AI RESEARCH
An Interpretable Framework Applying Protein Words to Predict Protein-Small Molecule Complementary Pairing Rules
arXiv CS.LG
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ArXi:2604.16550v1 Announce Type: new Despite the high accuracy of 'black box' deep learning models, drug discovery still relies on protein-ligand interaction principles and heuristics. To improve interpretability of protein-small molecule binding predictions, we developed the PWRules framework, which applies binding affinity data to identify privileged small molecule fragments and subsequently defines complementary pairing rules between these fragments and protein words (semantic sequence units) through an interpretability module.