Yoshimori Atsushi, Kawasaki Enzo, Kanai Chisato, Tasaka Tomohiko
Institute for Theoretical Medicine, Inc.
INTAGE Healthcare, Inc.
Chem Pharm Bull (Tokyo). 2020;68(3):227-233. doi: 10.1248/cpb.c19-00625.
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.
药物设计的目标是在广阔的化学空间中发现具有合适药理特性的分子结构。近年来，作为一种生成具有所需特性新分子的有效方法，深度生成模型（DGMs）的使用备受关注。然而，大多数特性不具备三维（3D）信息，如形状和药效团。在药物发现中，药效团是寻找活性化合物的重要线索。在本研究中，我们提出了一种基于深度强化学习的计算策略，用于生成具有所需药效团的分子结构。此外，为了提取针对目标蛋白的选择性分子，基于化学基因组学的虚拟筛选（CGBVS）被用作深度强化学习的后处理方法。作为一个实例研究，我们采用该策略生成了选择性TIE2抑制剂的分子结构。该策略可普遍用于生成具有所需药效团的选择性分子。
