PACT: A Self-Explaining Scoring Function for Protein-Ligand Binding Affinity with Context-Dependent Interaction Terms

Structure-based affinity predictors return one number, and an explanation can be added afterward using post-hoc attributions or attention weights that are not expressed in affinity units and whose reliability is contested. Empirical scoring functions instead sum named physical terms but give each term one global weight, pricing a solvent-exposed hydrogen bond like a buried one. We introduce PACT, a predictor whose output is exactly decomposed into signed ligand-atom-residue contributions in pK over twelve interaction channels, with local chemistry and pocket context rescaling each contribution. Additivity determines how affinity is distributed over contacts, not whether that distribution can be trusted, so we evaluate the stability of the contributions under model retraining and contact removal. On a leakage-reduced split, PACT matches the accuracy of the strongest baselines we evaluated while producing faithful and reproducible contributions.