论文标题
通过邻里控制的语法增强分子优化
Reinforced Molecular Optimization with Neighborhood-Controlled Grammars
论文作者
论文摘要
制药行业的一个主要挑战是设计具有特定所需特性的新分子,尤其是当物业评估昂贵时。在这里,我们提出了MNCE-RL,这是一个通过增强学习,通过分子邻域控制的语法嵌入语法,用于分子优化的图形卷积策略网络。我们扩展了原始的邻域控制的嵌入语法,使其适用于分子图生成并设计有效的算法以从给定的分子推断语法生产规则。语法的使用保证了产生的分子结构的有效性。通过将分子图转换为用推断的语法解析树,分子结构生成任务被建模为马尔可夫决策过程,在该过程中,利用了策略梯度策略。在一系列实验中,我们证明了我们的方法在各种分子优化任务中实现最先进的性能,并在优化分子特性的情况下表现出重要的优势,具有有限的性质评估。
A major challenge in the pharmaceutical industry is to design novel molecules with specific desired properties, especially when the property evaluation is costly. Here, we propose MNCE-RL, a graph convolutional policy network for molecular optimization with molecular neighborhood-controlled embedding grammars through reinforcement learning. We extend the original neighborhood-controlled embedding grammars to make them applicable to molecular graph generation and design an efficient algorithm to infer grammatical production rules from given molecules. The use of grammars guarantees the validity of the generated molecular structures. By transforming molecular graphs to parse trees with the inferred grammars, the molecular structure generation task is modeled as a Markov decision process where a policy gradient strategy is utilized. In a series of experiments, we demonstrate that our approach achieves state-of-the-art performance in a diverse range of molecular optimization tasks and exhibits significant superiority in optimizing molecular properties with a limited number of property evaluations.