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Prediction of Chemical Reaction Type withHierarchical Graph Neural Networks
Tatsuya Ishimoto 1

Nobuyuki Yasuo 2

ishimoto.t.ac@m.titech.ac.jp

yasuo.n.aa@m.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 (TAC-MI), Tokyo Institute ofTechnology, Tokyo, 152-8550, Japan

Keywords: Chemical reaction classification, Graph Neural Networks
Chemical reaction classification help chemists to index their reaction database and identify newtypes of reaction [1]. Data-driven reaction classification methods have been developed [2-3].
However, most of them do not adequately represent the hierarchical structure of chemical reactions.
In this study, we propose the method of reaction classification with hierarchical graph representation.
We experimented the model using the USTPO dataset to evaluate its efficiency.
[1] Bawden, D. Classification of Chemical Reactions: Potential, Possibilities and Continuing
Relevance. J. Chem. Inf. Comput. Sci., 1991, 31 (2), 212–216.
[2] Schneider, N.; Lowe, D. M.; Sayle, R. A.; Landrum, G. A. Development of a Novel Fingerprint
for Chemical Reactions and Its Application to Large-Scale Reaction Classification and Similarity.
J. Chem. Inf. Model. 2015, 55 (1), 39–53.
[3] Schwaller, P.; Probst, D.; Vaucher, A. C.; Nair, V. H.; Kreutter, D.; Laino, T.; Reymond, J.-L.
Mapping the Space of Chemical Reactions Using Attention-Based Neural Networks. Nature
Machine Intelligence 2021, 3 (2), 144–152.

O1-2

Molecular Optimization by Graph Generative Model using
Transformers
Shunya Makino 1
makino.s.ad@m.titech.ac.jp

Nobuaki Yasuo 2
yasuo@cbi.titech.ac.jpMasakazu Sekijima 1
sekijima@c.titech.ac.jp

1

Department of Computer Science, Tokyo Institute of Technology, Tokyo, 152-8550, Japan
Academy for Convergence of Materials and Informatics (TAC-MI), Tokyo Institute ofTechnology, Tokyo, 152-8550, Japan

2Keywords: machine learning, molecular optimization, graph generation, Transformers
In drug discovery, the significant cost and time required to develop new drugs have become aproblem, and computational methods to reduce these costs have been actively researched. Lead
optimization plays an important role in drug discovery and aims to enhance the activity or improve
the ADMET properties of a compound known to be active against a target by changing its structure.
Recent advances in deep learning technology have led to the development of graph generation
methods such as VAE and GAN[1, 2], which have been applied to the generation of molecular graphs.
In the latest research, the Transformer[3] model has also been applied to graph representation
learning, with excellent results in the task of predicting molecular properties.[4]In this study, we extend graph representation learning with Transformer and propose a moleculargraph generation method using the Transformer model and we test the model on the ZINC[5]database to demonstrate the usefulness of the proposed method.
[1] Simonovsky, Martin, and Nikos Komodakis. "Graphvae: Towards generation of small graphs
using variational autoencoders." International conference on artificial neural networks. Springer,Cham, 2018.
[2] De Cao, Nicola, and Thomas Kipf. "MolGAN: An implicit generative model for small moleculargraphs." arXiv preprint arXiv:1805.11973 (2018).
[3] Vaswani, Ashish, et al. "Attention is all you need." Advances in neural information processingsystems 30 (2017).
[4] Ying, Chengxuan, et al. "Do transformers really perform badly for graphrepresentation?." Advances in Neural Information Processing Systems 34 (2021): 28877-28888.
[5] Irwin, John J., and Brian K. Shoichet. "ZINC− a free database of commercially available
compounds for virtual screening." Journal of chemical information and modeling 45.1 (2005): 177182.

O1-3

An Enhanced Machine Learning Models for Predicting
Retrosynthesis Accessibility
Mami Ozawa 1
ozawa.m.ac@m.titech.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, Tokyo, 152-8550, Japan
Academy for Convergence of Materials and Informatics (TAC-MI), Tokyo Institute ofTechnology, Tokyo, 152-8550, Japan

Keywords: drug discovery, machine learning, synthesizability prediction, retrosynthesis, moleculargeneration
In the field of drug discovery, the amount of time and money spent on research and development isa problem. According to [1], it takes about as much as 10-15 years and 2.6 billion. In this context,
the use of computational science to reduce the cost of drug discovery is attracting attention. An
example of use is to generate new compounds using molecular generation models and filter themby predicting their synthetic accessibility using machine learning. RAscore[2] is one of thepredictive models based on machine learning, and this study examines the practicality of theRAscore model and the problems associated with it. It also proposes a method for building models
that can make more accurate predictions based on hypotheses about causes. Specifically, in addition
to the ChEMBL[3] data used to train the original RAscore, the model was trained using datagenerated by a molecular generation model, ChemTS[4]. As a result, better area under the curve(AUC) and binary accuracy were achieved. The models, datasets, and source code are available athttps://github.com/sekijima-lab/retrosynthesizability_prediction_models .
[1] Hansen RW DiMasi JA, Grabowski HG. Innovation in the pharmaceutical industry: Newestimates of r&d costs. J Health Econ, 47(4):20–33, 2016.
[2] Amol Thakkar, Veronika Chadimov ́ a , Esben Jannik Bjerrum, Ola Engkvist, and Jean-LouisReymond. Retrosynthetic accessibility score (rascore) – rapid machine learned synthesizability
classification from ai driven retrosynthetic planning. Chem. Sci., 12:3339–3349, 2021.
[3] Nowotka M et al. Gaulton A, Hersey A. The chembl database in 2017. Nucleic Acids Res,45(D1):D945–D954, 2017.
[4] Xiufeng Yang, Jinzhe Zhang, Kazuki Yoshizoe, Kei Terayama, and Koji Tsuda. Chemts: anefficient python library for de novo molecular generation. Science and Technology of Advanced
Materials, 18(1):972–976, 2017.

O1-4

Multi-Objective Molecular Optimization Using MonteCarlo Tree SearchSuzuki Takamasa 1 †suzuki.t.dq@m.titech.ac.jp

Yasuo Nobuaki 2
yasuo@cbi.titech.ac.jp

Ma Dian 1 †ma.d.ab@m.titech.ac.jp

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

1

Department of Computer Science, Tokyo Institute of Technology, 152-8550, Japan
Academy for Convergence of Materials and Informatics, Tokyo Institute of Technology, Tokyo,152-8550, Japan†Corresponding Author
2

Keywords: In silico drug design, Molecular Generation, Multi -Objective Optimization
Drugs have saved millions of lives in human history, but the cost of developing a new drug has risen
dramatically in the last decades. Recently, more and more computer -aided drug discovery (CADD)
methods have been practiced in the pharmaceutical industry [1].
To address that problem, studies on molecular generation models which can generate compounds withnovel structures have been raised in recent years. Early generative models decide their search direction
on optimization for a single objective [3], however, a drug candidate required more multiple evaluations.
In this study, we developed a new deep learning-based extendable multiple-objective moleculargenerator, which could optimize molecules based on their properties and their affinity towards targetproteins. This generator utilizes a recurrent neural network (RNN) to generate molecules and Pareto
Multi-Objective Monte Carlo Tree Search (Pareto MOMCTS) to decide search direction.
By using our method, we have validated the generation of compounds for specific target proteins using
the drug-like properties and docking scores as objective functions. In each search process, a new Monte
Carlo search tree is built, and the change of the Pareto front, which decide t he search direction, shows
the transfer of the search process in different stages. This study will enable our method to provide more
effective drug design assistance when compared to methods with a s ingle objective, such as QED.
However, compared with a single objective of drug-like properties, which made our method more
effective in drug design.

[1] Stephen J. H.; Thomas U. M.; David T M.; Reza F.; Randall W. K.; Timothy J. M.; Stuart L S.;
Dissecting cellular processes using small molecules: Identification of colchicine -like, taxol-likeand other small molecules that perturb mitosis. Chemistry & Biology, 2000, 7(4), 275–286.
[2] Rafael G. B.; Jennifer N. W.; David D.; José M. H. L .; Benjamín S. L.; Dennis S.; Jorge A. I.;
Timothy D. H.; Ryan P. A.; Alan A. G.; Automatic chemical design using a data-driven continuous
representation of molecules. ACS Central Science,2018, 4(2):268–276[3] Robin W.; Floriane M.; Andreas S.; Hans B.; Frank N.; Djork-Arn´e C.; Efficient multi-objective
molecular optimization in a continuous latent space. Chemical Science, 2019, 10(34):8016–8024

O1-5

Morphology-based drug effect profiling fordelicate label-free phenotypic screening
Ryuji Kato 1
kato-r@ps.nagoya-u.ac.jp

Yuto Takemoto 1
takemoto.yuto.p2@s.mail.nagoya-u.ac.jp

Kenjiro Tanaka 1
tanaka-k@ps.nagoya-u.ac.jp

Yuta Imai 1
imai.yuta95@gmail.com

Yuto Okumura 1
okumura.yuto@f.mbox.nagoya-u.ac.jp
12

Kei Kanie 1,2
kanie-k@hiro.kindai.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 Biotechnology and Chemistry, Faculty of Engineering, Kindai University, 1Umenobe, Takaya, Higashi-Hiroshima, Hiroshima 739-2116, Japan.

Keywords: Label-free, phenotypic screening, cell morphology,
By the advances of cell science, in vitro phenotypic screening has become a leading drug screening
method. Label-free phenotypic analysis, which combines the recent image processing and machinelearning technologies for quantitative cell image analysis, offers a balanced combination of highthroughput and low cost, while providing a platform for non-invasive cell evaluation. It is not only
cost-effective, but also flexible since label-free assay is free from complex validation andoptimization of staining (such as customizing fluorescence wavelength overlap, bleaching effects,
and transfection efficiency). Moreover, it is also free from the pre-knowledge of biomarkers.
In conventional label-free cell image analysis, there are basically two types of image datautilization concepts. The first type extracts multiple features from the cellular area in the image todescribe cell morphology, such as area, roundness, and peripheral features [1], or intensity patterns
and textures for the following analysis. Commonly, such features reflect the microscopicallyobserved morphological characteristics, therefore analysis results, especially validity of the trainedmodel, can be interpreted biologically. The second type uses image-tiles in the image as
representative pixel patterns, and use them directly for training prediction models, such as deeplearning models [2]. The collected image-tiles serve as functional descriptors, however, theirfeatures and how they were used in the model are not interpretable.
We have been proposing the importance of the former concept to establish explainable machinelearning models for cell quality profiling. Practically, we have reported that morphology-based
features from single cells can be summarized to describe the “population profile” of cells, and suchfeatures are important and effective for robust morphology-based cell analysis with various typesof cells. In this presentation, we will present the performances and effectiveness of our morphologybased “cell population analysis” in the application of phenotypic drug screening [3].
[1] Sasaki, H. et al.: Label-free morphology-based prediction of multiple differentiation
potentials of human mesenchymal stem cells for early evaluation of intact cells. PLoSONE, 2014, 9, e93952.
[2] Piotrowski, T. et al. Deep-learning-based multi -class segmentation for automated, non invasive routine assessment of human pluripotent stem cell culture status. Computers inBiology and Medicine , 2021, 129,104172.
[3] Imai, Y. et al.: Label-free morphological sub-population cytometry for sensitive phenotypic
screening of heterogenous neural disease model cells. Scientific Reports, 2022, 12, 9296.

O1-6
A recommendation algorithm for predicting drug effects
considering directionality
Iori Azuma 1
azuma-iori@g.ecc.u-tokyo.ac.jp

Tadahaya Mizuno 1, †Hiroyuki Kusuhara 1, †tadahaya@mol.f.u-tokyo.ac.jp

kusuhara@mol.f.u-tokyo.ac.jp

1

Laboratory of Molecular Pharmacokinetics, Graduate School of Pharmaceutical Sciences, The
University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo, Japan†Author to whom correspondence should be addressed

Keywords: Matrix factorization, Recommendation system, Side effects, Therapeutic indications
Considerable information is available regarding the safety, efficacy, and tolerance ofapproved drugs, which can minimize the expenditure and time required to predict new drugeffects. With the development of machine learning techniques, recommendation systems
based on matrix factorization for the prediction of drug effects h ave been well studied andvarious algorithms have been devised. However, most algorithms use only the presence orabsence of known interactions as binary information and no studies that predict drug side
effects using a recommendation system that considers the bilateral character of drug effects(i.e., a therapeutic effect and a side effect) are available. In the present study, we proposeda novel algorithm named neighborhood regularized bidirectional matrix factorization(NRBdMF) to predict drug effects by incorporating bidirectionality, which is a characteristic
property of drug effects.

First, we conducted a survey of existing drug effect prediction algorithms using matrix
factorization recommendation systems, focusing on their conceptual aspects. We found thatexisting methods are broadly classified into seven representative algorithms and employ
binary information without considering the bilateral character of drug effects. Then wecompared the prediction performance of these methods. Among the representative methods,
neighborhood regularized logistic matrix factorization (NRLMF) [1] showed the best
performance in benchmark tests.
Next, inspired by NRLMF, we developed a more generalized multilabel learningalgorithm named NRBdMF that can handle bidirectionality and predict potential drug–target interaction. We used this proposed method for predicting side effects using a matrix
that considered the bidirectionality of drug effects, in which known side effects wereassigned a positive (+1) label and known treatment effects were assigned a negative (−1)
label. We compared the side effect prediction performance between NRBdMF and NRLMF
with enrichment score (ES), defined as the difference in enrichment values for each of theside effects and indications. The mean ES of NRBdMF (0.588  0.101) outperformed that ofNRLMF (0.191  0.106). This indicates that the proposed method, NRBdMF, can enrich sideeffects and eliminate indications in the top -ranked predictions, which reduces the false
positives and provides interpretable prediction results .
In a case study of the top 10 and bottom 10 of the predicted drug candidates that cancause hypertension, NRBdMF model could enrich more plausible drug candidates at the topranks and less plausible candidates at the bottom ranks in predicting drug side effects.
These results indicate that the proposed NRBdMF achieves highly interpretable side effectprediction in the framework of recommendation systems based on matrix factorization.
[1] Y. Liu, M. Wu, C. Miao, P. Zhao, X.-L. Li, Neighborhood Regularized Logistic Matrix
Factorization for Drug-Target Interaction Prediction, PLOS Comput. Biol. 12 (2016) e1004760