Preferred Networks, Inc., 1-6-1 Otemachi, Chiyoda-ku, Tokyo 100-0004, Japan.
J Chem Inf Model. 2022 Sep 12;62(17):4032-4048. doi: 10.1021/acs.jcim.2c00366. Epub 2022 Aug 12.
Automatic design of molecules with specific chemical and biochemical properties is an important process in material informatics and computational drug discovery. In this study, we designed a novel coarse-grained tree representation of molecules (Reversible Junction Tree; "RJT") for the aforementioned purposes, which is reversely convertible to the original molecule without external information. By leveraging this representation, we further formulated the molecular design and optimization problem as a tree-structure construction using deep reinforcement learning ("RJT-RL"). In this method, all of the intermediate and final states of reinforcement learning are convertible to valid molecules, which could efficiently guide the optimization process in simple benchmark tasks. We further examined the multiobjective optimization and fine-tuning of the reinforcement learning models using RJT-RL, demonstrating the applicability of our method to more realistic tasks in drug discovery.
自动设计具有特定化学和生化性质的分子是材料信息学和计算药物发现中的一个重要过程。在这项研究中，我们设计了一种新颖的分子粗粒度树表示（可逆连接树；“RJT”），用于实现上述目的，该表示可以在没有外部信息的情况下反向转换为原始分子。通过利用这种表示，我们进一步将分子设计和优化问题表述为使用深度强化学习（“RJT-RL”）的树结构构建。在这种方法中，强化学习的所有中间和最终状态都可以转换为有效的分子，这可以在简单的基准任务中有效地指导优化过程。我们进一步使用 RJT-RL 检查了强化学习模型的多目标优化和微调，证明了我们的方法在药物发现中更现实任务中的适用性。
