O4-1

Development of FMODB and Auto-FMO protocol
through 2022
Chiduru Watanabe 1
chiduru.watanabe@riken.jp

Daisuke Takaya 2
takaya-d@phs.osaka-u.ac.jp

Kikuko Kamisaka 1
kikuko.kamisaka@riken.jp

Teruki Honma 1
honma.teruki@riken.jp
12

RIKEN BDR, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
Osaka Univ., 2-8 Yamadaoka, Suita, Osaka 565-0871, Japan

Keywords: FMO, database, structure preparation, interaction energy analysis
Elucidating biomolecular interactions such as protein–ligand, protein–protein, and nucleic acidinteractions is essential for structure-based drug discovery. Our group has been focusing on quantum
mechanics (QM), which incorporates the effects of donating and withdrawing electrons and canappropriately deal with the CH/π, π-π, and cation/π interactions. Fragment molecular orbital (FMO)
method [1] enables us to perform ab initio QM calculations for large biomolecules efficiently. The
benefit of this fragmentation scheme is the availability of inter -fragment interaction energy (IFIE)
and pair interaction energy decomposition analysis (PIEDA).
Since 2014, we have performed the FMO calculations for various drug targets (e.g., kinase, nuclear
receptor, protease, protein-protein interaction, and COVID-19) as the activities of the FMO drug
design (FMODD) consortium. To accumulate the FMO data, we developed an FMO database(FMODB) and an automated FMO calculation (Auto-FMO) protocol [2,3]. First, FMODB has
already completed its basic design and is currently working on extending its analysis capabilities tomake it more user-friendly. While only individual entry data could be analyzed in the past, we areworking to enable IFIE/PIEDA analysis for a series of related data, such as tens to hundreds of FMO
calculations based on molecular dynamics snapshots [4 ], in a batch. In addition, a function togenerate an interaction energy diagram between fragments is under development. Next, the AutoFMO protocol has also been developed by BIOVIA Pipeline Pilot and MOE software as a primary
type. Currently, we are working on developing an Auto -FMO protocol in a configuration using freesoftware such as python and Amber Tools. The protocol was validated using X -ray crystal structure
data already registered with FMODB and AlphaFold2 model structures, which are in high demandthese days. The goal is to construct a system that allows computational experts and not e xperts, suchas experimental researchers, to perform FMO calculations and analyze IFIE/PIEDA efficiently.
Acknowledgement
The authors thanks Dr. Kazumi Tsuda, Mr. Daisuke Murayama, Dr. Shu Koyama, and Dr. Tomoharu
Isobe of Science & Technology Systems, Inc., Japan for technical support. This research was donein activities of FMO Drug Design Consortium, https://fmodd.jp/top-en/ . The results of FMO
calculations were obtained using the Fugaku (project IDs: hp220143 and ra000017) and HOKUSAI(ID: Q22306).
Reference
1. K. Kitaura et al., Chem. Phys. Lett., 1999, 313, 701–706.
2. C. Watanab et al., CBI J., 2019, 19, 5–18.
3. D. Takaya et al., J. Chem. Inf. Model., 2021, 61, 777–794.
4. K. Takaba et al., J. Comput. Chem., 2022, 43, 1362–1371.

O4-2

Hit Identification for SARS-CoV-2 Main Protease
Using Convolutional Neural Network
Nobuaki Yasuo 1

Hiroshi Yoda 2

yasuo.n.aa@m.titech.ac.jp_Masakazu Sekijima 2
sekijima@c.titech.ac.jp

12

Academy for Convergence of Materials and Informatics (TAC-MI), Tokyo Institute ofTechnology, S6-404, Ookayama 2-12-1, Meguro-ku, Tokyo, 152-8550, Japan
Department of Computer Science, Tokyo Institute of Technology, 4259-J3-23 Nagatsutacho,Midori-ku, Yokohama, Kanagawa, 226-8501, Japan

Keywords: Virtual screening, Ligand docking, Machine learning, SARS-CoV-2
Docking-based virtual screening is widely applied in drug discovery campaigns. Visual inspectionof binding modes is frequently used to filter out false positive-like docking results[1, 2]. In thisstudy, we propose a convolutional neural network-based virtual screening method named VisINet(Visual Inspection Network). VisINet predicts whether a ligand binds to the target protein by images
that is taken from docking pose using PyMOL. We also performed virtual screening to find hit
compounds of SARS-CoV-2 Main Protease.
[1] Fischer, A.; Smieško, M.; Sellner, M.; Lill, M. A. Decision Making in Structure-Based
Drug Discovery: Visual Inspection of Docking Results. Journal of Medicinal
Chemistry 2021 , 64 (5), 2489–2500.
[2] Singh, K.; Coopoosamy, R. M.; Gumede, N. J.; Sabiu, S. Computational Insights andin Vitro Validation of Antibacterial Potential of Shikimate Pathway-Derived Phenolic
Acids as Nora Efflux Pump Inhibitors. Molecules 2022 , 27 (8), 2601.

O4-3
Evaluating Computer-assisted Single-step Retrosynthesis:
How Significant are Recent Improvements?

12Haris Hasic 1,2

Takahiro Inoue 1

haris.hasic@elix-inc.com

takahiro.inoue@elix-inc.com

Tatsuya Okubo 1

Takashi Ishida 2

tatsuya.okubo@elix-inc.com

ishida@c.titech.ac.jpElix Inc., Daini Togo Park Building 3F, 8-34 Yonbancho, Chiyoda-ku, Tokyo 102-0081, Japan
Department of Computer Science, School of Computing, Tokyo Institute of Technology, W885, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8552, Japan

Keywords: Machine Learning – AI Method Development, Synthetic Pathway Prediction
Computer-assisted synthesis grew to become one of the most active fields in chemoinformatics,
with scientists all over the globe frequently introducing new and improved approaches. In recentyears, the research focus shifted towards the retrosynthesis tech nique concepts of transforming thetarget compound into less complex precursor compounds. The improvement of the single -step
retrosynthesis mechanism (i.e., the disconnection suggestion procedure within the route planningstrategy) is among the most popular objectives. However, none of the recent improvements proved
to be a robust enough foundation to establish a practically applicable, standalone solution .
The current practices in modelling the problem of single -step retrosynthesis might conceal a hardceiling on performance, but it is impossible to know because of insufficient chemical reaction data.
Nevertheless, analyzing the current state-of-the-art more rigorously using available open-source
datasets could help to shed more light on that question . Thus, the purpose of this research is toconsolidate, analyze and discuss the significance of the recent frequent improvements in the fieldof computer-assisted single-step retrosynthesis.
First, the Top-3 template-based [1-3] and the Top-3 template-free [4-6] single-step retrosynthesis
approaches as of July 2022 are implemented and adjusted to customized open -source chemical
reaction datasets. Secondly, the performance of these approaches is analyzed using various metrics
and statistical significance tests. Finally, using the acquired results, the impact of each of theseapproaches is illustrated, thereby consolidating the best practices for the analysis and evaluation offuture research in this field.
[1] Dai, H.; Li, C.; Coley, C. W.; Dai, B. W.; Song, L. Retrosynthesis Prediction with Conditional
Graph Logic Network, Advances in Neural Information Processing Systems , 2019, 32.
[2] Somnath, V. R.; Bunne, C.; Coley, C. W.; Krause, A. W.; Barzilay, R. Learning Graph Modelsfor Retrosynthesis Prediction, Advances in Neural Information Processing Systems , 2021, 34,9405-9415.
[3] Chen, S.; Jung, Y. Deep Retrosynthetic Reaction Prediction using Local Reactivity and GlobalAttention, JACS Au, 2021, 1, 10, 1612-1620.
[4] Tetko, I. V.; Karpov, P.; Van Deursen, R.; Godin, G. State-of-the-Art Augmented NLP
Transformer Models for Direct and Single-step Retrosynthesis, Nature Communications, 2020,11, 5575-5586.
[5] Ucak, U. V.; Ashyrmamatov, I.; Ko, J.; Lee, J. Retrosynthetic Reaction Pathway Prediction
through Neural Machine Translation of Atomic Environments , Nature Communications, 2022, 13,1186-1196.
[6] Zhong, Z.; et al. Root-aligned SMILES: A Tight Representation for Chemical Reaction
Prediction, arXiv:2203.11444v4 [cs.LG], 2022.

O4-4

Developing a network-based combination therapy
approach for complex diseases
Midori Iida 1
redgreen@bio.kyutech.ac.jp

Mitsuhiro Goda 2,4
mgoda@tokushima-u.ac.jp

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

Yurika Kuniki 2c401703080@tokushima-u.ac.jp

Satoko Namba 1

345

6

Jun-ichi Takeshita 5
jun-takeshita@aist.go.jp

Michio Iwata 1

Yoshito Zamami 2,6

iwata121@bio.kyutech.ac.jp

zamami-y@okayama-u.ac.jp

Keisuke Ishizawa 2,3,4

2yagi.kenta@tokushima-u.ac.jp

namba.satoko775@mail.kyutech.jp

ishizawa@tokushima-u.ac.jp

1

Kenta Yagi 3

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

Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan
Department of Clinical Pharmacology and Therapeutics, Tokushima University Graduate
School of Biomedical Sciences, Kuramoto-cho, Tokushima 770-8503, Japan
Clinical Research Center for Developmental Therapeutics, Tokushima University Hospital,Kuramoto-cho, Tokushima 770-8503, Japan
Department of Pharmacy, Tokushima University Hospital, Kuramoto-cho, Tokushima 7708503, Japan
Research Institute of Science for Safety and Sustainability, National Institute of Advanced
Industrial Science and Technology (AIST), Tsukuba, Ibaraki 305-8569, Japan
Department of Pharmacy, Okayama University Hospital, Kita-ku, Okayama 700-8558, Japan

Keywords: Combination therapy, Cancer, Network medicine, Interactome, Transcriptome
Combination therapy, a treatment modality that combines two or more therapeutic drugs, has
become the standard treatment for complex diseases—however, the discovery of drug combinations
relies on serendipity by clinical experience and intuition [1]. A network-based approach allows us
to discover efficient drug combination by quantifying the network-based relationship between drug
targets and disease proteins in the human protein–protein interactome [2]. However, conventional
methods do not take into account changes in gene expression due to drug administration, resulting
in low prediction accuracy. In this study we developed a network-based method to predictsynergistic drug effects by integrating interactome and transcriptome data. The proposed method
outperformed the previous methods in terms of higher prediction accuracy for several cancers. To
validate the prediction results by our proposed method, we carried out in vitro cell surviving assays.
The results showed that more than half of the top 20 predicted drug pairs had synergistic effects on
the cell survival. Pathway enrichment analysis demonstrated several biological pathways areenriched with the genes used for prediction, suggesting that those pathways contribute to thesynergistic effect. This approach allows us to identify drug combinations for complex diseases,offering a powerful methodology to find efficacious combination therapies.
[1] Prasad, S.; Gupta, S. C.; Aggarwal, B. B. Serendipity in Cancer Drug Discovery: Rational orCoincidence? Trends in Pharmacological Sciences, 2016, 37 (6), 435–450.
[2] Cheng, F.; Kovács, I. A.; Barabási, A. L. Network-Based Prediction of Drug Combinations.
Nature Communications, 2019, 10 (1).

O4-5

Prediction of Protein Binding Region on RNA withTransformer and Attention Augmentation
Takayuki Kimura 1
kimura.t.bf@m.titech.ac.jp

Nobuaki Yasuo 2

yasuo@cbi.titech.ac.jp

Masakazu Sekijima 1
sekijima@c.titech.ac.jp
12

Department of Computer Science, Tokyo Institute of Technology, Tokyo, 152-8550, Japan
Academy for Convergence of Materials and Informatics, Tokyo Institute of Technology, Tokyo,152-8550, Japan

Keywords: RNA-protein binding prediction, Transformer, Statistical potential
RNA-protein interaction is important in post-transcriptional regulations [1]. However, the sizeof public databases is limited [2], and the cost of experimental approaches [3] is not low. Therefore,
computational prediction is necessary that predicts RNA -protein binding. The computationalmodel's evolution is nevertheless insufficient [4]. For example, accurate prediction models forprotein binding regions in RNA have not been reported yet . In this study, we developed aTransformer model [5] that predicts a protein binding region (101 nt). We adopted cross -attention
module architecture [6] and used statistical potential [7] as prior knowledge to augment the attention
weights [8]. The cross-attention module can generate feature vectors from the perspectives of both
the RNA and the protein. The test AUROC was higher than a baseline model of bidirectional -LSTM
and existing models. We also confirmed the effect of attention augmentation with stat isticalpotential on the binding prediction.
[1] Hentze MW.; Castello A.; Schwarzl T.; Preiss T, A brave new world of RNA-binding proteins,
Nature reviews Molecular cell biology , 2018, 19, 327-41.
[2] ENCODE Project Consortium, An integrated encyclopedia of DNA elements in the humangenome, Nature, 2012, 489, 57.
[3] Van Nostrand, E.L.; Pratt, G.A.; Shishkin, A.A.; Gelboin -Burkhart, C.; Fang, M.Y.;
Sundararaman, B.; Blue, S.M.; Nguyen, T.B.; Surka, C.; Elkins, K. and Stanton, R , Robusttranscriptome-wide discovery of RNA-binding protein binding sites with enhanced CLIP (eCLIP),
Nature methods, 2016, 13, 508-514.
[4] Wei J.; Chen S.; Zong L.; Gao X.; Li Y, Protein–RNA interaction prediction with deep learning:
structure matters, Briefings in bioinformatics, 2022, 23, bbab540.
[5] Vaswani A.; Shazeer N.; Parmar N.; Uszkoreit J.; Jones L.; Gomez AN.; Kaiser Ł.; Polosukhin
I, Attention is all you need, Advances in neural information processing systems , 2017, 30.
[6] Koyama K.; Kamiya K.; Shimada K, Cross attention DTI: Drug-target interaction prediction
with cross attention module in the blind evaluation setup, 19th International Workshop on Data
Mining in Bioinformatics, 2020, BIOKDD, San Diego[7] Kimura T.; Yasuo N.; Sekijima M.; Lustig B, Statistical potentials for RNA-protein interactions
optimized by CMA-ES, Journal of Molecular Graphics and Modelling , 2022, 110, 108044.
[8] Maziarka Ł.; Danel T.; Mucha S.; Rataj K.; Tabor J.; Jastrzębski S, Molecule attention
transformer, arXiv preprint arXiv, 2020, 2002.08264.

O4-6

Molecular Generation using Sequence-based Transformer
Generative Adversarial Network1

Chen Li 1

Yoshihiro Yamanishi 1

li260@bio.kyutech.ac.jp

yamani@bio.kyutech.ac.jp

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: Transformer, Deep generative model, Reinforcement learning, Molecular generation
Generating molecules with the desired chemical properties from scratch is a challenging task in denovo drug design. Deep generative models such as variational autoencoder (VAE) and generative
adversarial network (GAN) have been receiving much attention in recent years. In deep generativemodels, molecules are usually represented as molecular graphs and simplified molecular-input
line-entry system (SMILES) strings. In previous work, a Transformer-based objective-reinforced
GAN (TransORGAN) [1] was proposed to generate molecules with desired chemical properties
using SMILES strings. The generator of TransORGAN is a transformer architecture, which uses aself-attention mechanism to capture features in sequences. Self-attention has a global receptive
field that can capture long-range dependencies. Since TransORGAN uses the SMILES strings fromthe training set as a condition rather than generating molecules starting from noise or scratch, themolecular generation is restricted. Furthermore, the instability of TransORGAN leads to a highvariance in the distribution of the chemical properties of the generated molecules, which is an
obstacle in practice.
In this study, we propose a pure transformer encoder-based GAN for generating moleculeswith desired properties to solve the above problems. The generator and discriminator of theproposed model are variants of the transformer encoders, where atoms can access each other at
each encoder layer to capture complex semantic and syntactic rules of SMILES strings.
Furthermore, we implement an enhanced algorithm incorporating the mini-batch discrimination [2]and Wasserstein GAN [3] to mitigate the mode collapse problem. In addition, we present variantSMILES, a data augmentation to sufficiently train the generator in the training phase. Finally, weuse Monte Carlo policy gradient reinforcement learning [4] to improve the desired chemical
properties of generated molecules. To our knowledge, we are the first to produce molecules withchemical properties from SMILES strings using only transformer encoders. The experimental
results demonstrate the usefulness of the proposed models for generating molecules with desired
properties such as drug-likeness. Ablation studies validate the effectiveness of our proposedtechniques. The proposed method is expected to be useful for de novo drug design.
[1] C. Li; C. Yamanaka; K. Kaitoh; Y. Yamanishi. Transformer-based objective-reinforced
generative adversarial network to generate desired molecules, Proc. In the 31th International
Joint Conference on Artificial Intelligence (IJCAI), 3884-3890, 2022.
[2] T. Salimans; I. Goodfellow; W. Zaremba; V. Cheung; A.Radford; X.Chen. Improved techniques
for training gans. Advances in neural information processing systems, 29:2234–2242, 2016.
[3] I. Gulrajani; F. Ahmed; M. Arjovsky; V. Dumoulin; A. Courville. Improved training ofwasserstein gans. arXiv preprint arXiv:1704.00028, 2017.
[4] R.S. Sutton; D.A. McAllester; S.P. Singh; Y. Mansour. Policy gradient methods forreinforcement learning with function approximation, Proc. In Advances in Neural Information
Processing SystemsAdv Neural Inf Process Syst. 12, 1057-1063, 1999.