论文标题
通过深钢筋学习，在合成可访问的化学空间中的分子设计
Molecular Design in Synthetically Accessible Chemical Space via Deep Reinforcement Learning
论文作者
论文摘要
生成药物设计的基本目标是提出符合预定义活动，选择性和药代动力学标准的优化分子。尽管最近取得了进展，但我们认为现有的生成方法在优化过程中有利地转移分子特性的分布的能力受到限制。相反，我们为分子设计提出了一个新颖的增强学习框架，在该框架中，代理商学会了通过合成可访问的药物样分子直接优化。通过将Markov决策过程中的过渡定义为化学反应，这是可能的，并使我们能够利用合成路线作为电感偏见。我们通过证明它在优化药理学上的目标方面胜过现有的最新方法来验证我们的方法，而多目标优化任务的结果表明，对现实的药物设计问题的可扩展性提高了。
The fundamental goal of generative drug design is to propose optimized molecules that meet predefined activity, selectivity, and pharmacokinetic criteria. Despite recent progress, we argue that existing generative methods are limited in their ability to favourably shift the distributions of molecular properties during optimization. We instead propose a novel Reinforcement Learning framework for molecular design in which an agent learns to directly optimize through a space of synthetically-accessible drug-like molecules. This becomes possible by defining transitions in our Markov Decision Process as chemical reactions, and allows us to leverage synthetic routes as an inductive bias. We validate our method by demonstrating that it outperforms existing state-of the art approaches in the optimization of pharmacologically-relevant objectives, while results on multi-objective optimization tasks suggest increased scalability to realistic pharmaceutical design problems.
