Representational Alignment with Chemical Induced Fit for Molecular Relational Learning

In three linesReAlignFit, a molecular relational learning method, improves model stability by aligning substructure representations using chemical Induced Fit-based inductive bias. Tested on 9 datasets, it outperforms state-of-the-art models and enhances robustness to distribution shifts (rule-shifted and scaffold-shifted data).

Summary generated by Claude — human-verified