Heterogeneous Graph Convolutional Network integrates Multi-modal Similarities for Drug-Target Interaction Prediction

Abstract

Accurate identification of drug-target interactions (DTIs) play a crucial role in drug discovery. Conventional computational methods almost simply view heterogeneous networks which integrate diverse drug-related and target-related dataset instead of fully explored drug and protein similarities. In this paper, we propose a new method, named HGSDTI. Firstly, the low-dimensional features of drugs, proteins, diseases and side-effects are obtained by a denoising autoencoder. Then, we construct a heterogeneous network across drug, protein, disease and side-effect nodes, and a three-layer graph convolutional network (GCN) is applied to learn the neighbor topology information and integrate the low-dimensional features of nodes. Next, we calculate multi-modal drug similarities and protein similarities from multi-scale relations between drugs, proteins, diseases and side-effects. Finally, a multiple-layer convolutional neural network (CNN) deeply integrate similarity information of drugs and proteins with the neighbor topology information. Experiments have demonstrated its effectiveness and better performance than state-of-the-art methods.