Representational Alignment with Chemical Induced Fit for Molecular Relational Learning

The paper introduces ReAlignFit, a novel approach to Molecular Relational Learning (MRL) that enhances substructure representation alignment by incorporating a chemical Induced Fit-based inductive bias. This method employs a Bias Correction Function for substructure edge reconstruction and integrates the Subgraph Information Bottleneck to improve the generation of molecular embeddings, resulting in superior performance on nine datasets compared to existing models. This advancement is significant for practitioners as it offers a more stable and chemically informed framework for predicting molecular interactions, particularly in variable chemical spaces.