Where Black-box Drug-Target Interaction Prediction Models Look: Cross-Method Explainability
A new research paper explores the explainability of black-box drug-target interaction (DTI) prediction models, specifically auditing the BridgeDPI architecture. The study employs a combination of gradient-based attribution methods and feature-wise occlusion to understand how these models utilize sequence, fingerprint, and graph features. The findings suggest that explainability can serve as a critical tool for model evaluation, revealing issues like modality dominance, artifactual patterns, and dataset-specific behaviors, thereby generating hypotheses for further validation in drug discovery. AI
IMPACT Enhances understanding of AI model behavior in drug discovery, potentially leading to more reliable and interpretable computational drug design.