Recently, molecular relational learning, whose goal is to predict the
interaction behavior between molecular pairs, got a surge of interest in
molecular sciences due to its wide range of applications. In this work, we
propose CMRL that is robust to the distributional shift in molecular relational
learning by detecting the core substructure that is causally related to
chemical reactions. To do so, we first assume a causal relationship based on
the domain knowledge of molecular sciences and construct a structural causal
model (SCM) that reveals the relationship between variables. Based on the SCM,
we introduce a novel conditional intervention framework whose intervention is
conditioned on the paired molecule. With the conditional intervention
framework, our model successfully learns from the causal substructure and
alleviates the confounding effect of shortcut substructures that are spuriously
correlated to chemical reactions. Extensive experiments on various tasks with
real-world and synthetic datasets demonstrate the superiority of CMRL over
state-of-the-art baseline models. Our code is available at
this https URL

本文提出一种针对分子关系学习中分布变化的稳健模型 CMRL，该模型基于领域知识并构建了结构因果模型（SCM），引入一种新的条件干预框架，并成功地从因果子结构中学习，并减轻了与化学反应表面相关的快捷子结构的混杂效应。