Drug-drug interaction prediction based on neighborhood relation-aware graph neural network
Research on drug-drug interaction(DDI)is conducive to clinical medication and new drug development.Existing research technologies do not fully consider the topological structure of drug enti-ties and other entities such as drugs,targets,and genes in the drug knowledge graph,as well as the se-mantic importance of different relationships between entities.To solve these problems,this paper pro-poses a model based on neighborhood relation-aware graph neural network(NRAGNN)to predict DDI.Firstly,the graph attention network is utilized to learn the weights and feature representations of diffe-rent relationship edges,which enhances the semantic features of drug entities.Secondly,neighborhood representations for different layers around the drug entity are generated to capture the topological struc-ture features of drug entities.Finally,the drug-drug interaction score is obtained by element-wise multi-plication of the two drug feature representation vectors.Experimental results show that the proposed NRAGNN model achieves 0.899 4,0.944 4,0.956 7,and 0.902 3 in ACC,AUPR,AUC-ROC,and F1 indicators on the KEGG-DRUG dataset,respectively,outperforming other current models.
万方数据
维普
