A Scalable and Robust Ensemble Deep Learning Method for Predicting Drug-Target Interactions

Accurate identification of drug-target interactions (DTIs) is a crucial step in drug discovery. Computational DTI prediction methods can significantly reduce the time and cost associated with drug development. However, effectively integrating multisource features for high-precision DTI prediction remains a challenge. In this study, we propose EDeepDTI, an ensemble deep learning framework designed to increase the accuracy and generalizability of DTI predictions by efficiently integrating multi-view features. EDeepDTI calculates multiple molecular fingerprints to extract rich substructural information from drugs, leverages several advanced pre-trained models to generate drug and protein features enriched with structural and semantic information, and calculates multiple semantic similarity features for drugs and proteins using various similarity measures. During the ensemble learning process, we design a deep learning base learner for each unique pairing of drug and protein features. This ensures that each base learner captures distinct feature interactions, enhancing both independence and diversity within the ensemble. Finally, a greedy strategy is employed to aggregate the predictions from all base learners to improve overall performance. The experimental results demonstrate that EDeepDTI and its variant consistently outperform the baseline methods across multiple datasets and prediction tasks, highlighting the superior performance, robustness, and scalability of EDeepDTI.