Mapping Non-Homologous Pocket Compatibilities to Identify Hidden Drug-Target Relationships: A Pocket Hopping Framework

Predicting small molecule-protein interactions across nonhomologous proteins remains challenging because shared ligand recognition is often not evident from sequence, fold, or pocket similarity. Here, we introduce pocket hopping, a machine-learning framework that learns residue-level interaction patterns from coligand binding pockets and infers compatibility between nonhomologous pockets for similar chemotypes. Using shared ligands as supervision rather than explicit geometric alignment, pocket hopping identifies pocket relationships that are not readily captured by conventional chemical-, sequence-, or structure-based comparisons. In two case studies, pocket hopping demonstrates broad utility in drug discovery by enabling de novo hit identification and mechanistic interpretation, identifying fedratinib and its analogues as helicase WRN inhibitors. The model also identified the clinical-stage HDAC inhibitor abexinostat as a direct ENPP1 binder and inhibitor, and cellular assays showed enhanced cGAMP-STING signaling under cGAMP stimulation. Together, these results indicate that pocket-level compatibility can complement existing approaches for target identification, hit discovery, and polypharmacology analysis.