No Ligand Binds in Isolation: Competition-Aware Bioactivity Prediction

Accurate prediction of drug-target bioactivity is central to early-stage drug discovery, yet existing deep learning models evaluate protein-ligand interactions in isolation, ignoring the competitive binding context that governs drug action in cellular environments. In radioligand displacement assays, observed bioactivity is modulated by the relative binding strength and spatial pose overlap between a test ligand and a competing radioligand within the same protein pocket—factors that conventional single-ligand models neglect. We present DrugComp, a competition-aware deep learning framework that explicitly encodes atom-level binding affinities and steric overlaps between competing ligands within shared protein binding pockets. DrugComp integrates seamlessly with diverse backbone architectures, including graph neural networks and transformers. To support competition-aware learning, we curate CompDB, a dataset of 10,675 radioligand displacement assays spanning 151 proteins, 181 radioligands, and 8,674 test ligands, enriched with docking-derived structural context. Evaluated in five-fold cross-validation, DrugComp consistently outperforms competition-blind baselines across all architectures, achieving up to 44.7% improvement in bioactivity prediction metrics. These results demonstrate that reframing bioactivity prediction as competitive-context modeling substantially improves predictive performance and better reflects the mechanistic basis of drug action.