A generalizable deep learning framework for structure-based protein-ligand affinity ranking

Rapid and accurate estimation of protein-ligand binding affinities is crucial for early-stage drug discovery, yet hindered by a trade-off between the accuracy of gold-standard physics-based methods and the speed of simpler empirical scoring functions. Machine learning (ML) promised to bridge this gap, but its potential is unrealized due to limited model generalizability. Current ML models often fail when predicting affinities for novel proteins or chemical series unseen during training. We hypothesize that this failure stems from a competition within these models during training, where the learning of spurious correlations from structural motifs prevalent in the training data competes with the learning of transferable, physicochemical principles governing molecular interaction. Here, we introduce COnvolutional Representation of Distance-dependent Interactions with Attention Learning (CORDIAL), a deep learning framework designed with an inductive bias toward learning the distance-dependent physicochemical interaction signatures between proteins and ligands, explicitly avoiding direct parameterization of their chemical structures. This interaction-only approach proves effective. Through leave-superfamily-out validation that simulates encounters with novel protein families, we demonstrate that CORDIAL maintains predictive performance and calibration. This contrasts with diverse contemporary ML models, whose predictive ability is degraded under these conditions. Our results highlight the value of encoding appropriate task-specific physicochemical principles into ML architectures and offer a validated strategy for developing generalizable models for structure-based drug discovery.