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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. With theoretical justification, we propose the \\textbf{Re}presentational \\textbf{Align}ment with Chemical Induced \\textbf{Fit} (ReAlignFit) to enhance the stability of MRL. ReAlignFit dynamically aligns substructure representation in MRL by introducing chemical Induced Fit-based inductive bias. In the induction process, we design the Bias Correction Function based on substructure edge reconstruction to align representati","title":"Representational Alignment with Chemical Induced Fit for Molecular Relational Learning","url":"https://arxiv.org/abs/2502.07027","vendor":"arxiv_cs_ai"},"summary":"arXiv:2502.07027v4 Announce Type: replace-cross \nAbstract: 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. With theoretical justification, we propose the \\textbf{Re}presentational \\textbf{Align}ment with Chemical Induced \\textbf{Fit} (ReAlignFit) to enhance the stability of MRL. ReAlignFit dynamically aligns substructure representation in MRL by introducing chemical Induced Fit-based inductive bias. 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