定量的構造物性相関/定量的構造活性相関モデルの逆解析を利用した化学構造創出に関する研究

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 船津 公人, 東京大学教授 酒井 康行, 東京大学准教授 杉山 弘和, 東京大学准教授 伊藤 大知, 京都大学特任教授 奧野 恭史, スイス連邦工科大学教授 Gisbert SchneiderUniversity of Tokyo(東京大学

Prediction of Reaction Yield for Buchwald-Hartwig Cross-coupling Reactions Using Deep Learning

Chemical reaction yield is one of the most important factors for determining reaction conditions. Recently, several machine learning-based prediction models using high-throughput experiment (HTE) data sets were reported for the prediction of reaction yield. However, none of them were at a practical level in terms of predictive ability. In this study, we propose a message passing neural network (MPNN) model for chemical yield prediction, focusing on the Buchwald-Hartwig cross-coupling HTE data set. As an initial atom embedding in MPNN model, we propose to use the Mol2Vec feature vectors pre-trained using a large compound database. Predictive ability of the proposed model was higher than that of previously reported five models for the three out of five data sets. Moreover, visualization of important atoms based on self-attention mechanism was in favor of Mol2Vec as an atom embedding rather than other embeddings including previously employed simple representations

Exploring differential evolution for inverse QSAR analysis [version 1; referees: 2 approved]

Inverse quantitative structure-activity relationship (QSAR) modeling encompasses the generation of compound structures from values of descriptors corresponding to high activity predicted with a given QSAR model. Structure generation proceeds from descriptor coordinates optimized for activity prediction. Herein, we concentrate on the first phase of the inverse QSAR process and introduce a new methodology for coordinate optimization, termed differential evolution (DE), that originated from computer science and engineering. Using simulation and compound activity data, we demonstrate that DE in combination with support vector regression (SVR) yields effective and robust predictions of optimized coordinates satisfying model constraints and requirements. For different compound activity classes, optimized coordinates are obtained that exclusively map to regions of high activity in feature space, represent novel positions for structure generation, and are chemically meaningful

Exploring Topological Pharmacophore Graphs for Scaffold Hopping

The primary goal of ligand-based virtual screening is to identify active compounds consisting of a core scaffold that is not found in the current active compound pool. Scaffold hopping is the term used for this purpose. In the present study, topological representations of pharmacophore features on chemical graphs were investigated for scaffold hopping. Pharmacophore graphs (PhGs), which consist of pharmacophore features as nodes and their topological distances as edges, were used as a representation of important information on compounds being active. We investigated ranking methods for prioritizing PhGs for scaffold hopping. The proposed method, NScaffold, which ranks PhGs based on the number of scaffolds covered by the PhGs, outperforms other conventional methods. As a demonstrative case, using a thrombin inhibitor data set, we interpreted the highest-ranked PhGs by NScaffold from the protein–ligand interaction point of view. It resulted that the NScaffold method successfully retrieved three known important interactions, showing the potential for identifying scaffold-hopped compounds with interpretable PhGs

Ligand-based Activity Cliff Prediction Models with Applicability Domain

Activity cliffs (ACs) are formed by pairs of structurally similar compounds with large differences in potency. Predicting ACs is of high interest in lead optimization for drug discovery. Previous AC prediction models that focused on matched molecular pair (MMP) cliffs produced adequate performances. However, the extrapolation ability of these models is unclear because the main scaffold for MMPs, the core structure, could exist in both training and test data sets. Also, representation of MMPs did not consider the attachment points where the core and R-group substituents are connected. In this study, we aimed to improve a ligand-based AC prediction method using molecular fingerprints. We incorporated applicability domain, which was defined using R-path fingerprints to consider the local environment around an attachment point. Rigorous evaluation of the extrapolation ability of AC prediction models showed that MMP-cliffs were accurately predicted for nine biological targets. Furthermore, incorporation of training MMPs with cores distinct from those of test MMPs improved the predictability compared with using training MMPs with only similar cores