Drug-target affinity (DTA) prediction is central to computational drug discovery, yet many structure-aware models still weakly constrain geometric
locality and solvent mediation at the binding interface. We present WACA-DTA, a structure-conditioned affinity model that reformulates interface
matching as pose-conditioned atom-residue cross-attention with factorized interaction logits. Each cross-interface score is decomposed into
direct, geometric, and hydration-mediated terms, with geometry and hydration injected as additive logit-level priors rather than post hoc feature
concatenations. Under a fixed pair-pose protocol and identical structural preprocessing, 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
show that the gains are most consistent when pair-specific geometric and hydration cues are injected directly into the interaction logits. Our
claims are restricted to input-matched comparisons and within-dataset evaluation, and the hydration interpretation is cross-checked on PDBbind
complexes with retained crystallographic waters. Code, split definitions, and preprocessing manifests are available at https://github.com/khan1
14514/WACA-DTA2.

Co-authors: Chang Li, Jing Ji, Zimo Tang