基于邻域关系感知图神经网络的DDI预测

Drug-drug interaction prediction based on neighborhood relation-aware graph neural network

研究药物的相互作用DDI有助于临床用药与新药研发。现有的研究技术没有充分考虑药物知识图谱中药物实体与其他药物、靶标和基因等实体的拓扑结构,以及实体之间不同关系的语义重要性。针对这些问题,提出基于邻域关系感知的图神经网络模型NRAGNN预测药物的相互作用。首先,使用图注意力学习不同关系边的权重与特征表示,强化药物实体的语义特征;然后,生成药物实体周围不同层的邻域表示,捕获药物实体的拓扑结构特征;最后,将2种药物特征表示向量进行逐元素相乘得到药物相互作用分数。实验预测结果表明,提出的NRAGNN模型在KEGG药物数据集上的ACC、AUPR、AUC-ROC和F1指标分别达到了0.8994,0.9444,0.9567和0.9023,优于当前的其他模型。

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 entities and other entities such as drugs,targets,and genes in the drug knowledge graph,as well as the semantic importance of different relationships between entities.To solve these problems,this paper proposes 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 structure features of drug entities.Finally,the drug-drug interaction score is obtained by element-wise multiplication of the two drug feature representation vectors.Experimental results show that the proposed NRAGNN model achieves 0.8994,0.9444,0.9567,and 0.9023 in ACC,AUPR,AUC-ROC,and F1 indicators on the KEGG-DRUG dataset,respectively,outperforming other current models.