ISAAC: Prior-aligned structural sensitivity auditing for drug–target interaction models
Barbara Tarantino, Sun Kim, Yijingxiu Lu, Paolo Giudici · Artificial Intelligence in the Life Sciences · 2026
Deep learning models for drug–target interaction (DTI) prediction often achieve strong benchmark performance while relying on input patterns that are not captured by standard accuracy-based evaluation. We introduce ISAAC (Intervention-based Structural Auditing ApproaCh for Model Evaluation), a post-hoc framework that evaluates prior-relative structural sensitivity by probing frozen models through matched prior-aligned and prior-misaligned input-level interventions, independently of predictive accuracy. Applied to three sequence-based DTI architectures on the Davis kinase benchmark, ISAAC reveals statistically significant differences in prior-alignment ratios across models operating within a comparable predictive regime, differences that are not explained by predictive performance alone and that are robust to the choice of perturbation operator. These discrepancies are not reflected by the predictive ranking and motivate the use of post-hoc structural auditing as a complement to standard performance evaluation in DTI modeling and, more broadly, in scientific machine learning settings where structured priors are available.