Orthogonal Evaluations Enable More Robust Predictions of Protein-Ligand Interactions

Computational models can predict protein‑ligand interactions (PLIs) at scales that far surpass experimental validation, which makes reliable confidence estimation critical. Existing approaches use protein structure and function as complementary, independently derived comparators for predicting and evaluating PLIs. However, function‑based evaluations perform poorly for promiscuous ligands, which target proteins with diverse functions. Accordingly, confidence estimation for modeled PLIs involving promiscuous ligands remains an open challenge. To address this gap, we introduce a novel physicochemical representation as an additional comparator for evaluating PLIs. Our representation encodes binding-pocket-specific features along a protein's surface, which influence its affinity for a ligand. In preliminary experiments on PLIs involving promiscuous ligands, we find that incorporating these features yields more robust confidence estimates compared to using structure and function alone. These results suggest that physicochemical representations capture meaningful biological signals for prioritizing high-quality drug leads, motivating a multimodal evaluation framework for drug discovery.