Drug-target interaction prediction using multi-head self-attention and graph attention network.

Identifying drug-target interactions (DTIs) is an important step in the process of new drug discovery and drug repositioning. Accurate predictions for DTIs can improve efficiency in drug discovery and development. Although rapid advances in deep learning technologies have generated various computational methods, it is still appealing to further investigate how to design efficient networks for predicting DTIs. In this study, we propose an end-to-end deep learning method (called MHSADTI) to predict DTIs based on the graph attention network and multi-head self-attention mechanism models. First, the characteristics of drugs and proteins are extracted by the graph attention network model and multi-head self-attention mechanism, respectively. Then, the attention scores are used to consider which amino acid subsequence in a protein is more important for the drug to predict its interactions. Finally, we predict DTIs by a fully connected layer network after obtaining the feature vectors of drugs and proteins. The experiments in four datasets, human, C.elegans, DUD-E and DrugBank show our method outperforms the state-of-the-art method in terms of AUC, Precision, Recall, Recall, AUPR and F1-score. In addition, the case studies further demonstrate that our method can provide effective visualization to interpret the prediction results from biological insights.