基于符号图谱与卷积网络的药物互作用关系预测  

DRUG-DRUG INTERACTIONS PREDICTION BASED ON SIGNED GRAPH SPECTRUM AND CONVOLUTIONAL NETWORKS

作　　者：陈明 钟世杰 刘先锋 肖球 Chen Ming;Zhong Shijie;Liu Xianfeng;Xiao Qiu(College of Information Science and Engineering,Hunan Normal University,Changsha 410081,Hunan,China)

机构地区：[1]湖南师范大学信息科学与工程学院,湖南长沙410081

出　　处：《计算机应用与软件》2025年第2期81-87,共7页Computer Applications and Software

基　　金：国家自然科学基金项目(11871210,62002116);湖南省教育厅科学研究项目优秀青年项目(20B348)。

摘　　要：药物相互作用可能会引起未知的风险甚至严重的不良反应,当前流行的检测方法耗时且昂贵。最近兴起的图神经网络在药物互作用预测上取得了显著提升效果,但大多数方法所建模的非负图只适用于同质关系。药物间的一些语义关系,如减弱效应或药物不良反应,实为异质关系,可描述为负边。提出基于符号网络的药物互作用关系预测方法,它利用拉普拉斯矩阵的谱分解和符号图卷积对药物节点进行嵌入表达,并采用问题依赖的损失函数,端对端地训练网络模型。在两个预测问题的三个测试数据集上进行对比实验,结果表明该方法在各个评价指标上都展现出了较好效果。Drug-drug interactions(DDIs)may give rise to the risk of unanticipated adverse effects,but current DDIs detection is expensive and time-consuming.Recently,graph neural network has achieved significant improvement in DDIs prediction,but the non-negative graph modeled by most methods adapts to assortative relations.Some semantic relationships between drugs,such as degressive effects or adverse side reactions,are actually disassortative relations,which can be described as negative edges.In this study,a method based on signed network was proposed for DDIs prediction.The drug nodes were embedded through a signed graph convolutional network which took the spectral decomposition of the signed Laplacian as the initial input.The problem-specific loss function was used to end-to-end training network model.Through comparative experiments on three test datasets of two prediction problems,it is verified that our method shows good performance in term of evaluation metrics.

关 键 词：药物相互作用 符号图拉普拉斯矩阵 符号图卷积 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]