WACA-DTA: Water-Aware Geometric Biases for Structure-Conditioned Drug-Target Affinity Prediction

Drug-target affinity (DTA) prediction is central to virtual screening, hit prioritization, and lead opti- mization because it estimates binding strength before costly experiments. However, current structure-aware DTA models often treat pocket geometry and solvent information as auxiliary features, leaving atom-residue matching weakly constrained by 3D locality, orientation, and water- mediated contacts. We present WACA-DTA, a structure-conditioned affinity model that refor- mulates interface matching as pose-conditioned atom-residue cross-attention with factorized di- rect, geometric, and hydration-mediated logits. Distance/orientation and HydraProt-derived hy- dration cues are injected as additive logit-level priors rather than post hoc features. Under a fixed pair-pose protocol and shared structural prepro- cessing, WACA-DTA improves over a matched pocket-aware baseline on Davis and KIBA across drug, target, and pair affinity cold-start splits, while controlled ablations across Davis, KIBA, and PDBbind indicate that the gains are most con- sistent when pair-specific geometry and hydration are injected directly into the interaction logits.