Drug Target Interaction Prediction Method Based on Graph Convolutional Neural Network

Drug-target interaction prediction plays an important role in drug discovery and repositioning.However, existing prediction methods have the problem of insufficient predictive performance while processing data with highly unbalance positive and negative samples.Therefore, a novel computational method based on graph convolutional neural network（GCN） for predicting drug-target interactions is proposed.In this method, a heterogeneous information network is constructed, which integrates diverse drug-related information and target-related information.From the heterogeneous information network, low-dimensional vector representation of features, which accurately explains the topological properties of individual and neighborhood feature information, is learned by using GCN and then prediction is made based on these representations via a vector space projection scheme.The AUPR（Area Under the Precision-Recall Curve） values of the proposed method outperforms other four existing methods in the prediction of drug-target i