EnsDTI: Predicting Drug-Target Interaction With Mixture-of-Experts and Confidence Assessment

Accurately identifying drug-target interactions (DTIs) is a critical step in drug discovery. While structure-based drug design methods demonstrate impressive docking prediction accuracy, their heavy computational demands and resource-intensive nature make them impractical for directly processing vast chemical spaces containing a large number of compounds. This limitation highlights the need for a computational tool that balance speed and accuracy to rank and filter potential drug candidates efficiently. In contrast, existing ligand-based drug design methods, which learn representations from diverse protein and molecule features, often fail to make consistent predictions on unseen data or external databases, limiting their applicability for ranking and filtering potential drug candidates accurately. To address these challenges, we propose EnsDTI, a novel framework that bridges the gap between structure-based and ligand-based drug design approaches. EnsDTI utilizes a mixture-of-experts architecture to enhance DTI predictions using existing deep learning models and incorporates an inductive conformal predictor to assess prediction quality with confidence scores, ensuring reliability. Experimental results on four widely used benchmark datasets show that EnsDTI consistently achieves high performance in both prediction accuracy and confidence estimation. In addition, its candidate rankings correlate well with actual docking affinities, suggesting its practical utility in drug discovery.