for virtual screening, and (c) various
chemoinformatics techniques; (2) Supports for protein dynamics studies based on integrated
structural analyses by X-ray crystallography, NMR, SAX, XFEL and cryo-EM; (3) Promotion
and facilitation of developed tools and databases for the availability for researchers (incollaboration with the platform optimization unit of BINDS); (4) Improvement and expansionof tools and facilities for more efficient supports.
In this talk, in addition to recent activities of the in-silico analysis unit of BINDS, I will illustrate
my own research activities on computational drug discovery mainly on the basis of quantum
chemistry, molecular dynamics and machine learning. Through all these presentations, I would
like to show some perspectives on the future of in-silico drug discovery technology as an
essential part in multi-disciplinary innovative collaborations.

24Shigenori Tanaka, Ph.D.
Department of Computational Science
Graduate School of System Informatics
Kobe University
Professor

Shigenori Tanaka received his PhD in theoretical physics in 1986 from the University of Tokyo.
He worked as a research associate at the University of Tokyo, and later as a researcher at the
Toshiba Research and Development Center. Meanwhile, he worked at the California Instituteof Technology in 1995–1996 as a visiting researcher. Since 2004, he has been a professor at
Kobe University.
He was also a project leader for JST-CREST "Development of biomolecular calculation
software on the basis of the fragment molecular orbital method" in 2004-2010 and is currently
a program officer of in-silico analysis unit of AMED-BINDS. His primary research interests
are the development of first-principles computational methods for biomolecular systems andtheir applications for bottom-up modeling of biological phenomena.

25

26

Session 1
Chemoinformatics and ADMET prediction(Chairs: Liu Hong, and Kyong Tai No)

27

In Silico ADMET Prediction and Optimization
Yun Tang*
Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Scienceand Technology, 130 Meilong Road, Shanghai 200237, China*E-Mail: ytang234@ecust.edu.cn

Chemical ADMET properties play key roles in drug discovery and development, which
accounts for about 40% of drug failures. Therefore, it is urgent to evaluate chemical ADMET
properties as early as possible. However, experimental evaluation of ADMET properties iscostly and time-consuming, so it is reasonable and practicable to develop in silico methods topredict chemical ADMET properties by fully taking advantage of big data and artificial
intelligence technology.
In the past ten years, we have carried out a series of studies on ADMET prediction andoptimization. (1) By mining and collecting data from literature and open source databases
such as PubChem, we constructed ADMET related databases[1]; (2) built a large number of
ADMET prediction models based on machine learning methods; (3) developed an ADMET
optimization method, named ADMETopt, based on scaffold hopping methods[2]; (4) built a
web server named admetSAR (http://lmmd.ecust.edu.cn/admetsar1/)[1], recently upgraded toversion 2.0 (http://lmmd.ecust.edu.cn/admetsar2/)[3], which is widely used worldwide. Our
goal is not only to predict ADMET properties at the lead discovery stage, but also to optimize
ADMET properties during the lead optimization stage.
References[1] Cheng F, Li W, Zhou Y, et al. admetSAR: a comprehensive source and free tool forassessment of chemical ADMET properties. J. Chem. Inf. Model., 2012, 52(11): 30993105.
[2] Yang H, Sun L, Wang Z, et al. ADMETopt: a web server for ADMET optimization in
drug design via scaffold hopping. J. Chem. Inf. Model., 2018, 58(10): 2051-2056.
[3] Yang H, Lou C, Sun L, et al. admetSAR 2.0: web-service for prediction andoptimization of chemical ADMET properties. Bioinformatics, 2019, 35(6): 1067-1069.

28

Systems Chemo-informatics and Fragment based Lead Discovery
Chulho Lee,1,* Doona Song,1 Gyoonhee Han1,2,*
1

Department of Biotechnology, Yonsei University, Republic of Korea
Department of Pharmacy, Yonsei University, Republic of Korea

2*gyoonheet@yonsei.ac.kr.

The drug discovery research in the post-genomic has been based on the discovery of novel andvalidated drug targets. This research trend allowed “target rich” environment but the actual drug
discovery process has been suffered by “bottle neck” of the good drug candidates. Now a day,the chemistry side of drug discovery has been more concerned on the innovated drug discovery
platform such as artificial intelligence (AI) The combined efforts of chemical biology andchemo-informatics have been more productive way for improving overall efficiency of theoverall process. Systems chemo-informatics and fragment-based lead discovery of thebiological targets for their chemical ligands may afford an efficient method for small molecule
entity in many disease areas.

References[1] Novel pendrin inhibitor attenuates airway hyperresponsiveness and mucin expression inexperimental murine asthma. Journal of Allergy and Clinical Immunology,
https://doi.org/10.1016/j.jaci.2019.07.016[2] Control of leucine-dependent mTORC1 pathway through chemical intervention of leucyltRNA synthetase and RagD interaction. Nature Communications 2017, 732.
[3] Small-molecule binding of the axin RGS domain promotes β-catenin and Ras
degradation. Nature Chemical Biology 2016, 12, 593-600.

29

Prediction of Membrane Permeability
and Plasma Protein Binding of Cyclic Peptides
Satoshi Sugiyama, Jianan Li, Yuta Yamada, Shotaro Murata,
Yasushi Yoshikawa, Masahito Ohue, and Yutaka Akiyama*
Department of Computer Science, Tokyo Institute of Technology, Japan*akiyama@c.titech.ac.jp

Cyclic peptide drugs are attracting broad attentions because of their higher target specificity
and promising feasibility as PPI inhibitors. However, even though they can provide excellent
target affinity, their tendency of lower membrane permeability and lower plasma protein
binding (PPB) bring severe difficulties in terms of ADMET of the compound. Thus we havebeen developing computational methods to quickly predict membrane permeability and PPB ofcandidate cyclic peptides, in order to efficiently guide the whole peptide design process which
are currently lead only by the experimental capacity of rapid peptide display system.
Our approach toward membrane permeability prediction is composed of several different
elements: machine learning-based regression model with 2D chemical descriptors, 3D
descriptors combined with intensive peptide conformation search, as well as full molecular
dynamics (MD) simulation of membrane permeation phenomenon. Because membrane
permeation occurs in milli-seconds and it is prohibitively long time for a straightforward MD
simulation. We combine enhanced-sampling techniques and power of large-scale GPU clusters(Tokyo Tech's TSUBAME3.0 equipped with 2160 x P100 in total, and AIST's ABCI systemequipped with 4352 x V100 GPUs in total). Membrane permeability can be directly calculated
when its potential mean force profile is correctly obtained, or roughly estimated using sampled
3D conformations involved in the permeation steps based on our machine-learning model.
On the other hand, for the prediction of plasma protein binding (PPB), we mainly utilize
machine learning techniques (random forest, SVR, deep learning, etc.). Our model works
reasonably good for smaller cyclic peptides and now we are trying to expand its boundary tolarger peptides. We have collected experimental PPB data for cyclic peptides, and also
developed a technique to utilize the PPB data publicly available for small molecules [1].
References[1] Tajimi T, Wakui N, Yanagisawa K, Yoshikawa Y, Ohue M, Akiyama Y. Computational
prediction of plasma protein binding of cyclic peptides from small molecule experimental datausing sparse modeling techniques, BMC Bioinformatics, 19(Suppl 19): 527, 2018.

30

A proteochemometric approach for beta-lactam antibiotic response
prediction in Staphylococcus aureus
Jae Hong Shin,1,* DongJun Kim,2 Mina Rho 2,*12Standigm Inc., Korea

Department of Computer Science, Hanyang University, Korea*jaehong.shin@standigm.com, *minarho@hanyang.ac.kr

Antibiotics resistance is becoming a great threat to human health in worldwide. To develop new
antibiotics, every synthesized chemical compound is evaluated by antibiotic activity assay
across resistance strains which is time consuming and laborious. Here, we attempt to build aproteochemometric model to predict beta-lactam antibiotics responses across Staphylococcus
aureus. We first collected structures of beta-lactam and their antibiotic responses, and protein
sequences of four different penicillin binding proteins (PBP1-4), which may be involved inbeta-lactam resistance, from 18 strains of S. aureus. We then integrated structural informationof beta-lactam with protein sequences. The compiled dataset consists of 210 samples including
118 beta-lactam structures and their antibiotic responses across 18 strains. The Random Forestmodels is employed to build a classification model with recursive feature selection since wegenerated very large features (p = 5165) including molecular descriptors representing betalactam structures and position specific mutations for PBP proteins of S. aureus. In results, we
successfully built a model resulting in f1-score of 0.847 with the classification error of 16.2%.
Our random forest model consists of 47 features including molecular descriptors and position
specific mutations of penicillin binding proteins (PBP1-4). We also conducted a leave-one-out
cross validation and it resulted in f1-score of 0.816 with the classification error of 19.0%. In
addition to antibiotic response prediction, our model is able not only to capture importantchemical features correlating to antibiotic responses but also important mutation positions inproteins which may help to understand protein structural mechanism of resistance.
References[1] Darst BF, Malecki KC, Engelman CD. Using recursive feature elimination in random
forest to account for correlated variables in high dimensional data. BMC Genet.
2018;19(Suppl 1):65. Published 2018 Sep 17. doi:10.1186/s12863-018-0633-8[2] Ventola CL. The antibiotic resistance crisis: part 1: causes and threats. P T.
2015;40(4):277–283.

31

32

Session 2
Structures of Key Drug Targets(Chairs: Takatsugu Hirokawa, and Yun Tang)

33

CryoEM analysis of the GPCR neurotensin receptor 1-G protein complex
for the future engineering of biased allosteric modulator
Hideaki Kato*The University of Tokyo, Japan*hekato@bio.c.u-tokyo.ac.jp

G protein-coupled receptor (GPCR) family is one of the largest membrane receptor protein
families in human, and they are involved in almost every physiological process. About onethird of FDA-approved drugs target GPCRs and they represent very attractive drug targets[1].
The neurotensin receptor 1 (NTSR1) is one of such GPCRs involved in regulation of bloodpressure, body temperature, weight, and response to pain. NTSR1 couples to multiple G-protein
subtypes, but the molecular details of G-protein activation remain unknown. In this seminar, I
will present 3Å structures of the human NTSR1 in complex with the heterotrimeric Gi1 protein
in two distinct conformations (C and NC state)[2]. While the C-state complex is similar torecently reported GPCR-Gi/o complexes, the G protein in the NC state is rotated by ~45 degrees
relative to the receptor. NTSR1 in the NC state exhibits features of both active and inactive
conformations, suggesting that the structure represents an intermediate along the G-protein
activation pathway. This structural information provides insights into the complex process of
G-protein activation and paves the way for the future engineering of biased allosteric modulator
to control the specific G protein and/or arrestin signaling pathway.
References[1] Santos R et al. A comprehensive map of molecular drug targets, Nat. Rev. Drug Discov.,
16, 19-34, 2017.
[2] Kato HE et al. Conformational transitions of a neurotensin receptor 1-Gi1 complex,
Nature, 572, 80-85, 2019.

34

Discovery and Development of Novel CCR5 Antagonists
Jiang Wang, Huan Chen, Jian Li, Beili Wu, Yong-Tang Zheng*, Hualiang Jiang, Hong Liu*aState Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of

Sciences, 555 Zu Chong Zhi Road, Shanghai 201203, China
Key Laboratory of Bioactive Peptides of Yunnan Province, Key Laboratory of Animal Models andb

Human Disease Mechanisms of Chinese Academy of Sciences, KIZ-CUHK Joint Laboratory of
Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese
Academy of Sciences, Kunming, Yunnan 650223, China*hliu@simm.ac.cn; zhengyt@mail.kiz.ac.cn
CC-chemokine receptor 5 (CCR5) is an attractive target for preventing the entry of humanimmunodeficiency virus 1 (HIV-1) into human host cells. Maraviroc is the only CCR5 antagonist, andit was marketed in 2007. To overcome the shortcomings of maraviroc, structure-based drug design was
performed to minimize CYP450 inhibition and to enhance anti- HIV potency and bioavailability. A
series of novel 1-heteroaryl-1,3-propanediamine derivatives were synthesized, displaying CCR5antagonist activities in the 2.3−296.4 nM range. Among these, compounds 21 and 34 were the mostpotent CCR5 antagonists, with excellent in vitro anti-HIV-1 activity, low cytotoxicity, and an acceptable
pharmacokinetic profile. Furthermore, the X-ray crystal structures of compounds 21 and 34 bound to
CCR5 were determined at 2.8 Å resolution. Compound 34 exhibited no CYP450-inhibition activity,
which overcomes the potential drug−drug interaction of maraviroc. Compound 34 represents apromising drug candidate for HIV-infection treatment.

Keywords: CCR5, antagonist, HIV-1, CYP450, drug candidate
References[1] Tan, Q. X.; Zhu, Y.; Li, J.; Chen, Z. X.; Han, G. W.; Kufareva, I.; Li, T. T.; Ma, L. M.;
Fenalti, G.; Li, J.; Zhang, W. R.; Xie, X.; Yang, H. Y.; Jiang, H. L.; Cherezov, V.; Liu, H.;
Stevens, R. C.; Zhao, Q.; Wu, B. L. Structure of the CCR5 chemokine receptor-HIV entry
inhibitor maraviroc complex. Science 2013, 341, 1387−1390.
[2] Walker, D. K.; Abel, S.; Comby, P.; Muirhead, G. J.; Nedderman, A. N.; Smith, D. A. Species
differences in the disposition of the CCR5 antagonist, UK-427,857, a new potential treatment
for HIV. Drug Metab. Dispos. 2005, 33, 587−595.
[3] Peng, P.; Chen, H.; Zhu, Y.; Wang, Z.; Li, J.; Luo, R.; Wang, J.; Chen, L.; Yang, L.; Jiang,
H.; Xie, X.; Wu, B.;* Zheng, Y.;* Liu. H.* Structure-Based Design of 1-Heteroaryl-1,3Propanediamine Derivatives as a Novel Series of CC-chemokine Receptor 5 (CCR5)
Antagonists. J Med. Chem. 2018, 61, 9621−9636.

35

Molecular modeling study for identification of binding siteof AIMP2-DX2 inhibitor
Yuno Lee,* Srigouri Huddar and Sunkyung Lee
Korea Research Institute of Chemical Technology, Daejeon 34114, Korea*yunolee1@krict.re.kr

Alternative splicing variant of Aminoacyl-tRNA synthetase-interacting multifunctional
protein2 (AIMP2) lacking exon2, AIMP2-DX2 (hereafter DX2), was identified as an oncogene
differently from AIMP2 [1]. Recently, our collaborators have been found that inhibition of theprotein-protein interaction between DX2 and HSP70 suppressed the DX2-mediated tumor
progression [2]. Since structural understanding of target protein could be important to overcome
huge obstacles in drug discovery, 3D structures of the proteins have been analyzed byexperimental X-ray crystallography and NMR. But it is difficult to identify the disordered
flexible region on many target proteins using traditional analysis tools. Due to the significanceof this flexible region for protein function, many efforts are being devoted. DX2 also has adisordered flexible region in the N-terminal site, N-loop consisting of 50 amino acids. To study
the binding mechanism of BC-DXI-inhibitor with DX2-N-loop, we performed molecular
dynamics (MD) simulations. From the result of MD simulations, we found that BC-DXIinhibitor binds to the pocket surrounded DX2-N-loop and GST-N domain, and the binding key
residues were successfully proved by mutagenesis experiments. This MD study also suggested
the inhibition mechanisms of BC-DXI-inhibitor, competitive interference to access of HSP70
and masking of HSP70 binding residues located in surface of DX2 by conformational changeof DX2 induced by hydrophobic intramolecular interaction between DX2-N-loop and GST-N
in bound state of BC-DXI-inhibitor.
References[1] Choi JW, Kim DG, Lee AE, Kim HR, Lee JY, et al. Cancer-Associated Splicing Variantof Tumor Suppressor AIMP2/p38: Pathological Implication in Tumorigenesis. PLOS
Genetics, 7(3), e1001351, 2011.
[2] Lim S, Cho HY, Kim DG, Roh Y, Son S, et al. Tumor-promoting interaction between heatshock protein 70 and AIMP2-DX2, Nature Chemical Biology, in revision.

36Computer Aided Drug Discovery with Natural Compounds
Ji Young LEE1, Sangwon LEE1,2, Ayoung BAEK1, Myungwon SEO2, Kyoung Tai NO1,2,*
Bioinformatics and Molecular Design Research Center, Seoul Korea

1

2

Department of Biotechnology, Yonsei University, Seoul Korea*ktno@bmdrc.org

Natural products on Earth contain huge number of structurally diverse compounds and most of
the compounds are involved in the physiological activity of living organisms.

For this reason,

many drugs have been developed from natural compounds or chemically modified naturalcompounds.

It has been studied by many researchers for a long time to find naturalcompounds with physiological activity, but the traditional natural product research strategy is
low in R&D efficiency due to the need for much time and labor intensity.
To overcome the shortcoming of the natural product research, BMDRC working on two
projects, 1) on developing a (MS / MS big data)-(High precision MS / MS spectrometer)-(ANNbased Artificial Intelligent) fusion system that determines the structure of the molecules
involved in an extract without separating each compound from the extract, 2) among the
300,000 known natural compounds, 3000 representative compounds with structuralrepresentativeness and high drug-like structure (both rule of five & rule of four) compounds
were selected and construct NP3000 library.

By combining 1) & 2), we will construct natural

product research platform, Flora Genesis system.
This presentation, we will introduce the progress of the Flora Genesis system construction andwill show some of research results performed with currently built system though there are still
a lot to be developed and added to the system.

37

38

Session 3
Artificial Intelligence for Drug Discovery(Chairs: Weiliang Zhu, and Gyoonhee Han)

39

Drug discovery with artificial intelligence: advances and challenges
Xin Yang, Yifei Wang, Shengyong Yang*
State Key Laboratory of Biotherapy and Cancer Center, West China Hospital,
Sichuan University, Chengdu, Sichuan 610041, China*yangsy@scu.edu.cn

The research and development (R&D) cycle for innovative small-molecule drugs faces many
challenges, such as high cost-to-market, limited success in clinical trials, and long cycle times.
The productivity of drug R&D in the pharmaceutical industry remains on the decline, despite
record expenditures. Courses leading to this situation might be complicated. However, thecomplexity of drug R&D could be one of the key reasons. Artificial intelligence (AI) provides
opportunities for dealing with this kind of complex task. AI, specifically, deep learning, hasmade great progress in the past years, and has been successfully applied in areas such as image
and voice recognition, natural language processing, auto-driving, and intelligence game. AI has
also been applied to the drug R&D. Nevertheless, the progress of AI’s application in drug R&D
is still slower than that in aforementioned areas. We will in this presentation make a summary
to the history of AI, AI’s application in drug R&D, and challenges of AI algorithms faced indrug–related studies. Several research works that have been done recently by us to improve theperformance of AI algorithms in drug-related studies will be presented as well.
References[1] Yang X, Wang Y, Byrne R, Schneider G, Yang S, Concepts of Artificial Intelligence for
Computer-Assisted Drug Discovery. Chem. Rev. 119, 18, 10520-10594, 2019.

40

Multiple Approaches to Discover Human DDX3 Inhibitors
for Cancer Therapeutics
Keun Woo Lee
Division of Applied Life Science (BK21 Program), Gyeongsang National University (GNU),
501 Jinju-daero, Gazha-dong, Jinju, 52828 Republic of Korea
E-mail: kwlee@gnu.ac.kr

DDX3 belongs to RNA helicase family that demonstrates oncogenic properties and has gained
wider attention due to its role in cancer progression, proliferation and transformation.
Mounting reports have evidenced the role of DDX3 in several cancers making it a promisingtarget. In order to find out proper inhibitor against DDX3 we employed three different
strategies: 1) traditional pharmacophore modeling and virtual screening, 2) butein
modification, 3) De novo fragment designing. First, common feature pharmacophore and thereceptor-based pharmacophore models were generated and subsequently validated andutilized as 3D queries to screen the InterBioScreen database. This resulted in one best
compound, curcumin, that was escalated to molecular dynamics simulation studies and invitro analysis was conducted on three cell lines such as MCF-7, MDA-MB-231 and HeLa,
which were evaluated along with exemestane. Furthermore, via the 2nd and 3rd approaches,
additional seven compounds were computationally designed and synthesized.
The computationally retrieved compound was docked into the active site of the protein target(PBD code 2I4I) to estimate the binding affinity. The compound has interacted with two key
residues and has displayed stable molecular dynamics simulation results. The obtained results
illuminate the use of curcumin and the synthesized compounds can be adapted as alternative
DDX3 inhibitors and can serve as a chemical scaffold to design new small molecules.
Additionally, our results elucidate that the combinatorial treatments may show better results,
paving way for new research avenues.
Recently we also challenge to develop smarter CADD system or server which is able to use
AI or ML algorithms for better efficiency. With Korea Chemical Bank (KCB) data we aretrying to build AI-based CADD server which can help basic drug design work for open users.
The draft version will be introduced in the talk.

41

Visualizations within chemogenomic active learning reveal how protein
descriptors are used and underlying truths about chemogenomic and single
target QSAR model utility
J.B. Brown*
Kyoto University Graduate School of Medicine, Life Science Informatics Research Unit, Japan*jbbrown@kuhp.kyoto-u.ac.jp

Despite the concept of active learning for drug discovery being published in 2003 [1],it was not until only a few years ago that large-scale databases of chemogenomic databases
became available, and at AHeDD 2017 and 2018, I presented our work in the applications ofactive learning for chemogenomic-based drug discovery, where it has become clear that acareful selection of a reduced number of ligand-target pairs approaches the same predictive
performance as if one had used all available data for estimator (model) building [2]. Asymptotic
performance holds regardless of whether the active learning is retrospective (selection andprediction on the full dataset) or semi-prospective (withheld data that will not be available forselection and model building) [3].
Recently, our group further pushed to further clarify the domain of applicability ofactive learning by executing a semi-prospective study in which compounds classified as nonprobes were used to extract patterns and predict bioactivity on probes classified as chemicalprobes (specificity, potency). We particularly focused on the role of protein target descriptors,
starting with a biologically meaningless baseline identity descriptor and comparison to
improvements obtained with amino acid subsequence frequency descriptors. The key question,
how the improvement was achieved, was investigated and has refined expectations for machinelearning in drug discovery [4]. In this talk, I will discuss these findings and how they impact
the philosophy by which we approach e-drug discovery.
References[1] Warmuth, M.K. et al., 2003. J. Chem. Inf. Comput. Sci. 43, 667–673.
[2] Reker, D. et al., 2017. Future Med. Chem. 9, 381–402.
[3] Rakers, C. et al., 2018. ChemMedChem. 13, 511-521.
[4] Polash, A.H. et al., 2019. Molecules 24, 2716.

42

Bioinformatics-Facilitated Therapeutic Target Discovery
Prof. Feng ZHU*
College of Pharmaceutical Sciences, Zhejiang University, China*zhufeng@zju.edu.cn

With the advent of the era of bioinformatics and artificial intelligence (AI), the bioinformaticsfacilitated drug discovery (especially the discovery of novel therapeutic target) has attracted
great interest from the researchers. In this report, I would like to introduce the most recent
advances in my lab (Lab of Innovative Drug Research and Bioinformatics), which included: (1)the target big data and the construction of deep learning algorithm, and (2) the development of
the target identification methods based on multiple OMICs data. Welcome to visit our website
at: https://idrblab.org/

References[1]

Yin J, Sun W, Li F, …, Zeng S*, Yu L*, Zhu F*. VARIDT 1.0: variability of drugtransporter database. Nucleic Acids Res, doi: 10.1093/nar/gkz779, 2019.

[2]

Wang Y, Zhang S, Li F, …, Chen Y*, Zhu F*. Therapeutic target database 2020:
enriched resource for facilitating research and early development of targeted
therapeutics. Nucleic Acids Res, doi: 10.1093/nar/gkz981, 2019.

[3]

Yang Q, …, Qiu Y*, Zhu F*. Consistent gene signature of schizophrenia identified by anovel feature selection strategy from comprehensive sets of transcriptomic data. Brief
Bioinform, doi: 10.1093/bib/bbz049, 2019.

[4]

Tang J, Fu J, Wang Y, …, Zhu F*. Simultaneous improvement in the precision,accuracy and robustness of label-free proteome quantification by optimizing data
manipulation chains. Mol Cell Proteomics, 18, 1683-1699, 2019.

[5]

Zhu F*, …, Chen Y*. Clinical success of drug targets prospectively predicted by insilico study. Trends Pharmacol Sci, 39, 229-231, 2018.

[6]

Li B, Tang J, Yang Q, …, Zhu F*. NOREVA: normalization and evaluation of MSbased metabolomics data. Nucleic Acids Res, 45, 162-170, 2017.

43

44

Session 4
High-Performance Molecular Simulation(Chairs: Keun Woo Lee, and Shengyong Yang)

45

Development of the rapid QM/MM molecular dynamics techniques
aimed at medium molecular drug discovery
Hiroaki Nishizawa
Center for Computational Sciences, University of Tsukuba

Molecular dynamics (MD) has been employed for investigating the properties of biomolecularsystems. In several cases, MD simulations are performed by the molecular mechanical (MM)approach because a long time step is required for obtaining sufficient sampling. However, theMM approach cannot accurately describe a biomolecular system including a metal ion because
the interaction parameters do not change during MM simulations. On the other hand, metal ionsplay a crucial role in the living body. For example, amyloid fibrils of amyloid-β (Aβ) peptides
cause Alzheimer’s disease. During the initial formation of the amyloid fibrils, metal ions form
coordinate bonds with the Aβ peptides and precipitate the aggregation of Aβ peptides. A
quantum mechanical/molecular mechanical (QM/MM) approach based on the densityfunctional tight-binding (DFTB) theory[1] is a useful tool for analyzing such a chemical reaction
system in detail. However, since the computational cost of the DFTB calculation is proportional
to the cube of the system size, the number of atoms for QM region is limited to small. In thisstudy, an efficient DFTB method by the combination of the DFTB method with the divide-andconquer (DC) method is developed[2] aimed at MD simulation for medium-size molecules. For
assessing the performance of this method, the computational cost and energy deviations areinvestigated for simple molecules. Furthermore, DFTB/MM-MD simulations are conducted fora system consisting of two Aβ peptides and a zinc ion in explicit water.
References[1] Elstner M, Porezag D, Jungnickel G, Elsner J, et al. Self-consistent-charge densityfunctional tight-binding method for simulations of complex materials properties, Phys.
Rev. B, 58, 7260–7268, 1998.
[2] Nishizawa H, Nishimura Y, Kobayashi M, Irle S, Nakai H, Three pillars for achieving
quantum mechanical molecular dynamics simulations of huge systems: Divide-andconquer, density-functional tight-binding, and massively parallel computation, J. Comput.
Chem., 37, 1983–1992, 2016.

46

New Method for Highly Efficient MD Simulation
Cheng Peng, Jinan Wang, Zhijian Xu, Weiliang Zhu*
Shanghai Institute of Materia Medica, Chinese Academy of Science, Shanghai 201203, China* wlzhu@simm.ac.cn

Not only appropriate structural conformation, but also dynamic properties are important forunderstanding proteins’ function. Although the number of experimentally determined protein
structures is rapidly increased in PDB, it is still a great challenge to obtain dynamic propertiesof proteins by experimental protocol. Replica exchange molecular dynamics (REMD)
simulation is a popular enhanced sampling method, which is widely used for exploring atomic
mechanism of protein conformational change. However, REMD requires huge computational
resources, largely limiting its application. In this talk, I will show you the methods wedeveloped for improving the availability and efficiency of a variant of REMD, namely vsREMD
and its application on adenylate kinase (AdK) as an example. While vsREMD required lessthan 40% of the replicas for conventional REMD, it achieved consistent results with that fromconventional REMD and experimental studies. Thus, vsREMD is able to characterize protein
conformational change and associated free energy profile for better understanding proteins’
function.

47

Next Generation Accelerated Supercomputing:Cygnus System at University of Tsukuba
Taisuke Boku
Director, Center for Computational Sciences, University of Tsukuba
taisuke@ccs.tsukuba.ac.jp

HPC (High Performance Computing) is now one of the most important technologies toproceed any scientific field including drug discovery. The traditional supercomputing
facilities have been contributing to the scientific calculations which require very highperformance of floating point computation. With such a background, today's world
leading supercomputers are equipped with GPU (Graphics Processing Unit) beside ofordinary CPU (Central Processing Unit). Actually, about half of the systems in TOP-10
machines in the world are the large cluster systems with tens of thousands of GPUs.
However, the request for new fields of scientific computation such as deep learning ismuch more complicated where the traditional simple computing power cannot cover it.
One of the big change in the processor architecture is the change of floating point
precision, FP16 (16-bit half precision floating point) for example. Although new
generation of GPUs and CPUs are supporting such a request nowdays, we need moreaggressive challenge for new system architecture not only for high performance but also
for high performance per energy consumption.
In our Center for Computational Sciences, University of Tsukuba, we have been
researchingtheoriginaltechnologies

towardnext

generation

acceleratingsupercomputing. GPU is still the main player for it, but we need to consider wider variety
and possibility of other kind of accelerators. One of the key technologies for processor
architecture recently focused is FPGA (Field Programmable Gate Array) where the logic
circuit itself can be programmed by some specific hardware description language
according to the algorithm of target application. We are building a new method to
combine GPU and FPGA together in a single system to compensate the weak point of
GPU to be covered by the flexibility of FPGA toward complicated algorithms andproblems. As the practical testbed for this challenge, our center introduced the world firstcluster combing GPU and FPGA technologies for advanced scientific research. In thistalk, I will introduce such a new concept of supercomputing with system development
and applications toward next generation accelerated supercomputing.

48

In silico Drug Discovery using Molecular Modeling and Simulation
Takatsugu Hirokawa1,2,*
1

Molecular Profiling Research Center for Drug Discovery, National Institutes of Advanced Industrial
Science and Technology, Tokyo, Japan2

Department of Chemical Biology, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan*t-hirokawa@aist.go.jp

With the development of increasing number of genome sequence dataand protein structure

determination, pharmacological and drug discovery research based on the structural biology
data have been accelerated.

However, some of experimental structural data may be

inconvenient to use for drug discovery due to the constraints on crystallization conditions orundesirable biological state. Molecular modeling and simulation can help to bridge the gap
between original state of experimental structural data and drug discovery oriented structuralmodels.

Structural models from the collaboration between the experimental data and in silico

technology will be useful to guide sophisticated drug discovery process. In some collaboration
projects, we proposed the supporting and developing of in silico drug discovery using molecularmodeling and simulation based on fundamental technologies such as homology modeling,
docking simulation and molecular dynamics (MD) simulation [1-3]. Our goal is to achieve insilico drug discovery technology having high practicability for translational research inrelationships between pharmaceutical industry and academia.

References[1] Shiba-Ishii A, et al. Stratifin Inhibits SCF(FBW7) Formation and Blocks Ubiquitinationof Oncoproteins during the Course of Lung Adenocarcinogenesis. Clin Cancer Res. 2019
May 1;25(9):2809-2820.
[2] Toyoda Y, et al. Ligand binding to human prostaglandin E receptor EP(4) at the lipidbilayer interface. Nat Chem Biol. 2019 Jan;15(1):18-26.
[3] Asamitsu K, et al. MD simulation of the Tat/Cyclin T1/CDK9 complex revealing thehidden catalytic cavity within the CDK9 molecule upon Tat binding. PLoS One. 2017 Feb
8;12(2):e0171727.

49

50

Flash Talk Session
Presentations by Young Researchers in Asia-Pacific Regions(Chair: Masahito Ohue)

51

Systematic construction of the cosolvents sets for cosolvent MD (CMD)with the large-scale simulation
Keisuke Yanagisawa,1 Yoshitaka Moriwaki,1 Tohru Terada,2,3 Kentaro Shimizu1,2,*
1

Department of Biotechnology, Graduate School of Agricultural and Life Sciences,
The University of Tokyo, Japan
2

3

Interfaculty Initiative in Information Studies, The University of Tokyo, Japan

Agricultural Bioinformatics Research Unit, Graduate School of Agricultural and Life Sciences,
The University of Tokyo, Japan*shimizu@bi.a.u-tokyo.ac.jp

Cosolvent MD (CMD) is an MD simulation of a protein in explicit water molecules mixed withcosolvent molecules. The simulation has been used for hotspot detection [1,2], binding site
identification [3] and binding energy estimation [3]. Existing methods (i.e. MixMD [1,2],
SILCS [3], and MDmix [4]) utilize small molecules which represent functional groups ofcompounds, such as isopropanol and benzene; however, different sets of cosolvents were usedin these studies and no guidelines have been proposed for the selection of the cosolvents except
for use of the functional groups. Thus we aimed to propose a method to construct a set ofcosolvents in a rational way.
In the present study, we extracted typical substructures from FDA approved drugs and generated> 130 cosolvent structures. For each cosolvent molecule, we conducted 10-ns CMD simulations
20 times to generate a spatial probability distribution map of cosolvent atoms (Pmap). After
that, the relationships between the cosolvents were visualized based on the similarity between
the Pmaps. We found a cosolvent pair with the Pmap similarity greater than 0.75 tended to sharesimilar structural features. Using the Pmap similarity, we performed a K-means clustering on
the cosolvents. Instead of using the centroids, we selected a representative from each cluster so
as to maximize the worst similarity between the representative and any member in the cluster.
When we set K = 15, every cosolvent had > 0.75 similarity to one of the representatives.
References[1] P Ghanakota et al., J Chem Inf Model, 58, 784−793, 2018.
[2] PMU Ung et al., Biopolymers, 105, 21−34, 2016.
[3] VD Ustach et al., J Chem Inf Model, 59, 3018−3035, 2019.
[4] D Alvarez-Garcia & X Barril, J Med Chem, 57, 8530−8539, 2014.

52

Investigation of protein-protein interactions and hot spot region between
PD-1 and PD-L1 by fragment molecular orbital method
Hocheol Lim,1,2,* Jongwan Kim,1,2 Jiaxin Liu,1 Kyoung Tai No2,*
1

Department of Biotechnology, Yonsei University, Seoul, Republic of Korea

Bioinformatics and Molecular Design Research Center (BMDRC), Yonsei University, Seoul,

2

Republic of Korea*ihc0213@yonsei.ac.kr

Inhibitors to interfere protein-protein interactions (PPI) between programmed cell death 1 (PD1) and programmed death ligand-1 (PD-L1) block evasion of cancers from immune
surveillance. Analyzing hot spot residues in PPI is important for small-molecule drug
development. In order to find out hot spots on PPI interface in PD-1/PD-L1 complex, we
analyzed PPI in PD-1/PD-L1 with a new analysis method, 3-dimensional scattered pairinteractions energies (3D-SPIEs), which assorts significant interactions with fragment
molecular orbital (FMO) method. By additionally analyzing PPI in PD-1/antibody and PDL1/antibody complexes, and small-ligand interactions in PD-L1/peptide and PD-L1/smallmolecule complexes, we narrowed down the hot spot region with 3D-SPIEs-based interaction
map, which integrates PPI and small-ligand interactions. Based on the map, there are two hot
spot regions in PPI of PD-1/PD-L1 and the first hot spot region is important for inhibitors. In
particular, Y56, E58, and N66 in the first hot spot of PD-L1 are important for PD-L1-antibodies
and small-inhibitors in common, while M115 is important for small-inhibitors. Therefore, the
3D-SPIEs-based map would provide valuable information for designing new small-molecule
inhibitors to inhibit PPI of PD-1/PD-L1 and the FMO/3D-SPIEs method provides an effectual
tool to understand PPI and integrate PPI and small-ligand interactions at a quantum mechanicallevel.

53

Metagenome analysis implies bacterial community andgene category composition disorder in periodontal disease sites
Kazuki Izawa 1, *, Kazuko Shibayama 2, Masahito Ohue 1,
Kazuyuki Ishihara 2, Yutaka Akiyama 1
1

Department of Computer Science, School of Computing, Tokyo Institute of Technology, Japan
2

Microbiology, Department of Basic Science, Tokyo Dental College, Japan* izawa@bi.c.titech.ac.jp

Periodontal disease is a complex infection caused by various bacteria accumulated in gingival
sulcus as plaque. In previous study, some bacterial species are considered as the causative
because they are isolated from periodontal disease sites [1]. However, recent metagenomic
approach reveals that the causatives are resident bacteria. Therefore, periodontal disease now
considered that it caused by dysbiosis in plaque [2].
In this study, to reveal the differences in bacterial community and gene category composition
between periodontal disease site and healthy site in Japanese, we sampled 28 plaques including
healthy and disease sites from 5 healthy and 14 affected persons. The sequences from plaquesamples were aligned to 16S rRNA database distributed by NCBI with BLAST and KEGG
genes database with GHOSTZ-GPU [3] for bacterial community and gene categorycomposition analysis, respectively. These alignment results were post-processed by MEGAN
and HUMAnN, and the composition data were subjected to the multivariate analysis.
The multivariate analysis suggests several taxonomic and gene category features and
PERMANOVA implies these features represent the states of sites (p < 0.05). This result suggeststhat suggested features are useful to early detection and risk evaluation of periodontal disease.
References[1] Socransky SS, Haffajee AD. Dental biofilms: difficult therapeutic targets, Periodontol
2000, 28, 12-55, 2002.
[2] Honda K, Littman DR. The microbiome in infectious disease and inflammation, Annu Rev
Immunol. 30, 759-795, 2012.
[3] Shuji Suzuki, Masanori Kakuta, Takashi Ishida, Yutaka Akiyama. GPU-Acceleration ofSequence Homology Searches with Database Subsequence Clustering, PLoS ONE, 11(8):e0157338, 2016.

54

In silico drug discovery targeting Hippo pathway and YAP-TEAD Protein
Protein Interactions for small molecules anti cancer agent
Kim, Jongwan ,1,2* No, KT1,2
1

Computational Systems Biology Lab.,Department of Biotechnology, Yonsei University, Seoul,
Republic of Korea

2

Bioinformatics and Molecular Design Research Center, Yonsei Engineering Research Park,Seoul,
Republic of Korea

Hippo pathway is one of important pathways regulating tissue growth and proliferation.
Dysregulation on this pathway can result in overgrowth of phenotypes because of amalfunction of stem cell proliferation, differentiation and apoptosis, which are directly
involved with components to cancer cell developments. Protein-Protein interaction (PPI) in
YAP (yes-associated protein) and TEAD (transcriptional enhancer associate domain) is a key
interaction which regulate cancer cell growth in hippo pathway. Regulating YAP-TEAD
Protein-Protein interaction by small molecules could play an important role in cancer therapy.
In this study, we identified small molecules inhibiting YAP-TEAD interactions by in silico
approach. Molecular hits were identified by pharmacophore based virtual screening. The hit
compounds from virtual screening were tested by surface plasmon resonance(SPR),
Luciferase activity test. As a result, several hit compounds were identified to inhibit YAPTEAD protein-protein interactions. These selected hit compounds also evaluated bymolecular docking study, and FMO(Fragment Molecular Orbital) binding energy calculations.
Overall results, we identified hit compounds to inhibit YAP-TEAD PPIs, and also analyzed hit
compound’s binding interaction energy to TEAD Protein.

55

Discovery of Leucyl-tRNA Synthetase (LRS) PPi inhibitor for modulating
mTORC1 pathway via the chemical biology approach
Chulho Lee,1,2,* Doona Song,1,2 Gyoonhee Han1,2,*
1
2

Department of Integrated OMICS for Biomedical Science, Yonsei University, South Korea.

Translational Research Center for Protein Function Control, Department of Biotechnology, Yonsei
University, South Korea.

*chulho.l41@yonsei.ac.kr, gyoonhee@yonsei.ac.kr

In the early stage of drug discovery, various computational methods such as in silico hit
prioritization and virtual screening, as well as chemical and biological information helped toshorten the discovering process from hit to lead. Leucyl-tRNA synthetase (LRS) is known as aleucine sensor for modulation of mTORC1 pathway. We exhibited the leucine sensing functionof LRS for mTOR activation could be decoupled from its catalytic activity. Based on theseresearch background, we discovered Leucyl-tRNA synthetase (LRS) PPI inhibitors thatmodulate the mTORC1 signaling pathway. The lead compound was successfully discovered via
optimization using pharmacophore generated from the active screened compounds, and themechanisms of action (MoA) was proved with active chemical by chemical biology approach.
References[1] Kim JH, et al. Control of leucine-dependent mTORC1 pathway through chemicalintervention of leucyl-tRNA synthetase and RagD interaction. Nat Commun, 8, 732, 2017.

56

Novel computational approach for natural product (NP) research:development of natural compound molecular fingerprint (NC-MFP) for
exploring new NP-based drugs
Myungwon Seo,1, * Hyun Kil Shin,3 Yoochan Myung,4 Sungbo Hwang,1, 2 Kyong Tai No1, 2, **1

Department of Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul,
Republic of Korea

2

Bioinformatics and Molecular Design Research Center, Yonsei Engineering Research Park, Seoul,
Republic of Korea
3

Department of Predictive Toxicology, Korea Institute of Toxicology, Daejeon, Republic of Korea
4

Department of Biochemistry and Molecular Biology, Bio21 Institute, University of Melbourne,
Melbourne, Victoria 3010, Australia*seomyungwon@gmail.com**ktno@bmdrc.org

A natural compound (NC) produced by a living organism.[1] Since many of new drugs candidates areanalogous to or derived from the NCs[2], computer-aided research on the relationship between the NPs
and their biological activities have been widely studied by describing the molecular structure.
Since structure of NC has distinctive structural characteristics compared to synthetic compound[3],previously developed topological descriptors have limitation in expressing molecular structure of
specific NC.
In this study, novel molecular fingerprint, called Natural Compound Molecular Fingerprints (NCMFP) was developed for explaining NC structures related to biological activities. The NC-MFP
consists of scaffolds, scaffold-fragment connection points (SFCP), and fragments. The scaffolds ofNC-MFP constructed based on both the BM method and the classification system of Dictionary of
Natural Product database (DNP). Two kinds of binary classification tasks were introduced forevaluation purpose. Task Ⅰ is classifying NCs in commercial DB into NC or synthetic compound. Task
Ⅱ is classifying whether NCs with inhibitory activity in seven biological target proteins is active orinactive. In the result of tasks, NC-MFP outperformed compared with other molecular fingerprints.
Therefore, NC-MFP is potent molecular descriptor for virtual screening of NC for NP-based new
drugs development

References[1] All natural. Nat Chem Biol, 2007. 3(7): p. 351.
[2] Newman, D.J. and G.M. Cragg, Natural Products as Sources of New Drugs from 1981 to
2014. J Nat Prod, 2016. 79(3): p. 629-61.
[3] Rodrigues, T., et al., Counting on natural products for drug design. Nat Chem, 2016. 8(6):p. 531-41.
57

Predicting membrane permeability for cyclic peptides
Yasushi Yoshikawa,* Satoshi Sugiyama, Yuta Yamada, Shotaro Murata, Masahito Ohue,
Yutaka Akiyama
Department of Computer Science, School of Computing, Tokyo Institute of Technology, Meguro-ku,
Tokyo 152-8550, Japan.

*yoshikawa@c.titech.ac.jp

Following small molecule and antibody drugs, cyclic peptides are getting attention as one of
the new drug modalities. It is considered that cyclic peptides can have high potency to the drug
targets like antibody drugs and good pharmacokinetics properties like small molecule drugs.
For that reason, cyclic peptides could be drugs which inhibit intracellular protein-protein
interactions. However, there is a problem that most cyclic peptides are not membrane permeablewith the exceptions such as cyclosporine A. “Chameleonic” properties have been proposed asmembrane permeation mechanisms of cyclic peptides1. But research about thepharmacokinetics properties, including membrane permeability, has not progressed comparedto that of small molecules. This is a major challenge for developing cyclic peptide drugs.
In order to address that problem, we are developing a method to predict passive membrane
permeability of cyclic peptides by combining machine learning and molecular mechanics
simulations. In this presentation, an overview of our effort will be introduced.
Reference[1] Whitty, A. et al. Quantifying the chameleonic properties of macrocycles and other highmolecular-weight drugs. Drug Discovery Today 21, 712–717 (2016).

58

Development of prediction model for onset of disease
and risk factor analysis
Atsuyoshi Matsuda,1,* Hinako Toyama,1 Kenji Araki,2 Taisuke Ogawa,2 Ryosuke Matsuo2
Kenichiro Shimai,3 Atsushi Shibata,4Yasuyuki Ohara,1 Megumi Koshiba,1 Yuji Naraki,1 Yuki Sumiya,1
1
2

University of Miyazaki Hospital, Japan
3

4

Logbii, Inc., Japan

Chiba University Hospital, Japan

Advanced Institute of Industrial Technology, Japan*atsuyoshi.matsuda@logbii.com

In recent years, doctors' hard work has become a problem in Japan, which can lead to missing
signs of serious complications. If it is possible to predict complications, it will help doctors
and improve patient outcomes. In former studies, prediction of complications in type 2
diabetes[1], and prediction of complications of pneumonia after cerebral hemorrhage[2] wereresearched. But there are few studies that analyze risk factors with machine learning analysis.
We developed a prediction model based on machine learning for the onset of disease for the
purpose of helping doctors and improving the prognosis of patients. We also analyzed howeach risk factor affected the prediction. We used the data of patients admitted to University ofMiyazaki Hospital.
References[1] Tanaka S, Tanaka S, Iimuro S et al. Predicting Macro-and Microvascular Complications
in Type 2 Diabetes: The Japan Diabetes Complications Study/the Japanese Elderly
Diabetes Intervention Trial Risk Engine., Diabetes Care, 36, 1193-1199, 2013.
[2] Matsumoto K et al. Predicting the complication of pneumonia after cerebral hemorrhage
by the level of consciousness at admission., Japan Association for Medical Informatics,
2018.

59

GCMQA: a novel single-protein structure model quality assessment
method using graph convolution
Rin Sato,1,2 Takashi Ishida1,*
Department of Computer Science, School of Computing, Tokyo Institute of Technology, 2-12-1 W8-76

1

Ookayama, Meguro-ku, Tokyo 152-8550, Japan
AIST-Tokyo Tech Real World Big-Data Computation Open Innovation Laboratory (RWBC-OIL),

2

National Institute of Advanced Industrial Science and Technology (AIST)*ishida@c.titech.ac.jp

Tertiary structure information of a drug-target protein is useful in drug discovery. However,experimental determination of a protein structure is a hard task and thus computational structure
prediction methods from its amino acid sequence have been researched. Model quality
assessment, which estimates the quality of predicted structure models for selecting the final
prediction, is one of the most crucial processes in protein tertiary structure predictions. Most
state-of-the-art single-model assessment methods use machine learning and residue-wisefeatures [1]. In such features, interactions among residues are implicitly included as calculated
interaction energy and local structure properties, such as the secondary structures. Thus, theinteraction between residues is not directly used in the learning process.
To solve this problem, we proposed a novel single-mode quality assessment method thatassesses local structure quality based on interaction among residues by using graph convolution
neural networks [2]. We defined a graph of a protein structure whose node represents a residue
and edge represents the interaction between residues. We trained a graph convolutional neuralnetwork for the graph to output the quality of local structure of a residue. We evaluated theperformance of the proposed method and the proposed method showed a significant
improvement compared with previous methods.
References[1] Uziela K, Hurtado DM, Shu N, Wallner B, Elofsson A. ProQ3D: Improved model quality
assessments using deep learning. Bioinformatics. 2017;33:1578–80.
[2] Fout A, Byrd J, Shariat B, Ben-Hur A. Protein Interface Prediction using Graph
Convolutional Networks. In: Guyon I, Luxburg U V, Bengio S, Wallach H, Fergus R,
Vishwanathan S, et al., editors. Advances in Neural Information Processing Systems 30.

60

Contest-based compound screening enables identification of chemicallydiverse inhibitors of target proteins
Shuntaro Chiba1, Masakazu Sekijima2
RIKEN Medical Sciences Innovation Hub Program, Japan1

Department of Computer Science, School of Computing, Tokyo Institute of Technology, Japan2

shuntaro.chiba@riken.jp

At the early stages of drug discovery, there is a need to identify compounds with functional
potency, such as inhibitory activity against a target protein. This could be achieved by utilizing
computational approaches that can select potentially active compounds from a compound
library containing a vast number of chemically diverse compounds; rather than resorting tohigh-throughput screening, which is challenging due to cost-effectiveness. The important
aspect is deciding which method should be employed before conducting experimental
assessment. Computational selecting methods are typically assessed retrospectively and are notcompared under similar experimental conditions; thus, impeding the aforementioned decision.
Hence, with an objective to assess various methods prospectively under identical experimental
conditions; and to be able to identify active compounds against a target protein simultaneously,
we conducted a series of compound proposal contests in 2014[1], 2015[2], and 2016[3], whereparticipants were asked to propose potential actives from approximately two million
compounds. In the first and second contests, approximately 60 and 180 compounds for eachgroup, and in total 600 and 1991 compounds, respectively, were experimentally assessed. In
both contests, cumulatively proposed compounds were found to be chemically diverse than was
possible with the use of a single method. Further, especially in the second contest, we identified
a statistically warranted prediction method from among 11 participating groups. More
importantly, identified actives were chemically diverse as well.
References[1] Chiba S, Ikeda K, Ishida T, Gromiha MM, Taguchi Yh, Iwadate M, et al. Identification ofpotential inhibitors based on compound proposal contest: Tyrosine-protein kinase Yes asa target. Sci Rep, 5, 17209, 2015.
[2] Chiba S, Ishida T, Ikeda K, Mochizuki M, Teramoto R, Taguchi YH, et al. An iterative
compound screening contest method for identifying target protein inhibitors using thetyrosine-protein kinase Yes. Sci Rep, 7, 12038, 2017.
[3] Chiba S et al., manuscript submitted.

61

Underestimated Noncovalent Interactions in Protein Data Bank
Zhijian Xu,* Weiliang Zhu*
CAS Key Laboratory of Receptor Research, Drug Discovery and Design Center, Shanghai Instituteof Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China*zjxu@simm.ac.cn, wlzhu@simm.ac.cn

Noncovalent interactions (NCIs) play essential roles in the structure and function of
biomacromolecules. There are various NCIs, e.g., hydrogen bonds (HBs), halogen bonds(XBs), cation-π, π-π interactions, and ionic bonds. The distribution of experimental determined
NCIs could be observed in protein data bank (PDB). The proportion of side-chain XBs tooverall XBs decreases as structural resolution becomes lower and lower, indicating theunderestimation of the XBs in PDB. With a systematic search in the PDB, we found that the
HB number per residue in proteins decreases as structural resolution becomes lower, implyingthat HBs are overlooked even today, regardless of the type of refinement approach used. Byutilizing the ratio of the observed HBs over pseudo HBs, we demonstrated that HBs in bothprotein-ligand and protein-protein interfaces are overlooked in structures deposited in PDB.
After the QM/MM optimization of 12 protein-ligand complexes, we showed that the overlooked
HBs could be recovered. Similarly, cation-π, π-π and ionic interactions were found to besignificantly lost, manifesting the universal underestimation of various NCIs. Considering thevital role of NCIs, it is important to recover the NCIs to facilitate drug design, to explore
protein-protein interaction and to study protein structure and function.
References[1] Xu, Z, Zhang, Q, Shi, J, Zhu, W. Underestimated noncovalent interactions in protein data
bank. J. Chem. Inf. Model, 59, 3389-3399, 2019[2] Zhang, Q, Xu, Z, Zhu, W. The underestimated halogen bonds forming with protein side
chains in drug discovery and design. J. Chem. Inf. Model, 57, 22-26, 2017[3] Zhang, Q, Xu, Z, Shi, J, Zhu, W. Underestimated halogen bonds forming with protein
backbone in protein data bank. J. Chem. Inf. Model, 57, 1529-1534, 201762

Evaluation of a Self-Balanced Force Field for Biomolecule Simulations
Sung Bo Hwang,1 Kyoung Tai No1,2,*
Yonsei University, Department of Biotechnology, Republic of Korea

1
2

Bioinformatics & Molecular Design Research Center, Republic of Korea*ktno@yonsei.ac.kr

The self-balanced force field (SBFF) proposed herein is the result of over 25 years of work
and was developed by integrating a set of potential energy function in which each term in anintermolecular potential energy function is derived based on experimental values, such as the
dipole moments, lattice energy, proton transfer energy, and X-ray crystal structures. The term,“self-balanced”, is used to emphasize the idea that the experimental observables that areconsidered to be the most relevant to each term are used for the parameterization, rather thanparameterizing all observables together against the target value. We evaluated the SBFF in three
ways. First, the conformational energy differences of 18 organic compounds were examinedbecause we adapted MM3[1] to describe the intramolecular interactions. Second, a moleculardocking simulation was conducted to determine the suitability in a biomolecule simulation to
examine the capability of expressing an energy-stable structure of the biomolecules. Last, logP
calculation was applied for 193 neutral peptides to show how well the SBFF combines with ageneralized solvation free energy density model[2], which was specifically designed for the
SBFF. Therefore, we concluded that the SBFF provides reliable information based on the
structure in a biological system and interprets the biological phenomena accurately byproviding more accurate evidence of the biological phenomena.
References[1] Allinger NL, Yuh YH, Lii JH. Molecular mechanics. The MM3 force field forhydrocarbons. 1. Journal of the American Chemical Society, 111, 8551-8566, 1989.
[2] Lee SH, Cho K-H, No KT, et al. A generalized G-SFED continuum solvation free energy
calculation model. Proceedings of the National Academy of Sciences, 110, E662, 2013.
[3] No KT. Grant JA. Scheraga, HA. Determination of net atomic charges using a modified
partial equalization of orbital electronegativity method. 1. Application to neutral
molecules as models for polypeptides. The Journal of Physical Chemistry, 94, 4732-4739,
1990.

63

Application of informatics approaches to compound library design for
intractable target chemical spaces
Kazuyoshi Ikeda,* Yugo Shimizu, Bao Yu, Masanori Osawa
Keio University, Faculty of Pharmacy, Division of Physics for Life Functions, Japan*ikeda-kz@pha.keio.ac.jp

In recent years, artificial intelligence (AI) represented by deep learning has been
applied to biochemistry and drug discovery. To improve the accuracy of prediction models
using the AI technologies, quality and quantity of data are required.
Protein-protein interactions (PPIs) are recognized as essential targets in recent drug
discovery. Recently, screening data targeting PPIs have been registered [1] in drug discovery
databases such as ChEMBL[2]. It also includes PPI inactive data (i.e., compounds that havebeen confirmed not to bind PPI). According to our analysis, the results of NIH screening
campaigns, those with a hit rate exceeding 1% account for 17% of the total screening. However,there are few screenings with a hit rate exceeding 1% when targeting PPI (less than 1% in the
total). That is, the hit rate of screenings for the PPI targets using the conventional compound
library is considerably low.
Our group (Keio University) is now developing an in-silico platform to design various
chemical libraries such as HTS diversity library, focused-library as well as core-library. So far,
we developed a relational database of chemical libraries for designing PPI inhibitors. This
research would uncover the role of data-resources to supply new compound information to
drug-targets with limited data volumes such as PPI. Finally, we would discuss how importantis data science for AI drug discovery.

References[1] Morelli X, Bourgeas R, Roche P., Curr Opin Chem Biol. 2011 Aug;15(4):475-81.
[2] ChEMBL: https://www.ebi.ac.uk/chembl/[3] PubChem: https://pubchem.ncbi.nlm.nih.gov/

64

Learning-to-rank for ligand-based virtual screening
Masahito Ohue*, Shogo D. Suzuki, Yutaka Akiyama
Department of Computer Science, School of Computing, Tokyo Institute of Technology, Japan* ohue@c.titech.ac.jp

Learning-to-rank (LTR) is a machine-learning method that is widely used for information
retrieval and for learning the order relation between data. Activity values cannot be obtained
directly; instead, the prediction result of a compound is obtained in the form of a list arranged
according to the activity. The characteristics of LTR can be considered to fall between those ofregression and binary classification. LTR is more robust against errors compared to regression,and higher accuracy can be expected when the ranking order is generated. After virtual
screening, experiments with biochemical assays are generally performed for verification.
However, the number of compounds that are selected for experimental verification is frequently
limited in advance because of limited budgets, time, and experimental facilities. These
problems can be overcome by employing LTR methods, which can rank compounds with highaccuracy in descending order of possible activity, and which are suitable for virtual screening.
We developed two LTR methods for virtual screening [1][2]. PKRank [1] can use
activity data of different targets for learning and is useful for finding hits of a target that have alack of known ligands. SPDRank [2] uses a technique that ignores the ranking of similar activity
ligands and is an effective method for learning from experimental values with large errors suchas high-throughput screening.

References[1] Suzuki SD, Ohue M, Akiyama Y. PKRank: A novel learning-to-rank method for ligandbased virtual screening using pairwise kernel and RankSVM, Artif Life Robot ics, 23(2):205-212, 2018.
[2] Ohue M, Suzuki SD, Akiyama Y. Learning-to-rank technique based on ignoring
meaningless ranking orders between compounds, J Mol Graph Model, 92:192-200, 2019.

65

66

AMED BINDS Project Special Sessions

67

In silico drug discov ery based on structural informatics and FMO

methodTeruki Honma*
RIKEN Center for Biosystems Dynamics Research, Japan*honma.teruki@riken.jp

To accelerate biological study and drug discovery, the aim of our laboratory is developing newtechnologies for in silico screening of biological tools and drug candidates. Our basic concept
is the integration of both informatics (statistical analysis, artificial intelligence (AI), etc.) andmolecular modeling theories (molecular dynamics (MD) simulation, quantum mechanics(Q M), etc.) to design promising drug candidatess. PALLAS, MUSES, LAILAPS, and FMOPBSA[1] as well as AI -based ADMET prediction models were developed for the efficient insilico screening based on the concept. Recently, the world first quantum mechanics calculation
database for protein systems named FMO-DB[2] has been released. In this session, the outlineof the technologies raised above and examples applied to drug discovery will be introduced.

Figure. Web interface and detailed FMO calculation data pages
References[1] Watanabe C, et al. Theoretical Analysis of Activity Cliffs among Benzofuranone Class
Pim1 Inhibitors Using the Fragment Molecular Orbital Method with Molecular
Mechanics Poisson-Boltzmann Surface Area (FMO+MM-PBSA) Approach. J Chem Inf
Model, 57(12), 2996-3010, 2017.
[2] https://drugdesign.riken.jp/ FMODB/

68

Molecular simulation studies on protein functions and improvement of theefficiency of cryo-EM data collection by machine learning
Tohru Terada,1,2,* Yuichi Yokoyama,1 Yoshitaka Moriwaki,2 Kentaro Shimizu1,2
1Interfaculty Initiative in Information Studies, The University of Tokyo, Japan

Graduate School of Agricultural and Life Sciences, The University of Tokyo, Japan2*

tterada@iu.a.u-tokyo.ac.jp

In the BINDS projects, we are conducting collaborative researches with experimental
researchers to clarify protein function mechanisms. Among these researches, we will first talkabout a molecular dynamics (MD) study of a multidrug transporter, MdfA [1]. MdfA is a
H+/drug antiporter working on the cell membrane of bacteria. When a proton binds to theprotein, its conformation is changed from an outward-open conformation to an inward-open
conformation. The proton is then released to the cytoplasm and a drug binds to the protein. As
a result, the outward-open conformation is restored and the drugs is released to the periplasm.
Since experiments have shown two acidic residues are important for the function, we performed
MD simulations for the protein with one or both of the residues protonated. When a specificresidue was protonated, the protein conformation was changed to an occluded conformationfrom both the inward-open and the outward-open conformations. Based on these results, we
proposed a mechanism of the functional conformational change. We will next talk about a new
method we have developed to facilitate the collaborative researches. Recent advances in cryoelectron microscopy (cryo-EM) have enabled protein structure determination at an atomic
resolution. Cryo-EM specimens are prepared by rapidly freezing the protein solution on a metal
grid coated with holey carbon film; this results in the formation of an ice film on each hole. The
thickness of the ice film is a critical factor for high-resolution structure determination—ice thatis too thick degrades the contrast of the protein image, while ice that is too thin excludes theprotein from the hole. Thus, trained researchers have to manually select “good” regions with anappropriate ice thickness. To alleviate this time-consuming burden, we have developed a deeplearning-based method to identify such “good” regions from low-magnification EM images.
Reference[1] Nagarathinam K, et al. Outward open conformation of a Major Facilitator Superfamily
multidrug/H+ antiporter provides insights into switching mechanism” Nat Commun, 9,4005, 2018.

69

Structural modeling of the entire EhV-ATPase in multiple states using
cryo-EM data and homology modeling
Yu Yamamori,1* Jun Tsunoda,2,3 Ray Burton-Smith3, Song Chihong3, Ryota Iino2,4,Kazuyoshi Murata2,3, Kentaro Tomii1
National Institute of Advanced Industrial Science and Technology, AIST, Japan, 2SOKENDAI, Japan

1

3

National Institute of Physiological Science NIPS, Japan, 4Institute of Molecular Science IMS, Japan*yu.yamamori@aist.go.jp

While many studies have focused on F-ATPase, the overall structure of V-ATPase inEnterococcus hirae (EhV-ATPase) in addition to the mechanism of an ATP-driven ion-pumpremain unclear. Most recently, single-molecule analysis proposed that the chemo-mechanicalscheme of the catalytic domain of EhV-ATPase is involved in the different sub-states from F1ATPase [1]. The first entire structure was also obtained at 17Å resolution using single-particle
cryo-electron microscopy (cryo-EM) with Zernike phase plate [2]. In this study, we tried toobtain 3D models of the entire EhV-ATPase, which include the multiple states, with all nonhydrogen atoms, based on the density maps from the low resolution cryo-EM map. Ourmodeling protocols of EhV-ATPase, which is based on our own structure-prediction scheme[3], were as follows: 1) Homology models were built based on results of profile-profilealignment. 2) Model selection was initially performed with the correlations between individualmodels and the density maps, and finally the models were evaluated structurally using empiricalscoring functions such as verify-3D. 3) Molecular dynamics simulations were performed withrestraints between the selected models and the density map. The constructed models are
expected to be valuable to understand the ion-pumping mechanism of EhV-ATPase.
Acknowledgement
This study is partially supported by BINDS from AMED under grant number JP19am0101110(support number 1445).
References
1.

2.

3.

70

Iida T, Minagawa Y, Ueno H, Kawai F, Murata T, Iino R. Single-molecule analysis reveals
rotational subsets and chemo-mechanical coupling scheme of Enterococcus hirae V1-ATPase, J.
Biol. Chem. (2019), in press.
Tsunoda J, Song C, Imai F. L, Takagi J, Ueno H, Murata T, Iino R, Murata K, Off-axis rotor inEnterococcus hirae V1-ATPase visualized by Zernike phase plate single-particle cryo-electron
microscopy, Scientific Report. (2018) 8:1563.
Nakamura T., Oda T., Fukasawa Y., Tomii K., “Template-based quaternary structure predictionof proteins using enhanced profile-profile alignments”, PROTEINS, (2018) 86 1:274-282.

Finding a drug candidate regulating protein function at an allosteric site
Yuko Ito,* Yuji Tokunaga, Koh Takeuchi, Takatsugu Hirokawa
Molecular Profiling Research Center for Drug Discovery,
National Institute of Advanced Industrial Science and Technology, Japan*yuko.ito@aist.go.jp

Protein kinases are major drug targets for oncology. There has been an increasing interest in
non-ATP competitive kinase inhibitors, because they can offer higher selectivity andimproved biochemical efficiency. However, a framework for rational design to identify nonATP-competitive inhibitors has not been well established, because their inhibition
mechanisms are diverse, and in most cases their targeting pockets are not obvious in crystal
structures. Most of allosteric inhibitors thus far have been accidentally discovered. As such,rational design of allosteric inhibitor is critical need in drug development. Here, throughcomputational and empirical approaches, we designed allosteric inhibitors for the MAP kinase
ERK2 of which constitutive activation is found in many types of cancer cell. This work may
expand the possibility of the ERK2 inhibitors and also represent a shift from the conventional
paradigm of drug design.

71MD simulations and QM/MM analysis to gain insight into protein functions
Yoshitaka Moriwaki,1,* Tohru Terada,2,3 Kentaro Shimizu1,2
1

Department of Biotechnology, Graduate School of Agricultural and Life Sciences,
The University of Tokyo, Japan
2

3

Interfaculty Initiative in Information Studies, The University of Tokyo, Japan

Agricultural Bioinformatics Research Unit, Graduate School of Agricultural and Life Sciences,
The University of Tokyo, Japan*moriwaki@bi.a.u-tokyo.ac.jp

Molecular Dynamics (MD) simulations are highly useful to analyze time-resolved motions of
biological macromolecules such as proteins and internal water molecules at atomic resolution.
QM/MM is also a powerful computational method to study reactions in the macromoleculesthat are essential for their functions. Here, we introduce two case studies of proteins, PHBH
and LFS, using these computational methods to gain insight into their functions that are
intractable experimentally. PHBH is a flavoprotein monooxygenase that has been studied formore than 40 years [1]. We have found that the L199V/Y385F mutant can produce gallic acidbesides the original product, 3,4-dihydroxybenzoate, and elucidated the mechanism, which
involves a water-mediated hydrogen bond network and regiochemistry observed in 400-ns MD
simulations. The second topic is LFS, an enzyme of onion that produces lacrimatory factor.
Although the lacrimatory factor is very familiar with our daily life, the producing mechanism
from its substrate, (E)-1-propenesulfenic acid, has not been characterized until recently. Our
QM/MM calculations suggest that LFS catalyzes transfer of a proton from the sulfenic acid of
the substrate to 2-carbon with the help of Glu88, Tyr102, and Tyr114. This mechanism isdistinct from the other known LFSs, but consistent with the experimental data [2]. Taken
together, we have suggested that these calculation methods are also applicable to understandingof functional mechanism of drugs, which is of increasing significance for today.
References[1] Entsch B, Ballou DP, Massey V. Flavin-oxygen derivatives involved in hydroxylation by
p-hydroxybenzoate hydroxylase, J Biol Chem, 251, 2550–2563, 1976.
[2] Arakawa T, Sato Y, Takabe J, Masamura N, Kato M, Aoyagi M, Kamoi T, Tsuge N, ImaiS, Fushinobu S. Structure of Allium lachrymatory factor synthase elucidates catalysis on
sulfenic acid substrate, bioRxiv, DOI: 10.1101/142687

72

Improving the virtual screening ability using machine learning
Masakazu Sekijima*
Advanced Computational Drug Discovery Unit, Tokyo Institute of Technology, Japan*sekijima@c.titech.ac.jp

Virtual screening is a promising computational method for obtaining novel hit compounds indrug discovery. It aims to enrich potentially active compounds from a large chemical library forfurther biological experiments. However, the accuracy of current virtual screening methods is
insufficient.
Drug discovery requires to find molecules that interact with targets with high affinity andspecificity. Virtual screening application has been developed to this goal. However, current
methods still show relatively weak predictive power.
In this work, we proposed a new virtual screening method named Visual Inspection Network(VisINet) inspired by image classification. We will show how recent advances in computer
vision can be applied to structure-based virtual screening by adopting a CNN model based on
the ResNet architecture.
References[1] N. Yasuo and M. Sekijima, "Improved Method of Structure-Based Virtual Screening via
Interaction-Energy-Based Learning", Journal of Chemical Information and Modeling, 59(3), pp. 1050-1061, DOI: 10.1021/acs.jcim.8b00673 , 2019.

73

Combination of Molecular Dynamics Simulations
and Small-angle X-ray Scattering Experiments
Mitsunori Ikeguchi*
Yokohama City University, Graduate School of Medical Life Science, Japan*ike@yokohama-cu.ac.jp

Protein dynamics is crucial for protein functions. To investigate protein dynamics, molecular
dynamics (MD) simulation now becomes a powerful tool with the advance of computational
resources such as parallel computers and GPU. Among experiments of structural biology, thesmall-angle X-ray scattering (SAXS) is a unique experiment, because SAXS is capable ofproviding structural information in solution even for very flexible proteins. However, the SAXS
data are limited to low resolution. Therefore, the combination of MD simulation and SAXS,
called MD-SAXS, has been demonstrated to be useful for studies of protein dynamics insolution. In this talk, the recent applications of the MD-SAXS method as well as the recent
development of the hybrid method of coarse-grained MD and SAXS (CG-MD-SAXS method)
will be presented.
References[1] Oroguchi T, Hashimoto H, Shimizu T, Sato M, Ikeguchi M, Intrinsic dynamics ofrestriction endonuclease EcoO109I studied by molecular dynamics simulations and xray scattering data analysis. Biophys. J., 96, 2808-2822, 2009.
[2] Anami Y, Shimizu N, Ekimoto T, Egawa D, Itoh T, Ikeguchi M, Yamamoto K, Apoand antagonist-binding structures of vitamin D receptor ligand-binding domain revealed
by hybrid approach combining small-angle x-ray scattering and molecular dynamics, J.
Med. Chem. 59, 7888-7900, 2016.
[3] Ekimoto T, Kokabu Y, Oroguchi T, Ikeguchi M, Combination of coarse-grained molecular
dynamics simulations and small-angle X-ray scattering experiments, Biophys.
Physicobiol. in press.

74

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75

AHeDD2019/IPAB2019 Joint Symposium
Trends in Artificial Intelligence and Molecular Simulation
for Accelerating e-Drug Discovery
November 27 (Wed.) - 29 (Fri.), 2019
Tonomachi King Skyfront Area, Japan
Publisher Certified NPO Initiative for Parallel Bioinformatics (IPAB)
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