Graph-Based Cross-Modal Learning for Drug–Target Affinity Prediction

Drug-target binding affinity prediction is a fundamental task in structure-based drug discovery. But, in general, sequence- and structure-based deep learning methods lack explicit modeling of the physicochemical interactions between drug atoms and protein residues at the binding site. We present HeteroBindNet, a heterogeneous graph neural network that combines 1) GINE-based convolutions over RDKit-derived atomic graphs, 2) GCN-based protein contact graph learning over ESM-2 contact maps, 3) auxiliary global features enriched by Morgan fingerprints, 4) multi-scale 1-D CNN sequence encoders within a shared embedding space, and 5) a novel cross-modal HeteroBindNet which learns an atom-residue interaction matrix through scaled, temperature-gated attention and bidirectional gated message passing, capturing the local structural complementarity that governs binding. Even without any binding-site supervision, this interaction matrix is able to identify a subset of ligand atoms and protein residues (top 5%) that are enriched near known binding regions, consistent with established pharmacophoric contacts. Across multiple evaluation splits, HeteroBindNet achieves a CI of 0.791 and MSE of 0.347 on the KIBA benchmark cold-drug split, outperforming the baseline model, DMFF-DTA, by 14.95% on MSE and improving CI by 5.05%, with consistent performance on the Davis benchmark and strong generalization in cold-start scenarios, demonstrating its utility for real-world drug discovery applications.