Escaping the Drug-Bias Trap: Using Debiasing Design to Improve Interpretability and Generalization of Drug-Target Interaction Prediction

Abstract

Considering the high cost associated with determining reaction affinities through in vitro experiments, virtual screening of potential drugs bound to specific protein pockets from vast compounds is critical in AI-assisted drug discovery. Deep-learning approaches have been proposed for predicting Drug-Target Interactions (DTIs). However, they have shown an overestimated accuracy due to the drug-bias trap, a challenge where traditional multimodal models overly rely on the drug branch while underutilizing protein information. This raises doubts about the interpretability and generalizability of existing DTI models. Therefore, we introduce UdanDTI, an innovative deep-learning architecture explicitly designed for predicting drug-protein interactions. UdanDTI applies an unbalanced dual-branch system and an attentive aggregation module to enhance interpretability from a biological perspective. Across various public datasets, UdanDTI demonstrates outstanding performance, outperforming state-of-the-art models under in-domain, cross-domain, and structural interpretability settings. Notably, it demonstrates exceptional accuracy in predicting drug responses of two crucial subgroups of Epidermal Growth Factor Receptor (EGFR) mutations associated with non-small cell lung cancer, consistent with experimental results. Meanwhile, UdanDTI could complement the advanced molecular docking software DiffDock.