Tracing Protein–Ligand Predictions Back to Atomic Contacts, Binding Context, Structural Alternatives, and Model Uncertainty Download PDF

Computational models can assign credible binding-affinity or interaction predictions to protein–ligand complexes while providing little defensible information about the atomic relationships, hydration patterns, chemical microstates, or conformational states on which those predictions depend. This distinction limits the scientific interpretation of model outputs in pharmaceutical research because a numerically accurate prediction does not necessarily identify a transferable binding hypothesis or explain how a molecular modification may alter affinity or selectivity. This Original Molecular Evidence-Tracing Article proposes the Protein–Ligand Molecular Evidence Trace, a conceptual record that connects a versioned model output to atomic-contact hypotheses, binding-context assumptions, structural alternatives, evidence provenance, and multidimensional uncertainty. The approach treats direct contacts, water-mediated interactions, protonation and tautomer states, ligand poses, receptor conformations, and model-derived contributions as related but non-interchangeable evidence objects. It further distinguishes model faithfulness from mechanistic confirmation and separates numerical confidence from uncertainty about structural state, applicability, and external evidence quality. The proposed construct is intended to support later perturbation testing, counterfactual analysis, structural corroboration, and bounded interpretation in hit assessment, selectivity reasoning, and lead optimization. Its principal contribution is not a new affinity predictor, but an explicit evidentiary architecture for showing what a prediction refers to, which structural explanation it assumes, what alternatives remain plausible, and which claims are scientifically admissible. The construct remains conceptual and requires architecture-specific, system-specific, and prospective validation. It cannot establish binding mechanism, causal interaction importance, experimental confirmation, pharmaceutical developability, clinical utility, or operational readiness on its own.