基于图卷积神经网络的药物靶标作用关系预测方法

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 AUPRCArea Under the Precision-Recall Curve) values of the proposed method outperforms other four existing methods in the prediction of drug-target interaction on both DrugBankFDA and Yammanishi_08 datasets,and it preforms well on bigger datasets. The experimental results indicate that the proposed method improves the prediction performance of drug-target interaction on datasets with highly unbalanced samples. Furthermore, we validate novel(unknown) drug-target interactions which are predicted by GCN in biomedical databases.