10月27日（水）Zoom ブレイクアウトルーム
口頭発表３・４＜口頭発表３＞『ADME・毒性／バイオインフォマティックス／分子ロボティクス／その他』 .......... 1
座長：夏目 やよい（医薬基盤・健康・栄養研究所）、川又 生吹（東北大学）、
池田 和由（理化学研究所/慶應義塾大学）
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Hideaki Mamada (Japan Tobacco Inc. / Meiji Pharmaceutical
University)"Novel QSAR approach for clearance prediction, combination
DeepSnap-Deep Learning, and conventional machine learning"
Satoko Namba (Department of Bioscience and Bioinformatics, Facultyof Computer Science and Systems Engineering, Kyushu Institute ofTechnology)"From drug repositioning to target repositioning: omics-based
prediction of therapeutic targets for a variety of diseases"
Zhaonan Zou (Dept Drug Discov Med, Grad Sch Med, Kyoto Univ)"Transcription factor binding profiling using chemically induced
genes by ChIPEA"
Tomokazu Shibata (Department of Bioscience and Bioinformatics,
Kyushu Institute of Technology)"Food digital transformation: large-scale prediction of food functions
and elucidation of the mode-of-action"
Xiaoran Hu (School of Computing, Tokyo Institute of Technology
Molecular Robot Research Institute, Co., Ltd.)"Construction of super-resolution DNA AFM images with VR DNA
molecular models"Taisei Mori (Tohoku University)"Simulating Self-replication of Linear Structures"
Chen Ma (School of Computing, Tokyo Institute of Technology)"Tracking microtubule groups with deep learning and optical flow"＜口頭発表４＞『データサイエンス／創薬応用／臨床応用』 ...................................... 8
座長：佐藤 朋広（横浜市立大学）、永堀 博久（住友化学(株)）
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Chen Li (Kyushu Institute of Technology)"Transformer-based Generative Adversarial Networks for Generating
Molecules with Desired Properties"
Haris Hasic (Department of Computer Science, School of Computing,Tokyo Institute of Technology/Elix Inc.)"RetroSynthWAVE: An Open-Source Software Platform for Efficient
Chemical Synthesis Research"
Romeo Cozac (Elix, Inc.)"Graph Convolutional Networks for Ligand-based Virtual Screening
against the Androgen Receptor"
David Jimenez (Elix, Inc.)"Leveraging Self-Supervised Contextual Language Models for Deep
Neural Network Antibody CDR-H3 Loop Predictions"
Pierre Wuthrich (Elix, Inc.)"Using Attribution-based Explainability to Guide Deep Molecular
Optimization"
Laurent Dillard (Elix, Inc.)"Improving Molecular Property Prediction using Self-supervised
Learning"
Sae Okamoto (Kyushu Institute of Technology)"Estimation of disease preventive drugs and therapeutic targets using
clinical big data"

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Novel QSAR approach for clearance prediction,
combination DeepSnap-Deep Learning, and conventional
machine learning
Hideaki Mamada 1, 2

Yukihiro Nomura 1

hideaki.mamada@jt.com

yukihiro.nomura@jt.com

Yoshihiro Uesawa 2uesawa@my-pharm.ac.jp

12Drug Metabolism and Pharmacokinetics Research Laboratories, Central Pharmaceutical
Research Institute, Japan Tobacco Inc., 1-1, Murasaki-cho, Takatsuki, Osaka 569-1125, Japan
Department of Medical Molecular Informatics, Meiji Pharmaceutical University, 2-522-1,
Noshio, Kiyose-shi, Tokyo 204-858, Japan

Keywords: Clearance (CL), Quantitative structure-activity relationships (QSAR), DeepSnap-DeepLearning (DeepSnap-DL), ensemble model, consensus model
In drug discovery, there are some prediction targets for which the prediction accuracy by machinelearning is not sufficient. Therefore, the development of new prediction models is required. In thisstudy, rat clearance (CL) was selected as a challenging target because of poor prediction [1], and a
new prediction model was developed. A classification model was constructed using 1545 in-housecompounds for which rat CL data are available. The molecular descriptors calculated by Molecular
Operating Environment (MOE), alvaDesc, and ADMET Predictor software were used to construct
the prediction model. Molecular descriptors and random forest selected by DataRobot were used
for conventional machine learning. The area under the curve (AUC) and accuracy (ACC) were0.883 and 0.825, respectively. Conversely, compound images and Deep Learning were used for
DeepSnap and Deep Learning (DeepSnap-DL) [2]. AUC and ACC were 0.905 and 0.832,
respectively. The two models (conventional machine learning and DeepSnap-DL) were combined
to develop a novel prediction model. The ensemble model using mean of the predictedprobabilities from each model improved the evaluation scores (AUC=0.943 and ACC=0.874).
Furthermore, using the results of the agreement between each classification as a consensus model
resulted in higher ACC (=0.959). These combination models with a high level of predictive
performance can be applied to rat CL as well as other pharmacokinetic parameters. These models
will help the design of more rational compounds in drug discovery.
[1] McIntyre, T. A.; Han, C.; Davis, C. B. Prediction of animal clearance using naïve Bayesianclassification and extended connectivity fingerprints, Xenobiotica, 2009, 39, 487–494.
https://doi.org/10.1080/00498250902926906.
[2] Uesawa, Y. Quantitative structure–activity relationship analysis using deep learning based on anovel molecular image input technique, Bioorganic & Medicinal Chemistry Letters, 2018, 28,
3400–3403.
https://doi.org/10.1016/j.bmcl.2018.08.032.

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From drug repositioning to target repositioning:
omics-based prediction of therapeutic targets
for a variety of diseases
Satoko Namba 1

namba.satoko775@mail.kyutech.jp

Michio Iwata 1

iwata121@bio.kyutech.ac.jp

Yoshihiro Yamanishi 1

yamani@bio.kyutech.ac.jp

1

Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems
Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan

Keywords: Therapeutic target, Drug discovery, Transcriptome, Gene knock-down, Gene
over-expression
The identification of therapeutic targets, biomolecules that lead to therapeutic effects, for treating
diseases is vital in drug development [1]. However, most therapeutic targets easily identified using
pathological data have been thoroughly investigated. The conventional methods for investigating
individual diseases are limited in their ability to discover novel therapeutic targets. Recently, there
has been an accumulation of omics data on various diseases. Thus, there is a need to identify novel
therapeutic targets by effectively using omics data resources about various diseases.
In this study, we proposed the novel concept of target repositioning, an extension of theconcept of drug repositioning, to predict new therapeutic target for a variety of diseases. We
developed a novel computational method using genetically perturbed and disease-specific gene
expression signatures. We predicted inhibitory and activatory therapeutic targets separately,
assuming that gene expression following gene knock-down of inhibitory targets reflects thefunctions of drugs that inhibit the targets, and gene expression following gene over-expression
reflects the functions of drugs that activate the targets. Based on the inverse correlations between
the disease-specific and genetically perturbed signatures, we predicted novel therapeutic targets,and performed an integrative analysis taking into account the similarities among the diseases. Our
results revealed that the proposed method accurately predicted known inhibitory and activatory
targets for diseases. We also made a comprehensive prediction of therapeutic targets for a variety
of diseases, suggesting many potential therapeutic targets.

[1] Santos, R. et al. A comprehensive map of molecular drug targets. Nat Rev Drug Discov. 16,
19-34 (2017).

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Transcription factor binding profiling using chemicallyinduced genes by ChIPEA

1

2

Zhaonan Zou 1

Michio Iwata 2

zou.zhaonan.56e@st.kyoto-u.ac.jp

iwata121@bio.kyutech.ac.jp

Yoshihiro Yamanishi 2

Shinya Oki 1

yamani@bio.kyutech.ac.jp

oki.shinya.3w@kyoto-u.ac.jp

Department of Drug Discovery Medicine, Kyoto University Graduate School of Medicine, 53
Shogoin Kawahara-cho, Sakyo-ku, Kyoto 606-8507, Japan
Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems
Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan

Keywords: Drug modes of action, Transcriptome, ChIP-seq, Transcription factor, Epigenetic
landscape
Modification of disease-elicited gene expression is one of the core aspects in numerous drugs’modes of action. To predict drug–disease associations, transcriptomics-based approaches withpathway analysis, graph theory and supervised machine learning-based calculation were developed.
However, the pharmacological mechanism employed by drugs remain largely unknown.
In this study, we focused on transcription factors (TFs) that integratively regulatedifferentially expressed genes (DEGs) in response to drug treatment. In particular, TF enrichment
analysis by analyzing large-scale ChIP-seq data obtained from ChIP-Atlas database (ChIPEA) was
performed for each chemical to identify TFs with enriched binding for chemically perturbed DEGs.
Performance evaluation with area under the ROC curve (AUC) suggests the reliability of ChIPEA
in drug target discovery (global AUC = 0.66). Furthermore, we successfully identified the pivotal
factors that link drugs to diseases or side effects by utilizing protein–disease database (global AUC= 0.68). This approach is with high confidence because it is fully based on actual experiments ofgiven transcriptome data and public ChIP-seq data. In the pharmaceutical field, ChIPEA is usefulto shed light on compounds failed to be approved by identifying TFs primarily involved in themodes of action, together with the factors associated with potential side effects. Approved drugs
including agents composed of unidentified ingredients such as traditional herbal medicines canalso be re-examined for novel targets and actions, thus beneficial to drug repositioning research.

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Food digital transformation: large-scale prediction of foodfunctions and elucidation of the mode-of-action
Tomokazu Shibata 1

Yusuke Tanaka 2

shiba535@bio.kyutech.ac.jp

Hiromu Taguchi 2
h-taguchi@housefoods.co.jp

yusuke-tanaka@housefoods.co.jp

Ryusuke Sawada 1
sawad330@bio.kyutech.ac.jp

Takashi Hirao 2

Yoshihiro Yamanishi 1

t-hirao@housefoods.co.jp
12

Morihiro Aoyagi 2
m-aoyagi@housefoods.co.jp

yamani@bio.kyutech.ac.jp

Department of Bioscience and Bioinformatics, Kyushu Institute of Technology,
680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
Research and Development Headquarters, House Foods Group Inc.,
1-4 Takanodai, Yotsukaido, Chiba 284-0033, Japan

Keywords: food function, constituent compound, target protein, big data, machine learning
The aging of the population in developed countries, including Japan, has led to the problem of
increasing medical costs. It is important to extend healthy life expectancy so that people can leadhealthy and cultured lives. Daily food intake is closely related to health, and it would be ideal if
we could maintain health through our daily diet. Since foods contain a wide variety of constituent
compounds, there is a high possibility that they have unknown food functions and health effects.
Even if food functions are known, it is very difficult to understand the mode-of-action.
In this study, we developed a machine learning method to comprehensively predict foodfunctions and the mode-of-action based on a vast amount of food-related data. In contrast to ourprevious study on the prediction of health effects of food peptides [1], the scope of this study is not
limited to peptides but covers all possible constituent compounds of foods. First, we collected
information on the chemical structures of 69,594 constituent compounds for 757 foods fromliterature and databases. Next, using 1,830,624 compound-protein interaction pairs (1,288,343
compounds and 4,643 proteins) as training data, we constructed machine learning models to
predict compound-protein interactions, and comprehensively predicted the proteins with whichfood constituent compounds interact. To estimate food functions, we linked the food constituent
compounds to applicable diseases based on known therapeutic targets of 649 diseases. The
correspondence between the therapeutic target proteins and the applicable diseases was manually
collected from literature data. Finally, we predicted a large-scale network consisting of four typesof nodes (foods, constituent compounds, target proteins, and applicable diseases), and elucidated
the mode-of-action of the predicted food functions. For each food, the functional associations
among target proteins were also examined at the pathway level. The proposed method is expected
to be useful not only for prediction of food functions but also for elucidation of themode-of-action.
[1] Fukunaga, I.; Sawada, R.; Shibata, T.; Kaitoh, K.; Sakai, Y.; Yamanishi, Y. Prediction of the
Health Effects of Food Peptides and Elucidation of the Mode-of-action Using Multi-task Graph
Convolutional Neural Network. Molecular informatics, 2020, 39, 1900134.

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Construction of super-resolution DNA AFM images
with VR DNA molecular models
Xiaoran Hu 1,3

Masayuki Yamamura 1

hu.x.ab@m.titech.ac.jp

my@cs.titech.ac.jp

Hirotada Kondo 2

Akinori Kuzuya 2,3

kondo@vraide.jp

kuzuya@kansai-u.ac.jp

Gutmann Gregory Spence 1,3

Akihiko Konagaya 3

ggutmann13@jcu.edu
1
2
3

konagaya@molecular-robot.com

School of Computing, Tokyo Institute of Technology
Dept. Chemistry and Materials Engineering, Kansai University
Molecular Robot Research Institute, Co., Ltd.

Keywords: Molecular Robotics, Super Resolution, Atomic Force Microscope Images, Deep
Learning
An Atomic Force Microscope (AFM) is a high-resolution instrument that can detect various materials
and samples in the atmosphere and liquid environment. It has become a basic tool in molecular robot
research especially for DNA nano-structural design such as DNA origami technology. However, due tothe limitation of AFM imaging resolution, it is difficult to ob serve the details of double-helix DNA
structure, such as major groove and minor groove. In order to solve the difficulty to obtainhigh-resolution DNA pictures directly, this research first tries to establish a VR molecular model
approach to obtain super resolution AFM images in atomic levels. Then, we use a virtual AFM probes
in various scales to scan the DNA molecular models and to obtain DNA AFM images of different
resolutions by simulating the AFM imaging process. Finally, the deep learning method is ap plied tobuild a super-resolution network to obtain high-resolution AFM images with different resolution DNA
images as training sets.

[1] Gregory Gutmann, Ryuzo Azuma, Akihiko Konagaya: A Virtual Reality Computational
Platform Dedicated for the Emergence of Global Dynamics in a Massive Swarm of Objects, J. of
the Imaging Society of Japan, 2018, 57(6), 647-653.

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Simulating Self-replication of Linear Structures
Taisei Mori 1

Ibuki Kawamata 1,2

taisei.mori.t6@dc.tohoku.ac.jp

ibuki.kawamata@tohoku.ac.jp

Satoshi Murata 1
satoshi.murata.a4@tohoku.ac.jp

12

Department of Robotics, School of Engineering, Tohoku University, 6 -6-01, AramakiAzaAoba,
Aoba, Sendai, Miyagi, 980-0845, Japan
Natural Science Division, Faculty of Core Research, Ochanomizu University, 2 -1-1 Ohtsuka,
Bunkyo-ku, Tokyo 112-8610, Japan

Keywords: Self-replication, Virtual Spring Model, Transition rule, Autocatalysis
Self-replication is the process by which a system creates ones identical to itself without external
operations from outside. A typical example of self-replication is that of living organisms, but its
process is extremely complicated involving so many chemical reactions that it is difficult to see
what is essential in the process. Simulation models of self-replication help us to find out what are
the essential conditions for a system to replicate itself [1-3]. In order to describe theself-replication process, we have proposed the Virtual Spring Model [ 4], in which the bonds
between the elements are regarded as spring-mass-damper systems. In this model, the statetransition of each element is used to represent the chemical reactions among them.
By using this model, we were able to represent the self -replication system consisting of up to
three interconnected elements. As for the self-replication procedure, the use of catalytic elements
increased the success rate of self-replication, but the problem was that the number of transition
rules was too large despite the simplicity of the system. In order to achieve scalable
self-replication independent of the size of the system, the rules need to be redesigned.
Here, we propose a new self-replication procedure inspired by the concept of "complementarity
of DNA", that only complementary base pairs (A and T, C and G) selectively bind to each other toform a double helix [5]. In DNA replication, an enzyme (polymerase) takes advantage of thisproperty to make a complementary copy of the strand. By representing the DNA replication
process with the use of autocatalytic procedure in the Virtual Spring Model, a scalable
self-replication system for linear structures can be realized. Under this framework, we present a setof transition rules and simulation results of self -replication of a strand (linear structure) formed byinterconnected elements with various sequences. We expect that the framework will be also usefulto establish self-replication systems of more complex structures and dynamic mechanisms.
[1] S. A. Kauffman, “Cellular Homeostasis, Epigenesis, and Replication in Randomly AggregatedMacromolecular Systems.” Journal of Cybernetics 1, pp.71-96, 1971.
[2] K. Tomita, H. Kurokawa, S. Murata, “Graph automata: natural expression ofself-reproduction.” Physica D 171, pp.197-210, 2002.
[3] Z. Zeravcic, M. P. Brenner, “Self-replicating colloidal clusters.” PNAS 111(5), pp.1748-1753,
2014.
[4] K. Fujibayashi, S. Murata, K. Sugawara, M. Yamamura, “Self-Organizing Formation
Algorithm for Active Elements.” IEEE, pp.416-421, 2002.
[5] J. D. WATSON, F. H. C. CRICK, “Molecular Structure of Nucleic Acids: A Structure for
Deoxyribose Nucleic Acid.” Nature 171, pp.737-738, 1953.

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Tracking microtubule groups with deep learning andoptical flow
Chen Ma 13

Masayuki Yamamura 1

ma.c.ab@m.titech.ac.jp

my@cs.titech.ac.jp

Mousumi Akter 2

Akira Kakugo 23

mousumi@sci.hokudai.ac.jp

kakugo@sci.hokudai.ac.jp

Gutmann Gregory Spence 13
ggutmann13@jcu.edu

1
2
3

Akihiko Konagaya 3

konagaya@molecular-robot.com

School of Computing, Tokyo Institute of Technology.
Faculty of Science, Hokkaido University.
Molecular Robot Research Institute, Co., Ltd.

Keywords: Molecular Robotics, Gliding Assay, Deep Learning, Optical Flow
Microtubules often from groups at high density, which are able to glide in the same directions on
Kinesin coated glass surface. At higher density of microtubules, they tend to move together (snuggling)
to avoid collision and overriding of microtubules. As a result, microtubule groups emerge motion
patterns showing straight, curved or wave like trajectories [1].
This research aims to analyze the condition of phase transition of the motion patterns of microtubules
by deep learning which will bring new advancements in the field of molecular robotics. As a first step,
we have developed an order parameter analysis workflow which consists of the U-Net like Fully
Convolutional Neural Network (FCN) for noise filtering, Sparse Optical Flow (SOF) for tracking and
SOF cluster for cluster matching among frames.
In this workflow, at first we trained the parameters using videos generated by the latest version of
microtubule gliding assay simulation system [2], and then applied them on real experimental video data
for evaluation. The workflow could drastically accelerate its performance by GPU with CUDA parallel
programming.
[1] Daisuke Inoue, Greg Gutmann, Takahiro Nitta, Takahiro Nitta, Arif Md. Rashedul Kabir, Akihiko
Konagaya, Kiyotaka Tokuraku, Kazuki Sada, Henry Hess, Akira Kakugo: Adaptation of Patterns ofMotile Filaments under Dynamic Boundary Conditions, ACS Nano, 2019, 13, 11, 12452–12460.
[2] Greg Gutmann, Daisuke Inoue, Akira Kakugo, Akihiko Konagaya: Parallel Interaction Detection
Algorithms for a Particle-based Live Controlled Real-time Microtubule Gliding Simulation System
Accelerated by GPGPU, Inter. J. of New Generation Computing (NGC), 2017, 35, 157–180.

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Transformer-based Generative Adversarial Networks for
Generating Molecules with Desired Properties
Chen Li 1

Kazuma Kaitoh 1

li260@bio.kyutech.ac.jp

kaito168@bio.kyutech.ac.jp

Yoshihiro Yamanishi 1
yamani@bio.kyutech.ac.jp

1

Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka, 820-8502, Japan

Keywords: Transformer, Generative adversarial network, Policy gradient, Molecular generation
Molecules can be represented by string-based sequences derived from molecular graphs, called thesimplified molecular-input line-entry system (SMILES). Generative adversarial networks (GAN)
with SMILES strings [1] have attracted widespread attention in generating molecules in drug
discovery. Most models apply recurrent neural networks (RNNs) as the generator for the molecular
generation with SMILES strings. However, RNNs are difficult to generate molecules with complex
rings. In general, highly cyclic molecules have long sequence representations and more strict
syntax than acyclic molecules. Slight changes in the syntax may result in the generation ofmolecules with totally different chemical property, or invalid molecules. Furthermore, RNNs
cannot work on GPU versions because the current iteration must compute after the previous time
step, which is not conducive to handle big data to explore infinite chemical space.
To overcome the above drawbacks, we propose a transformer-based objective-reinforced GAN
model in this study. The model consists of two main parts: generator and discriminator. The
generator is a generative model that tries to generate realistic fake data, and it is a transformer
architecture with several stacked encoders and decoders. The discriminator is treated as a binaryclassifier, attempting to distinguish the generated data to avoid being fooled by the generator. The
discriminator is based on a convolutional neural network (CNN), which is composed of a
convolutional layer, a max-pooling layer, and a highway layer. Note that the generator anddiscriminator train in alternation. In addition, a reinforcement learning approach called the Monte
Carlo policy gradient (MCPG) [2] is applied. While ensuring that the discriminator effectively
guides the training of the generator, it also takes the desired chemical properties into account togenerate desired molecules. Concretely, the discriminator first outputs the probability that thecurrent input sequence is from the original SMILES dataset. Then, it calculates the chemical
properties of the current input sequence, such as drug-likeness and solubility. Finally, the sum of
the probability and the properties are used as a reward for MCPG. In experiments, we test our
proposed method on molecular generation from the ZINC chemical dataset, and demonstrate theusefulness of our method in terms of uniqueness, novelty, and diversity in generating molecules.
[1] Guimaraes, G. L.; Sanchez-Lengeling, B.; Outeiral, C.; Farias, P. L. C.; Aspuru-Guzik, A.
Objective-reinforced generative adversarial networks (ORGAN) for sequence generation models,arXiv preprint arXiv:1705.10843, 2017.
[2] Sutton, R.S.; McAllester, D.A.; Singh, S.P.; Mansour, Y. Policy gradient methods forreinforcement learning with function approximation, Proc. In Advances in Neural Information
Processing Systems 12, 1057-1063, 1999.

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RetroSynthWAVE: An Open-Source Software Platform for
Efficient Chemical Synthesis Research

12Haris Hasic 1, 2

Takashi Ishida 1

hasic@cb.cs.titech.ac.jp

ishida@c.titech.ac.jp

Department of Computer Science, School of Computing, Tokyo Institute of Technology, W8 -85,
2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550, Japan
Elix Inc., Daini Togo Park Building 3F, 8-34 Yonbancho, Chiyoda-ku, Tokyo 102-0081, Japan

Keywords: Machine Learning – AI Method Development, Data Curation, Data Visualization
The RetroSynthWAVE project aims to establish a systematic, open -source software platform
focused on the field of chemical synthesis. It enables quick and efficient research for beginners aswell as advanced users by providing software packages that cover the foll owing synthesis-related
functionalities: Widgets and helpers, Aggregated chemical compound and chemical reaction data,
Variety of popular existing model implementations, and Evaluation metrics. Each of the software
packages can be used independently, as well as within the project software stack.
RetroSynthWAVE: HANA is the first and fundamental software package of the project. The nameis derived as an acronym for Helpers ANd Accessories, and it represents a utility wrapper modulethat encapsulates all of the essential libraries (e.g., RDKit [1], RDChiral [2]) and offers additional
fundamental functionalities while being easy to use.
RetroSynthWAVE: COCORO is the second software package of the project which is developed
using the functionalities from the previous one. The name is derived as an acronym for COllectionof Chemical COmpound and ReactiOn Data, and it represents an easy-to-use data platform that
automates the retrieval, cleaning and featurization of available chemical information datasets. (e.g.,
ChEMBL [3], USPTO [4])RetroSynthWAVE: CO-OP is the third software package of the project which is developed using
the functionalities from the previous two. The name is derived as an acronym for COllection Of
Popular Models, and it represents an easy-to-use model platform for the reimplementation of
popular existing (retro)synthesis models using the PyTorch library. It enables other users to submit
the implementation of new models in a standardized fashion.
RetroSynthWAVE: REFEREE is the final software package of the project. The name is derived as
an acronym for Retroactive EFficiency MEtRics Evaluation FramEwork, and it represents a utility
module that enables the definition of user-defined, pre-existing, and novel evaluation metrics for(retro)synthesis-focused models. Furthermore, by using the functionalities from all of the previous
software packages, it enables the retroactive application of new metrics on pre-existing models
thus enabling more advanced benchmarking of all relevant models.
[1] RDKit: Open-Source Cheminformatics Software. https://www.rdkit.org/. (2021.08.13)[2] Coley, C.W.; Green, W.H.; Jensen, K.F.; RDChiral: An RDKit Wrapper for Handling
Stereochemistry in Retrosynthetic Template Extraction and Application, Journal of Chemical
Information and Modeling, 2019, 59, 6, 2529-2537.
[3] ChEMBL Database - EMBL-EBI: https://www.ebi.ac.uk/chembl/. (2021.08.13)[4] Lowe, D. M. Extraction of Chemical Structures and Reactions from the Literature. Ph.D. Thesis,
University of Cambridge, Department of Chemistry, Pembroke College, 2012 .

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Graph Convolutional Networks for Ligand-based Virtual Screening against theAndrogen Receptor
Romeo Cozac1
romeo.cozac@elix-inc.com

1

Nazim Medzhidov1

Casey Galvin1

nazim.medzhidov@elix-inc.com

casey.galvin@elix-inc.com

Shinya Yuki1
shinya.yuki@elix-in.com

Elix Inc., Daini Togo Park Building 3F, 8-34 Yonbancho, Chiyoda-ku, Tokyo 102-0081 Japan

Keywords: Androgen Receptor, Machine learning, Graph convolutional Networks, drug repurposing
Androgen receptor (AR) is a ligand-dependent transcription factor that belongs to the family of steroid hormone nuclear receptors.
Androgens bind to the ligand binding domain of the AR with strong affinity and are capable of regulating transcription of AR-regulated
genes. AR signaling has implications in pancreatic cancer as well as tumors in the lungs, kidney, liver, and bladder. The standard treatment
approach for patients with prostate cancer is to lower testosterone levels in the body, however, this does not always prove effective sincesome patients do not respond to this form of treatment. Therefore alternative treatment options are necessary. Small molecule antagoniststhat interfere with androgens binding to AR have been under active investigation. In this study we utilize a graph based Machine Learningmodel to identify small molecule AR antagonists. In our approach, we design a flexible architecture that supports different graphconvolutional layers. We used Bayesian optimization to find the best-performing graph kernel and hyperparameters, and we applied MC
Dropout to measure the variance and confidence of the predicted values. The trained model was used to screen three datasets ofcommercially available compounds: the ZINC dataset, the eMolecules dataset, and a list of FDA-approved drugs. Using a high confidence
threshold for the predicted activity and confidence, we reduced the original set of 364K molecules to 55 hits which were not present in our
training set. Following a patent search on the 55 hits, we noticed that 36% had at least one relevant patent describing high activity against
the Androgen Receptor, proving that graph-based predictive models can be efficient tools for virtual screening.

[1]Chothia C, Lesk AM, Tramontano A, Levitt M, Smith-Gill SJ, Air G, Sheriff S, Padlan EA, Davies D, Tulip WR, et al. Conformationsof immunoglobulin hypervariable regions. Nature. 1989 Dec 21-28;342(6252):877-83. doi: 10.1038/342877a0. PMID: 2687698.
[2]Jeffrey A Ruffolo, Carlos Guerra, Sai Pooja Mahajan, Jeremias Sulam, Jeffrey J Gray, Geometric potentials from deep learning
improve prediction of CDR H3 loop structures, Bioinformatics, Volume 36, Issue Supplement_1, July 2020, Pages i268–i275[3] Alexander Rives, Siddharth Goyal, Joshua Meier, Demi Guo, Myle Ott, C. LawrenceZitnick, Jerry Ma, Rob Fergus, Biological
Structure and Function Emerge from Scaling Unsupervised Learning to 250 Million Protein Sequences, Proceedings of the National
Academy of Sciences doi: 10.1073/pnas.2016239118

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Leveraging Self-Supervised Contextual Language Models for Deep Neural Network
Antibody CDR-H3 Loop Predictions
David Jimenez

1

Nazim Medzhidov

1

david.jimenez@elix-inc.com

1

nazim.medzhidov@elix-inc.com

Elix Inc., Daini Togo Park Building 3F, 8-34 Yonbancho, Chiyoda-ku, Tokyo 102-0081 Japan

Keywords: Protein modeling, Unsupervised Learning, Antibody Structure Prediction, Transformers, Self-Supervision
Immunoglobulins take a structural conformation that is conserved in most parts, except for the antigen-binding fragment (Fab) that
includes six complementarity-determining regions (CDRs). These CDRs are peptide loops on both antibody heavy and light chains that
impact antigen recognition. Therefore accurate prediction of CDR structures from amino acid sequences is of great importance inantibody design. While existing methods are capable of predicting folding of five of these CDRs [1], determining the structure of the
CDR H3 loop remains a challenge. This is due to the complex diversity in observed folding conformations in CDR H3 loop compared tothe canonical folds in other five CDR loops. Deep neural networks strive at capturing complex patterns, which makes them a promising
tool for protein structure prediction. However, the domain of antibody structure prediction has relatively scarce annotated data comparedto general proteins, which usually limits the depth and complexity of the models that can be trained. To bypass this limitation, we
propose to use a contextual language model trained unsupervised on a large general protein dataset using a proxy task, which is thenjoined with a H3-Loop predicting model. Here, the language model spans a representation space reflecting protein biochemical
knowledge, which is exploited by the H3-Loop model for the antibody structure prediction. This results in a deeper and more expressivemodel that outperforms the prediction capabilities of the H3-Loop model alone.

[1]Chothia C, Lesk AM, Tramontano A, Levitt M, Smith-Gill SJ, Air G, Sheriff S, Padlan EA, Davies D, Tulip WR, et al.
Conformations of immunoglobulin hypervariable regions. Nature. 1989 Dec 21-28;342(6252):877-83. doi: 10.1038/342877a0.
PMID: 2687698.
[2]Jeffrey A Ruffolo, Carlos Guerra, Sai Pooja Mahajan, Jeremias Sulam, Jeffrey J Gray, Geometric potentials from deep learning
improve prediction of CDR H3 loop structures, Bioinformatics, Volume 36, Issue Supplement_1, July 2020, Pages i268–i275[3] Alexander Rives, Siddharth Goyal, Joshua Meier, Demi Guo, Myle Ott, C. LawrenceZitnick, Jerry Ma, Rob Fergus, Biological
Structure and Function Emerge from Scaling Unsupervised Learning to 250 Million Protein Sequences, Proceedings of the National
Academy of Sciences doi: 10.1073/pnas.2016239118

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O4-5

Using Attribution-based Explainability to Guide Deep
Molecular Optimization
Pierre Wüthrich1

Jun Jin Choong1
junjin.choong@elix-inc.com

pierre.wuthrich@elix-inc.com

1

Elix Inc., Daini Togo Park Building 3F, 8-34 Yonbancho, Chiyoda-ku, Tokyo 102-0081 Japan

Keywords: Molecular Optimization, Genetic Algorithms, Interpretability, Graph Neural Networks
De novo molecular design is an optimization task where the objective is to find candidate molecules with desired
properties. This task is however challenging given the size of the drug-like chemical space. The recently proposed
Genetic expert guided learning (GEGL) [1] framework has demonstrated impressive performances on several
de novo molecular design tasks. Despite the displayed state-of-the art results, the proposed system relies on an
expert-designed Genetic expert. Although hand-crafted experts allow to navigate the chemical space
efficiently, designing such experts requires a significant amount of effort and might contain inherent biases
which can potentially slow down convergence or even lead to suboptimal solutions. In this research, we
propose a novel genetic expert which is free of design rules and can generate new molecules by combining
extracted molecular fragments. Fragments are obtained by using an additional graph convolutional neuralnetwork [2] which computes attributions [3] for each atom for a given molecule. Molecular substructures
which contribute positively to the task score are kept and combined to propose novel molecules. We
experimentally demonstrate that our attribution-based genetic expert is competitive on most tasks and evenoutperforms the previous state-of-the-art expert-designed genetic expert [4] when evaluating proposed
candidate molecules is limited. Furthermore, we empirically show that combining several experts that share a
fixed sampling budget at each optimization round either improves or maintains the overall performance of theframework.

[1] Ahn, S.-S.; Kim, J.; Lee, H.; Shin, J. Guiding Deep Molecular Optimization with Genetic Exploration.
Advances in Neural Information Processing Systems 2020, 33, 12008–12021.
[2] Kipf, T. N.; Welling, M. Semi-Supervised Classification with Graph Convolutional Networks. Proceedingson the 5th International Conference on Learning Representations. 2017.
[3] Zhou, B.; Khosla, A.; Lapedriza, A.; Oliva, A.; Torralba, A. Learning Deep Features for Discriminative
Localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016; pp
2921–2929.
[4] Jensen, J. H. A Graph-Based Genetic Algorithm and Generative Model/Monte Carlo Tree Search for the
Exploration of Chemical Space. Chemical Science 2019, 10, 3567–3572.

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O4-6

Improving Molecular Property Prediction using Selfsupervised Learning
Laurent Dillard1
laurent.dillard@elix-inc.com

1

Elix Inc., Daini Togo Park Building 3F, 8-34 Yonbancho, Chiyoda-ku, Tokyo 102-0081 Japan

Keywords: Self-supervised learning, transfer learning, molecular property prediction, graph neuralnetworks,
Fast computation of molecular properties holds great potential to boost the efficiency of drug discovery
pipelines. In recent years, there has been a surge of interest in developing deep learning models for suchapplications. A popular choice of architecture to process molecular data are Graph Neural Networks (GNNs).
However, as with most deep learning models, training GNNs faces the challenge of gathering large
amounts of labeled data. Since labels mostly come from experimental results, the data collection process isboth time-consuming and costly. On the other hand, it is easy to access large databases of molecules making
it attractive for approaches that do not rely explicitly on labels. Self-supervised learning techniques leveragelarge amounts of unlabeled data to train models on pretext tasks for which labels can be generated from theraw data. These pretext tasks help the model learn to extract useful feature representations from the data andthe trained model can then be fine tuned on downstream tasks. Recently, self-supervised learning techniques
have been applied to a growing number of fields, including chemistry. In this work, we introduce a selfsupervised framework for GNNs tailored specifically for molecular property prediction. Our framework uses
three different pretext tasks, each focusing on a different scale of molecules (atoms, fragments and complete
molecules). For the atom and molecule level tasks, a predefined list of fragments is used to encode atom
contexts and molecule labels to train the model in a classification setting. For the fragment level task,
molecules are decomposed into several fragments and the model is trained to recognize which fragments
belong to the same molecule through a binary classification task. Using a subset of ZINC15 molecule
database[1] as the pretraining dataset, we evaluate the efficiency of our framework on the MoleculeNet[2]benchmark datasets as well as ADME datasets collected from the literature[3-6]. Our results show that selfsupervised learning can successfully improve performance compared to training from scratch, especially in
low data regimes. The improvement varies depending on the dataset and model architecture reaching up to+2.6% in area under the curve (AUC) for classification tasks and up to +7% in coefficient of determination(R2) for regression tasks.
[1] Shoichet Brian K Irwin John J. Zinc–a free database of commercially available compounds for virtual
screening. Journal of chemical information and modeling, 2005.
[2] Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S. Pappu,
Karl Leswing, and Vijay S. Pande. Moleculenet: A benchmark for molecular machine learning. CoRR,abs/
1703.00564, 2017.
[3] Dominique Douguet. Data sets representative of the structures and experimental properties of fdaapproved drugs.ACS Medicinal ChemistryLetters, 9(3):204–209, 2018.
[4] Shuangquan Wang, Huiyong Sun, Hui Liu, Dan Li, Youyong Li, and Tingjun Hou. Admet evaluation in
drug discovery. 16. predicting herg blockers by combining multiple pharmacophores and machine learning
approaches.Molecular Pharmaceutics, 13(8):2855–2866, 2016. PMID:27379394.
[5] Mark Wenlock and Nicholas Tomkinson. Experimental in vitro dmpk and physicochemical data on a setof publicly disclosed compounds.
[6] Youjun Xu, Ziwei Dai, Fangjin Chen, Shuaishi Gao, Jianfeng Pei, and Luhua Lai. Deep learning for druginduced liver injury. Journal of Chemical Information and Modeling, 55(10):2085–2093, 2015.
PMID:26437739.

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Estimation of disease preventive drugs and therapeutictargets using clinical big data
Sae Okamoto 1

Ryusuke Sawada 1

okamoto.sae404@mail.kyutech.jp

sawad330@bio.kyutech.ac.jp

Yoshihiro Yamanishi 1
yamani@bio.kyutech.ac.jp

1

Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems
Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka,
Fukuoka 820-8502, Japan

Keywords: preventive drugs, therapeutic targets, clinical big data, adverse event
Drug development is the most important issue for medical care. However, it is extremely difficultand it requires a huge amount of time and money. Especially, the depletion of therapeutic targets
has become a serious problem in recent drug discovery, and the conventional methods forinvestigating individual diseases are limited in their ability to discover novel therapeutic targets[1]. Recently, there has been an accumulation of clinical and molecular data on various diseases.
Thus, there is a strong need to identify novel therapeutic targets by effectively using various big
data resources about various diseases.
In this study, we propose a new computational method to predict therapeutic targets via
large-scale analyses of clinical big data on patients with various diseases. First, we estimate thepotential preventive drugs that are effective in preventing the onset of the target disease by
calculating the reporting odds ratio based on the reports of clinical medication history (more than
40 million reports on drug responses and adverse events). Second, we predict proteins, with which
the preventive drugs interact, as candidates for therapeutic targets of diseases of interest based on
chemical structures and chemical-protein interactome [2]. We applied the proposed method tovarious diseases, and evaluated its performance in terms of reproducibility for known therapeutictargets. It was observed that the proteins with high prediction scores tended to correspond to the
known therapeutic targets of many diseases at the statistically significant level. For example, in theapplication to Alzheimer's disease (AD), we confirmed that some of the predicted drugs werereported to be effective against AD in recent literature. We also confirmed that some of thepredicted target proteins corresponded to known therapeutic targets of AD. For example,
butyrylcholinesterase (BCHE) and acetylcholinesterase (ACHE) were detected with highprediction scores. These results show the validity of the proposed method. Other predicted target
proteins are expected to be the potential candidates for therapeutic targets.
[1] Santos, R. et al. A comprehensive map of molecular drug targets. Nat Rev Drug Discov. 16,
19-34 (2017).
[2] Sawada, Ryusuke et al. “Target-Based Drug Repositioning Using Large-Scale Chemical-Protein
Interactome Data.” Journal of chemical information and modeling vol. 55,12 (2015): 2717-30.

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