论文标题
使用可解释的子结构的多目标分子生成
Multi-Objective Molecule Generation using Interpretable Substructures
论文作者
论文摘要
药物发现旨在找到具有指定化学特性曲线的新型化合物。在生成建模方面，目标是学会在多个性质约束的交集中进行采样分子。当有许多财产限制时，此任务变得越来越具有挑战性。我们建议通过从我们称为分子原理的子结构的词汇中组成分子来抵消这种复杂性。这些理由是从分子中确定为可能导致每个感兴趣财产的子结构。然后，我们学习使用图生成模型将理由扩展到完整的分子中。我们的最终生成模型将分子作为多种理由完成的混合物组成，并且该混合物经过微调以保留感兴趣的特性。我们在各种药物设计任务上评估了我们的模型，并在精确，多样性和新颖化合物的新颖性方面表现出对最先进基线的显着改善。
Drug discovery aims to find novel compounds with specified chemical property profiles. In terms of generative modeling, the goal is to learn to sample molecules in the intersection of multiple property constraints. This task becomes increasingly challenging when there are many property constraints. We propose to offset this complexity by composing molecules from a vocabulary of substructures that we call molecular rationales. These rationales are identified from molecules as substructures that are likely responsible for each property of interest. We then learn to expand rationales into a full molecule using graph generative models. Our final generative model composes molecules as mixtures of multiple rationale completions, and this mixture is fine-tuned to preserve the properties of interest. We evaluate our model on various drug design tasks and demonstrate significant improvements over state-of-the-art baselines in terms of accuracy, diversity, and novelty of generated compounds.
