O5-1

De novo generation of drug candidate compounds from
disease-specific transcriptome data using deep learning
Chikashige Yamanaka 1

Shunya Uki 1

yamanaka.chikashige215@mail.kyutech.jp

uki.shunya677@mail.kyutech.jp

1

Kazuma Kaitoh 1

Yoshihiro Yamanishi 1

kaito168@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,
JapanKeywords: De novo drug design, gene expression profile, inverse correlation, variationalautoencoder
It is extremely difficult to find molecules with desired properties in the drug discovery process. Totackle this problem, deep generative models, including variational autoencoder (VAE), have been
receiving much attention. Most previous methods are based on chemical information, but they do
not take into account biological information on genes and proteins. In this context, some previousstudies have proposed deep generative models that generate active molecules for therapeutic target
proteins using biological data such as gene expression profiles [1,2]; however, some limitations
remain, such as difficulty in applying their models to diseases with unknown therapeutic targets. In
a disease state, the disease-specific gene expression patterns typically emerge because of thedisruption of homeostasis, and the disease can be treated by a drug molecule that invert the diseasespecific gene expression patterns. Actually, the use of an inverse correlation between diseases anddrugs is popular in drug repositioning [3,4], but the number of predictable candidate drugs is limited.
In this study, we propose to utilize the disease-specific gene expression profiles of patients
toward the de novo design of drug candidate molecular structures. We present a computationalmodel based on VAE with a transformer architecture to generate molecules that invert the diseasespecific gene expression patterns. In the algorithm, we explore the latent space constructed by theVAE model using the inverse correlation with disease-specific transcriptome profiles of patients.
We applied the model to several cancers and neurodegenerative diseases, and successfully generated
new drug candidate molecular structures with the potential therapeutic effects against each disease.
The proposed model can generate molecules with potential therapeutic effects against any diseasein theory, thus the model is expected to make a great contribution to precision medicine.
[1] Méndez-Lucio, O.; Baillif, B.; Clevert, D.-A.; Rouquié, D.; Wichard, J. De novo generation of
hit-like molecules from gene expression signatures using artificial intelligence, NatureCommunications, 2020, 11, 10.
[2] Kaitoh, K.; Yamanishi, Y. TRIOMPHE: transcriptome-based inference and generation ofmolecules with desired phenotypes by machine learning, Journal of Chemical Information andModeling, 2021, 61, 4303–4320.
[3] Iorio, F.; Bosotti, R.; Scacheri, E.; Bernardo, D. D. Discovery of drug mode of action and drug
repositioning from transcriptional responses, Biophysics and computational biology, 2010, 107,14621–14626.
[4] van Noort, V.; Schölch, S.; Iskar, M.; Zeller, G.; Ostertag, K.; Schweitzer, C.; Wenrner, K.;
Weitz, J.; Koch, M.; Bork, P. Novel drug candidates for the treatment of metastatic colorectal
cancer through global inverse gene-expression profiling, Cancer Research, 2014, 74, 5690–5699.

O5-2

Data-Driven Understanding of TRPA1 Agonist Diversity
Kenjiro Tanaka 1
tanaka.kenjiro.t8@s.mail.nagoya-u.ac.jp

Minami Matsuyama 2south59moco@gmail.com

Masatoshi Shibuya 1
m-shibu@ps.nagoya-u.ac.jp

Keisuke Ito 2
sukeito@u-shizuoka-ken.ac.jp

1

2

Yuko Terada 2
yukoterada@u-shizuoka-ken.ac.jp

Masaya Fujitani 1
masaya.fujitani.0917@gmail.com

Yoshihiko Yamamoto 1
yamamoto-yoshi@ps.nagoya-u.ac.jp

Ryuji Kato 1
kato-r@ps.nagoya-u.ac.jp

Department of Basic Medicinal Sciences, Graduate School of Pharmaceutical Sciences, Nagoya
University, Tokai National Higher Education and Research System, Furocho, Chikusa -ku,Nagoya, Aichi 464-8601, Japan
Department of Food and Nutritional Sciences, Graduate School of Integrated Pharmaceutical andNutritional Sciences, University of Shizuoka, 52 -1 Yada, Suruga-ku, Shizuoka 422-8526, Japan

Keywords: Transient receptor potential ankyrin 1, Antagonist structure, Physicochemical property
Transient receptor potential ankyrin 1 (TRPA1) is a Ca 2+ -permeable cation channel expressed insensory neurons and non-neuronal cells of different tissues. TRPA1 has been linked to variou s
physiological functions, including pain and pungency perception, energy metabolism, hormone
secretion, and vasodilation [1,2], therefore it has been served as one of the amusing target ofpharmaceutical targets. However, since TRPA1 function as a multimodal receptor, the structural
diversity of TRPA1 agonists has not been fully understood.
We hypothesized that collecting a wider variation of TRPA1 –compound interaction data would aid
the understanding of its complex mechanism . Therefore, we aimed to explore such agonist diversity
using an image-based TRPA1 assay system combined with an in silico chemical space clustering
concept. We clustered our originally synthesized chemical library with 27 physicochemicalmolecular descriptors in silico, and selected structurally diverse compounds from each cluster for a
detailed kinetic assay to investigate variations of agonist structural rules. Through two sets of assays
evaluating various compounds in parallel with validating effects of the previously establishedstructural rules, we discovered that different chemical groups contribute to agonist activity,indicating that there are multiple agonist design concepts. A novel core structure for a TRPA1agonist has been also proposed.
Our data-driven approach to analyze the agonist diversity with physicochemical property rules
facilitated the objective finding of the structural diversity and new rules in TRPA1 agonists [3].
[1] Mahajan, N.; Khare, P.; Kondepudi, K.K.; Bishnoi, M. TRPA1: Pharmacology, natural activatorsand role in obesity prevention. European Journal of Pharmacology, 2021, 912, 174553.
[2] Wang, Z.; Ye, D.; Ye, J.; Wang, M.; Liu, J.; Jiang, H.; Xu, Y.; Zhang, J.; Chen, J.; Wan, J. The
TRPA1 channel in the cardiovascular system: Promising features and challenges. Frontiers inPharmacology, 2019, 10, 1253.
[3] Terada, Y.; Tanaka, K.; Matsuyama, M.; Fujitani, M.; Shibuya, M.; Yamamoto, Y.; Ito, K.; Kato,R. Collection of data variation using a high-throughput image-based assay platform facilitates
data-driven understanding of TRPA1 agonist diversity. Applied Sciences, 2022, 12, 1622.

O5-3

In Silico and In Vitro Screening for Inhibitors of SARSCoV-2 Main Protease Avoiding Peptidyl SecondaryAmides
Kazuki Yamamoto 1

kazuki@ric.u-tokyo.ac.jp

Nobuaki Yasuo 2

yasuo.n.aa@m.titech.ac.jp

Masakazu Sekijima 1

sekijima@c.titech.ac.jp

12

Department of Computer science, Tokyo Institute of Technology, Yokohama, 226-8501, Japan
Academy for Convergence of Materials and Informatics, Tokyo Institute of Technology, Tokyo,152-8550, Japan

Keywords: COVID-19, Protease inhibitor, Structure-based virtual screening
Vaccines and therapeutic agents are needed to control the damage caused by COVID-19 [1].
Currently available new drugs that inhibit SARS-CoV-2 main protease can suppress viral replicationin the early stages of infection, but they are not easy to use in clinical practice because they involve
inhibition of CYP3A4 [2-3]. Here, we performed structure-based virtual screening (SBVS) to findinhibitors with new scaffold structures, excluding compounds with peptidyl secondary amide bonds.
The 180 compounds selected by SBVS were subjected to the main protease inhibition assay, and 9compounds showed >5% inhibition at 20 µM. The IC50s of 6 of these compounds were determinedby dose–response experiments and were on the order of 10 -4 M. Based on these new scaffolds, wewill try to optimize them.
[1] Robinson, Philip C., et al. COVID-19 therapeutics: Challenges and directions for the future.
Proceedings of the National Academy of Sciences 119.15 (2022): e2119893119.
[2] Hammond, Jennifer, et al. "Oral nirmatrelvir for high-risk, nonhospitalized adults with Covid19." New England Journal of Medicine 386.15 (2022): 1397-1408.
[3] https://www.mhlw.go.jp/content/11121000/000966645.pdfO5-4
Active Learning via Incremental Revelation: Dipeptidyl Peptidase-4 Inhibitors CaseStudyDavid Jimenez Barrero

1

david.jimenez@elix-inc.com

Nazim Medzhidov

1

nazim.medzhidov@elix-inc.com

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

1

Keywords: Active Learning, Drug Discovery, Machine Learning, Sitagliptin
Active Learning (AL) is a machine learning algorithm consisting of a constantly-improving model which is capable of achieving better
accuracy with fewer labels than a regular model, as it is able to query the search-space and select expected promising samples[1]. This
proves particularly useful in spaces which are too large and/or expensive to evaluate exhaustively, making it a clear proposition for use in
the drug discovery context. Here the goal is to find a particular molecule with desired properties (high activity against a particular target,good synthesizability score, etc.) in a potentially infinite chemical space, and where the evaluation of each candidate is expensive
economically as well as time consuming. Despite AL being theoretically a good fit for drug discovery, tests in this domain have been
limited; due to the same economically-expensive nature of drug discovery. In this study, we designed a methodology that allows us to test
performance of AL in a realistic framework by replicating real drug discovery campaigns while simulating the conditions humanresearchers face, and thus addressing common validity problems, such as having a limited and bounded search-space, and positively
biased data. Under this scheme, we evaluated AL by replicating the discovery process of a dipeptidyl peptidase-4 (DPP4) inhibitorSitagliptin [2]. We obtained comparable results to the estimated human performance by using both a single IC50 activity model and anensemble of IC50 activity and lipophilicity models; thus proving the value of active learning in drug discovery, and the potential it has asa tool to aid medicinal chemists. We hope these landmark in-silico results pave the way for future tests of active learning using wet-labexperiments.

[1]Settles B. Active Learning Literature Survey. Computer Sciences Technical Report 1648. 2010 January 26; University ofWisconsin–Madison.
[2]Drucker, D., Easley, C. & Kirkpatrick, P. Sitagliptin. Nat Rev Drug Discov 6, 109–110 (2007).

O5-5

SmilesFormer: Language Model for Molecular Design
Joshua Owoyemi 1
joshua.owoyemi@elix-inc.com

1

Nazim Medzhidov 1
nazim.medzhidov@elix-inc.com

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

Keywords: De novo drug design, Language model, Molecule Optimization
The objective of drug discovery is to find novel compounds with desirable chemical properties.
Generative models have been utilized to sample molecules in the intersection of multiple property
constraints. In this study we pose molecular design as a language modelling problem where themodel implicitly learns the vocabulary and composition of valid molecules, hence it is able togenerate new molecules of interest. We present a SmilesFormer, Transformer-based model [1] which
is able to encode molecules, molecule fragments and fragment compositions as latent variables,
which is in turn decoded to stochastically generate novel molecules. This is achieved by fragmenting
the molecules into combinatorial groups, with methods such as RECAP [2], then learning themapping between the input fragments and valid SMILES sequences. The model is able to optimize
molecular properties through a stochastic latent space traversal technique [3]. This techniquesystematically searches the encoded latent space to find latent vectors that are able to produce
molecules to meet the multi-property objective. The model was validated through various de novo
molecular design tasks, achieving state-of-the-art performances when compared to previous
methods. Furthermore, we used the proposed method to demonstrate a drug rediscovery pipeline for
Donepezil [4], a known Acetylcholinesterase Inhibitor.
[1] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez,Lukasz Kaiser, and Illia Polosukhin. Attention Is All You Need. 31st Conference on NeuralInformation Processing Systems (NIPS 2017). 2017. 5998-6008.
[2] Xiao Qing Lewell, Duncan B. Judd, Stephen P. Watson, and Michael M. Hann. Recap Retrosynthetic combinatorial analysis procedure: A powerful new technique for identifying
privileged molecular fragments with useful applications in combinatorial chemistry. Journal ofChemical Information and Computer Sciences, 38(3). 1998. 511–522.
[3] Jonas Mueller, David Gifford, and Tommi Jaakkola. Sequence to better sequence: continuous
revision of combinatorial structures. International Conference on Machine Learning. 2017.
2536–2544.
[4] Hachiro Sugimoto, Hiroo Ogura, Yasuo Arai, Youichi Iimura, and Yosh iharu Yamanishi.
Research and development of donepezil hydrochloride, a new type of acetylcholinesterase
inhibitor. Japanese Journal of Pharmacology, 89(1). 2002. 7–20.

O5-6

Hit to Lead Discovery of BenzylpiperidineAcetylcholinesterase Inhibitors Using Generative
Models: a Retrospective Case Study

1

Nazim Medzhidov 1

Joshua Owoyemi 1nazim.medzhidov@elix-inc.com

joshua.owoyemi@elix-inc.com

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

Keywords: Generative Models, De novo drug design, Acetylcholinesterase inhibitors

Continuous increase in cost together with challenges associated with the traditional drug
discovery process have facilitated the application of machine learning approaches in thisdomain. Past several years have provided with the examples where generative models
proved successful in aiding de novo drug discovery [1]. However, the number of suchsuccess stories is still few and improved approaches are being continuously investigated.
In this study, we used our in-house developed generative model [2] to cover a hit-to-leaddiscovery
campaignof
Acetylcholinesterase(AChE)
inhibitors
containing
benzylpiperidine moiety. We designed a retrospective discovery scenario using publicly
available data, where data was arranged in a chronological order and chemotype relevance.
The goal was to replicate conditions of data scarcity during drug discovery process andinvestigation of novel target chemotype from an initial small set of hit compounds andpublic data. Generative model was then trained to generate molecules satisfying
multi-objective criteria accounting for desired physical-chemical & medicinal chemistry
properties, drug likeliness, synthetic accessibility, novelty, and activity towards AChE.
Generated molecules were then subjected to an extensive post-processing pipeline andfinal list of suggested molecules was ranked and evaluated. Interestingly, among the final
list of generated molecules we observed presence of documented successful compoundswith benzylpiperidine moiety, having distant scaffolds to the training set. Moreover, an
FDA-approved indanone-benzyliperidine AChE inhibitor donepezil and it’s derivative [3]were also among the final list of suggested molecules generated. Our model was able tostart from a set of hit compounds and successfully reach to novel and more potent scaffolds
that were discovered at later stages of the original drug discovery campaign of AChE
inhibitors.
[1] Zhavoronkov, A., Ivanenkov, Y.A., Aliper, A. et al. Deep learning enables rapid
identification of potent DDR1 kinase inhibitors. Nat Biotechnol 37, 1038–1040 (2019).
[2] Owoyemi, J., manuscript in preparation[3] Sugimoto, Hachiro et al. Research and development of donepezil hydrochloride, a new
type of acetylcholinesterase inhibitor. Japanese journal of pharmacology 89,1 (2002)