RLSynC: Offline–Online Reinforcement Learning for Synthon Completion,Journal of Chemical Information and Modeling

当前位置： X-MOL 学术 › J. Chem. Inf. Model. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

RLSynC: Offline–Online Reinforcement Learning for Synthon Completion
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2024-08-18 , DOI: 10.1021/acs.jcim.4c00554
Frazier N Baker ₁ , Ziqi Chen ₁ , Daniel Adu-Ampratwum ₂ , Xia Ning _{1,

2,

3,

4}

Affiliation

Retrosynthesis is the process of determining the set of reactant molecules that can react to form a desired product. Semitemplate-based retrosynthesis methods, which imitate the reverse logic of synthesis reactions, first predict the reaction centers in the products and then complete the resulting synthons back into reactants. We develop a new offline–online reinforcement learning method RLSynC for synthon completion in semitemplate-based methods. RLSynC assigns one agent to each synthon, all of which complete the synthons by conducting actions step by step in a synchronized fashion. RLSynC learns the policy from both offline training episodes and online interactions, which allows RLSynC to explore new reaction spaces. RLSynC uses a standalone forward synthesis model to evaluate the likelihood of the predicted reactants in synthesizing a product and thus guides the action search. Our results demonstrate that RLSynC can outperform state-of-the-art synthon completion methods with improvements as high as 14.9%, highlighting its potential in synthesis planning.

中文翻译：

RLSynC：用于 Synthon 补全的离线-在线强化学习

逆合成是确定可以反应形成所需产物的反应物分子组的过程。基于半模板的逆合成方法模拟合成反应的反向逻辑，首先预测产物中的反应中心，然后将得到的合成子完成回反应物。我们开发了一种新的离线-在线强化学习方法 RLSynC，用于在基于半模板的方法中完成合成子。RLSynC 为每个 synthon 分配一个代理，所有这些代理都通过以同步方式逐步执行操作来完成 synthon。RLSynC 从离线训练情节和在线交互中学习策略，这使得 RLSynC 能够探索新的反应空间。RLSynC 使用独立的正向合成模型来评估预测的反应物合成产物的可能性，从而指导动作搜索。我们的结果表明，RLSynC 的性能优于最先进的合成子完成方法，改进高达 14.9%，突出了其在合成规划中的潜力。

更新日期：2024-08-18

点击分享查看原文

点击收藏

公开下载

阅读更多本刊新发论文本刊介绍/投稿指南