Causal-IQD-DTA: Counterfactual Interaction-Quality Disentanglement for Robust Drug–Target Affinity Prediction

Drug–target affinity (DTA) prediction estimates binding strength between compounds and protein targets, providing an early computational screen before expensive biochemical assays. Structure- aware DTA models now combine molecular, se- quence, structural, and conformer inputs, but complex-level affinity labels can entangle selec- tive binding evidence with abundance shortcuts such as ligand size, pocket size, and candidate contact mass. The central goal is robustness under abundance shortcuts: affinity predictions should depend on selective binding evidence rather than changes in contact opportunity alone. Causal- IQD-DTA addresses this bottleneck by separating these signals: zQ aggregates pocket-calibrated atom–residue evidence for binding, and zN en- codes size and contact-mass factors that describe the candidate interaction map. We then perform representation-level counterfactual intervention on zN while holding zQ fixed, suppressing affinity changes caused by nuisance abundance. Across Davis, KIBA, and PDBbind, the model improves random and transfer splits, with diagnostics show- ing lower abundance-residual coupling and re- duced counterfactual sensitivity.