Drug-target interaction (DTI) prediction is of great practical value for discovering, developing, and repurposing drugs, which has tremendous advantages to pharmaceutical industries and patients. However, the prediction of DTIs using wet-lab experimental methods is generally expensive and time-consuming. To date, numerous machine learning-based approaches show promising performance, which greatly improves DTI discovery efficiency, but there are still two challenges remained. One is how to represent the spatial structure features of drugs and targets appropriately, and the other is how to explicitly and effectively model and learn local interactions between drugs and targets, for better interpretation and prediction. In this work, we propose a novel framework that combines a transformer and graph attention convolutional network for DTI prediction (TransGAT-DTI), which not only precisely predicts the putative DTIs with satisfactory overall performance on three benchmark datasets, but also captures the pivotal sequence that contributes the most to the positive predictions. Experimental results demonstrate that the proposed TransGAT-DTI achieves the best performance on BindingDB, BioSNAP, and Human datasets. Importantly, the proposed approach provides a novel solution for discovering and developing novel drugs.
Zhou et al. (Fri,) studied this question.