Explainable Physicochemical Determinants of Protein Ligand Binding via Non-Covalent Interactions
Protein-ligand binding governs enzymatic catalysis, metabolic homeostasis, and therapeutic modulation. Thus, the accurate prediction of these interactions underpins modern rational drug discovery. However, existing deep-learning frameworks largely operate as black-box predictors that fail to resolve the individual residues mediating binding or decode the fundamental non-covalent forces that drive molecular recognition. To address these limitations, we present ExplainBind, an interaction-aware framework that predicts binding likelihood, localizes specific binding residues at single-amino-acid resolution rather than coarse pocket-level regions, and decodes the underlying non-covalent interaction patterns, all zero-shot, without requiring prior three-dimensional structural inputs. To support residue- and interaction-level training and evaluation, we construct InteractBind, a protein-ligand benchmark with residue-atom interaction maps. Simultaneously, benchmarking experiments demonstrate that ExplainBind consistently outperforms state-of-the-art baselines across diverse protein and ligand spaces, maintaining high precision when generalized to entirely novel sequences and chemical scaffolds. When applied to two unseen therapeutic targets, ExplainBind successfully ranks potent angiotensin-converting enzyme (ACE) inhibitors and clarifies differences in their potency via affinity-stratified interaction landscapes. Furthermore, we demonstrate the prospective utility of ExplainBind by discovering novel inhibitors and activators of L-2-hydroxyglutarate dehydrogenase (L2HGDH) through wet-lab validation, with mechanistically distinct interaction profiles providing a clear molecular rationale for their divergent functional outcomes. Collectively, these results establish ExplainBind as a powerful, generalizable tool for mechanistically informed, interpretable drug discovery.