Oral Presentation 11

日時： 2020 年 10 月 30 日 14:45-16:15
チャンネル： ２

口頭発表１１『データサイエンス/ 構造相関解析』
Data Science/Structure Activity Relationship
座長： 植沢 芳広 （明治薬科大学）
Yoshihiro Uesawa (Meiji Pharmaceutical University),
湯田 浩太郎 （株式会社インシリコデータ）
Kotaro Yuta (In Silico Data, Ltd.)
O2-12"Single-step Retrosynthesis Prediction based on the Identification of Potential
Disconnection Sites"
Haris Hasic(Department of Computer Science, School of Computing, Tokyo Institute ofTechnology)
O2-13"Deep neural network models to predict ion channel inhibitors with pre-learning ofactivity information of analogous proteins"
Tomohiro Sato(Center for Biosystems Dynamics Research, RIKEN)
O2-14"Statistically estimating life-long progression of chronic disease from the informationof short clinical trials"
Ryota Jin
(Laboratory of Clinical Pharmacology and Pharmacometrics, Graduate School ofPharmaceutical Sciences, Chiba University)
O2-15"Prediction models of peroxisome proliferator-activated receptor-gamma through a
deep learning-based QSAR analysis, DeepSnap"
Yasunari Matsuzaka(Department of Medical Molecular Informatics, Meiji Pharmaceutical University)

O2-12Single-step Retrosynthesis Prediction based on theIdentification of Potential Disconnection Sites1Haris Hasic 1

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, 152-8550 Tokyo

Keywords: Machine Learning – AI Method Development, Descriptor (Parameter) Development
Retrosynthetic analysis or retrosynthesis is a technique used by chemists during the pl anning ofchemical synthesis routes [1]. The manual application of retrosynthesis requires a lot of knowledge
and experience to identify possible transformations for a given target compound. Furtherm ore,
since the complexity scales with the complexity of t he targets, efficient application on compoundswith complex structures becomes an almost impossible task for human chemists. The idea of using
computers in such situations has existed for a long time , but the accuracy was insufficient forpractical applications. However, with the steady improvement of artificial intelligence in recentyears, computer-assisted retrosynthesis has been gaining research attention again. Because of theoverall lack of available chemical reaction data, the main challenge of recen t computer-assisted
retrosynthesis approaches is low exploration ability during the analysis of target and intermediatecompounds [2]. This analysis is referred to as single-step retrosynthesis.
In this research, we developed a novel, template-free approach for the single-step retrosynthesis
task for any given molecule. To ensure general applicability of the approach, we used onlyindividual molecular substructures of the target compound to determine potential disconnection
sites, without relying on additional information. The model for the identification of potential
disconnection sites was constructed to use a n ovel molecular substructure fingerprint
representation based on the extended connectivity fingerprints [3]. Then, a simple structuralsimilarity-based reactant retrieval and scoring implementation was applied for each of theidentified disconnection sites. As a result of the evaluation experiments, t he proposed method
outperforms the state-of-the-art method [4] for the single-step retrosynthesis task by achieving
48.9% Top-1 accuracy on the pre-cleaned U.S. Patent Office dataset [5]. If the reaction classpredicted by the model was used to further narrow down reactant candidate search space, theperformance is additionally improved to 61.4% Top-1 accuracy [6].
.
[1] Clayden, J., Greeves, N., and Warren, S., Organic Chemistry, Oxford University Press, 2012.
[2] Coley, C.W., Rogers, L., Green, W.H., and Jensen, K.F., Computer-Assisted Retrosynthesis
based on Molecular Similarity, ACS Central Science, 3(12):1237-1245, 2017.
[3] Rogers, D., and Hahn, M., Extended-connectivity Fingerprints, Journal of Chemical
Information and Modelling, 50(5):742-754, 2010.
[4] Zheng, S., Rao, J., Zhang, Z., Xu, J., and Yang, Y., Predicting retrosynthetic reactions using
self-corrected transformer neural networks. Journal of Chemical Information and Modelling,
60:47-55, 2020.
[5] Schneider, N., Lowe, D.M., Sayle, R.A., Tarselli, M.A., and Landrum, G.A., Big Data fromPharmaceutical Patents: A Computational Analysis of Medicinal Chemists' Bread and Butter.
ACS Journal of Medicinal Chemistry, 59(9):4385-4402, 2016.
[6] Hasic H., and Ishida T., Single-step Retrosynthesis Prediction based on the Identification ofPotential Disconnection Sites using Molecular Substructure Fingerpr ints. Submitted to the BMCJournal of Chemoinformatics, 2020.

O2-13

Deep neural network models to predict ion channel
inhibitors with pre-learning of activity information ofanalogous proteins
Tomohiro Sato

Teruki Honma

tomohiro.sato@riken.jp

honma.teruki@riken.jp

Center for Biosystems Dynamics Research, RIKEN, 1-7-22 Suehiro-cho,
Tsurumi-ku, Yokohama City, Kanagawa, 230-0045, Japan

Keywords: deep neural network, pre-learning, machine learning, ion channel, cardiotoxicity
The inhibitions of ion channels are closely related to various drug side effects such as torsade d epointes caused by the inhibition of hERG potassium channel. Recent studies revealed that theinhibition of various cardiac ion channels could result in diverse cardiotoxicities[1].
Comprehensive prediction of inhibitory activities for cardiac ion channels could greatly reduce therisk of cardiotoxicities in drug discovery. Though we reported the construction of a highly accurate
hERG prediction model by integrating information dispersing in various databases[2,3], thelimited number of inhibitory activity information could become a stabling block to constructaccurate statistical models for most of the other cardiac ion channels. In this study, wedemonstrated that the pre-learning about activity information of analogous proteins could
effectively improve the prediction performances of deep neural network (DNN) models for ion
channels with less activity information.
In the case of discrimination analysis ofNav1.5, Kv1.5, and Cav1.2 inhibitors,
the pre-learning of other sodium
channels, potassium channels, andcalcium channels improved the kappa
statistics from 0.68 by normal DNN
model to 0.73 by pre-learned DNN
model for Nav1.5, from 0.496 to 0.545
for Kv1.5, and from 0.387 to 0.548 for
Cav1.2 (Figure 1), suggesting theusefulness of pre-learning techniquewhen the activity information of thetarget protein is insufficient toconstruct an accurate statistical model.
The prediction models were available
Figure 1. Kappa statistics of normal and pre-learned
at https://drugdesgin.riken.jp/hERG.
DNN models for Nav1.5, Kv1.5, and Cav1.2.
References[1] Bartos DC, Grandi E, Ripplinger CM. Ion Channels in t he Heart. Compr Physiol. 2015, 5(3),1423-1464[2] Sato, T., Yuki, H., Ogura, K., Honma, T. Construction of an integrated database for hERG
blocking small molecules. PLOS ONE 2018, 13(7)[3] Ogura, k., Sato, T., Yuki, H., Honma, T. Support Vector Machine model for hERG inhibitory
activities based on the integrated hERG database using descriptor selection by NSGA -II. Sci.
Rep. 2019, 9, 12220

O2-14
Statistically estimating life-long progression of chronic
disease from the information of short clinical trials
Ryota Jin 1

ryota-j@chiba-u.jp

Hiromi Sato 1

hiromi-s@chiba-u.jp

Shinya Kawamatsu 1, 2

Shogo Araki 1

s.kawamatsu.gifu@gmail.com

shogo.1202.tandf@gmail.com

Yasunori Sato 3

Akihiro Hisaka 1

Yasunori.sato@keio.jp

hisaka@chiba-u.jp

Laboratory of Clinical Pharmacology and Pharmacometrics, Graduate School of
Pharmaceutical Sciences, Chiba University, Chiba, 260-8675, Japan.
2
Clinical Operations, Japan Development, GlaxoSmithKline, Tokyo, Japan.
3
Department of Preventive Medicine and Public Health, Keio University School ofMedicine, Tokyo, Japan
1

Keywords: Nonlinear mixed-effect model, Population pharmacokinetics analysis, Long-term
pathological model, Chronic disease
Progressive central nervous system diseases including Alzheimer's disease and Parkinson's disease, andvarious chronic diseases including diabetes, cardiovascular disease, COPD and rheumatism, persistentlyprogress throughout life. The change of biomarkers associated with the progression and factors that
affect the progression are essential information as the basis of remedy , but they are usually difficult toelucidate due to the need for decades of cohort studies. However, if a large number of patients withdifferent degrees of disease progression are enrolled, even with short-term studies, that is, in caseof that the ergodic hypothesis (anzats) in mathematics is approximately satisfied for patients, it ispossible to estimate the long-term progression. Based on this concept, we developed SReFT(Statistical Restoration of Fragmented Time course) and analyzed the progression of Alzheimer's
disease [1]. In this study, the applications of SReFT to Parkinson's disease and COPD [2] will bedescribed. In each case, we succeeded in analyzing the change of biomarkers for more than 30 years
and factors influencing progressions such as gene mutation and smoking from individual
information of several biomarkers of hundreds to thousands of patients. The advantages of SReFT
have become apparent, such as detecting long-term changes that could not be according toconventional analyses and distinguishing early and late changes in the disease. In this presentation,
we will outline the algorithm of SReFT, which can be said to be a new type of machine learning,along with the currently available analysis environments, and report the results of clinical data
analysis.

[1] Ishida, T., et al. , A Novel Method to Estimate Long-Term Chronological Changes FromFragmented Observations in Disease Progression, Clin Pharmacol Ther, 105:436-447, 2019.
[2] Kawamatsu, S., et al., Scores of health-related quality of life questionnaire worsen consistently
in patients of COPD: estimating disease progression over 30 years by SReFT with individual
data collected in SUMMIT trial, J Clin Med, 9:2676, 2020

O2-15Prediction models of peroxisome proliferator-activatedreceptor-gamma through a deep learning-based QSAR analysis,
DeepSnap
Yasunari Matsuzaka

Yoshihiro Uesawa

matsuzys@my-pharm.ac.jp

uesawa@my-pharm.ac.jp

Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Tokyo,

Keywords: DeepSnap, Deep learning, peroxisome proliferator-activated receptor-gamma, QSAR,
Tox21,We have previously reported a novel technique for capturing images of chemical compounds fordeep learning (DL)-based quantitative structure-activity relationship (QSAR) analysis, namely,
DeepSnap-DL [1,2]. This approach can automatically perform feature extraction using snapshot
images derived from 3D chemical structures and create input image using data augmentation byangle-designated rotation, zoom specification, and RGB conversion, that expect to increase in theamount of information on chemical structure, not just data padding and to prevent over -fitting that
occur when learning is performed only with to match training data [1-5]. Thus, it has been shownthat the prediction performances of the DeepSnap-DL approach were higher than those of
conventional machine learning, including random forest, XGBoost, LightGBM, and CatBoost [3-5].
In general, the perturbation of nuclear receptor (NR) signaling pathway due to unwanted
interactions of chemical compounds with some NRs is associated with various adverse effects onhuman health. To develop new chemical risk assessment approaches, we established cell-based invitro high-throughput screening assays for agonist/antagonist activities of some NRs using thereporter assay in Toxicology in the 21 st Century (Tox21) program [6]. Among these NRs,
peroxisome proliferator-activated receptor-gamma (PPARg) plays a paramount role as anantioxidant response in tissue injury. Hence, we constructed prediction models of PPARg agonist
activity by the DeepSnap-DL approach using the Tox21 chemical library.
[1] Uesawa, Y. Quantitative structure-activity relationship analysis using deep learning based on anovel molecular image input technique, Bioorg Med Chem Lett, 28(20), 3400-3403, 2018.
[2] Matsuzaka, Y., Uesawa, Y. Optimization of a Deep-Learning Method Based on theClassification of Images Generated by Parameterized Deep Snap a Novel
Molecular-Image-Input Technique for Quantitative Structure-Activity Relationship (QSAR)
Analysis, Front Bioeng Biotechnol, 7:65, 2019.
[3] Matsuzaka, Y., Uesawa, Y. Prediction Model with High-Performance Constitutive Androstane
Receptor (CAR) Using DeepSnap-Deep Learning Approach from the Tox21 10K CompoundLibrary, Int J Mol Sci, 20(19):4855, 2019.
[4] Matsuzaka, Y., Uesawa, Y. DeepSnap-Deep Learning Approach Predicts Progesterone Receptor
Antagonist Activity With High Performance, Front Bioeng Biotechnol, 7:485, 2020.
[5] Matsuzaka Y, Hosaka T, Ogaito A, Yoshinari K, Uesawa Y. Prediction Model of Aryl
Hydrocarbon Receptor Activation by a Novel QSAR Approach, DeepSnap -Deep Learning,Molecules, 25(6):1317, 2020.
[6] Matsuzaka, Y., Uesawa, Y. Molecular Image-Based Prediction Models of Nuclear Receptor
Agonists and Antagonists Using the DeepSnap-Deep Learning Approach with the Tox21 10K
Library, Molecules, 25(12):2764, 2020.