为化学反应可行性预测提供可解释的最小必要理由
Prime Implicant Explanations for Reaction Feasibility Prediction
- 提出适合反应可行性的素蕴涵解释新方法
- 实验显示解释能准确捕捉关键分子特征
- 适合需要可解释性的合成路径规划场景
机器学习模型在预测化学反应可行性方面已成自动化合成规划的核心。尽管预测性能出色，但这些模型常缺乏透明性和可解释性。本文针对该领域提出一种新的素蕴涵解释（即最小必要理由）形式，并设计算法用于小规模反应预测任务中计算此类解释。初步实验表明，该解释概念保守地捕获了真实解释：虽然可能包含冗余键和原子，但始终保留对预测反应可行性至关重要的分子属性。
原文摘要 · Abstract (English)
Machine learning models that predict the feasibility of chemical reactions have become central to automated synthesis planning. Despite their predictive success, these models often lack transparency and interpretability. We introduce a novel formulation of prime implicant explanations--also known as minimally sufficient reasons--tailored to this domain, and propose an algorithm for computing such explanations in small-scale reaction prediction tasks. Preliminary experiments demonstrate that our notion of prime implicant explanations conservatively captures the ground truth explanations. That is, such explanations often contain redundant bonds and atoms but consistently capture the molecular attributes that are essential for predicting reaction feasibility.
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