药效团
化学空间
强化学习
虚拟筛选
化学
药物发现
计算机科学
人工智能
计算生物学
立体化学
生物化学
生物
作者
Atsushi Yoshimori,Enzo Kawasaki,Chisato Kanai,Tomohiko Tasaka
出处
期刊:Chemical & Pharmaceutical Bulletin
[Pharmaceutical Society of Japan]
日期:2020-03-01
卷期号:68 (3): 227-233
被引量:15
标识
DOI:10.1248/cpb.c19-00625
摘要
The goal of drug design is to discover molecular structures that have suitable pharmacological properties in vast chemical space. In recent years, the use of deep generative models (DGMs) is getting a lot of attention as an effective method of generating new molecules with desired properties. However, most of the properties do not have three-dimensional (3D) information, such as shape and pharmacophore. In drug discovery, pharmacophores are valuable clues in finding active compounds. In this study, we propose a computational strategy based on deep reinforcement learning for generating molecular structures with a desired pharmacophore. In addition, to extract selective molecules against a target protein, chemical genomics-based virtual screening (CGBVS) is used as post-processing method of deep reinforcement learning. As an example study, we have employed this strategy to generate molecular structures of selective TIE2 inhibitors. This strategy can be adopted into general use for generating selective molecules with a desired pharmacophore.
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