Sequence-Driven Drug-Target Affinity Prediction Via Graph Attention Networks and Bidirectional Cross-Attention Fusion

Abstract

Accurate prediction of drug-target affinity (DTA) is a core challenge in computational drug discovery. Structure-based methods depend on experimentally determined protein coordinates, which are unavailable for most drug-relevant targets. sequence-only approaches, in turn, operate on linear residue representations and lack an explicit mechanism to encode the spatial proximity relationships that govern protein-ligand interactions. We present XAttn-DTA, a sequence-driven framework that addresses both limitations without requiring experimental structural data. Drug molecules are encoded as 2D molecular graphs via multilayer Graph Attention Networks (GATs), capturing atomic topology and bond-level chemistry. Proteins are represented as residue-level graphs constructed from ESM2-predicted contact maps, that captures inter-residue coevolutionary and structural signals embedded within the sequence. The bidirectional cross-attention fusion module projects both embeddings into a shared latent space and applies dual multi-head cross-attention. This enables ligand and protein residue environments to inform one another. On the Davis benchmark, XAttn-DTA achieves a concordance index (CI) of 0.907 and MSE of 0.175, improving CI by 1.8% and reducing MSE by 9.3% over the strongest baseline. On KIBA, it achieves an MSE of 0.121, a 13.6% reduction. Under three strict cold-start settings across Davis, KIBA, and BindingDB, the model yields MSE reductions of up to 79.0% and CI improvements of up to 31.5% over the strongest baseline, demonstrating strong generalization to unseen scaffolds and novel protein families.