AI RESEARCH
Representational Alignment with Chemical Induced Fit for Molecular Relational Learning
arXiv CS.LG
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ArXi:2502.07027v2 Announce Type: replace Molecular Relational Learning (MRL) is widely applied in natural sciences to predict relationships between molecular pairs by extracting structural features. The representational similarity between substructure pairs determines the functional compatibility of molecular binding sites. Nevertheless, aligning substructure representations by attention mechanisms lacks guidance from chemical knowledge, resulting in unstable model performance in chemical space (\textit{e.g.}, functional group, scaffold) shifted data.