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

ABSTRACT: 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 in

SUBMITTER: Brown BP 

PROVIDER: S-EPMC12557521 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature