文献类型：期刊文献

中文题名：基于图卷积神经网络的药物靶标作用关系预测方法

英文题名：Drug Target Interaction Prediction Method Based on Graph Convolutional Neural Network

作者：高创[1];李建华[1,2];季秀怡[1];朱程龙[1];李诗良[2];李洪林[2]

机构：[1]华东理工大学信息科学与工程学院,上海200237;[2]上海市新药设计重点实验室,上海200237

年份：2021

卷号：48

期号：10

起止页码：127

中文期刊名：计算机科学

外文期刊名：Computer Science

收录：CSTPCD;;北大核心:【北大核心2020】；CSCD:【CSCD_E2021_2022】；

基金：国家重点研发计划项目(2016YFA0502304);国家重大新药创制项目(2018ZX09735002)。

语种：中文

中文关键词：图卷积神经网络;药物-靶标作用关系;异质信息网络;机器学习;向量表征

外文关键词：Graph convolutional neural networks;Drug-target interactions;Heterogeneous information network;Machine learning;Vector representation

摘要：药物-靶标作用关系预测在药物研发以及药物重定位中扮演着重要角色,但现有的机器学习方法在正负样本高度不平衡的数据上仍存在预测能力不足的问题。为此,提出一种基于图卷积神经网络的药物靶标作用关系预测方法。该方法首先构造一个结合多种药物(靶标)相关信息的异质信息网络,然后采用图卷积神经网络在此异质信息网络上学习得到能精确表达每个节点拓扑特征及邻居特征信息的低维向量表征,最后利用这些向量信息通过向量空间投影预测节点间概率的评分。在DrugBank_FDA和Yammanishi_08数据集上进行的药物-靶标作用关系预测的对比实验中,所提方法的AUPR(Area Under the Precision-Recall Curve)值都优于其他4种方法,并且在较大型数据集上也有较好的表现。实验结果表明,所提方法提高了样本高度不平衡时的药物-靶标作用关系预测性能;同时在生物药物数据库上的实验也验证了所提方法所发现的未知药物-靶标作用关系的有效性。
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 interaction on both DrugBank_FDA 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.