Drug-Target Interactions Prediction Based on Signed Heterogeneous Graph Neural Networks

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外文摘要：Drug-target interactions(DTIs)prediction plays an important role in the process of drug discovery.Most computational methods treat it as a binary prediction problem,determining whether there are connections be-tween drugs and targets while ignoring relational types information.Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target,in this work,we model DTIs on signed heterogeneous networks,through categorizing interaction patterns of DTIs and additionally ex-tracting interactions within drug pairs and target protein pairs.We propose signed heterogeneous graph neural net-works(SHGNNs),further put forward an end-to-end framework for signed DTIs prediction,called SHGNN-DTI,which not only adapts to signed bipartite networks,but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs)and protein-protein interactions(PPIs).For the framework,we solve the message pass-ing and aggregation problem on signed DTI networks,and consider different training modes on the whole networks consisting of DTIs,DDIs and PPIs.Experiments are conducted on two datasets extracted from DrugBank and relat-ed databases,under different settings of initial inputs,embedding dimensions and training modes.The prediction re-sults show excellent performance in terms of metric indicators,and the feasibility is further verified by the case study with two drugs on breast cancer.

外文关键词：

Drug-target interactionsSigned heterogeneous networkLink sign predictionGraph neural net-works

作者：

Ming CHEN、Yajian JIANG、Xiujuan LEI、Yi PAN、Chunyan JI、Wei JIANG

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作者单位：

College of Information Science and Engineering,Hunan Normal University,Changsha 410081,China

School of Computer Science,Shaanxi Normal University,Xi'an 710119,China

Faculty of Computer Science and Control Engineering,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China

Computer Science Department,BNU-HKBU United International College,Zhuhai 519087,China

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基金：

Shenzhen Science and Technology ProgramNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaGuangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science,BNU-HKBU United InternatiScientific Research Fund of Hunan Provincial Education Department of ChinaChangsha Natural Science Foundation of China

项目编号：

KQTD20200820113106007U22A204161972451622722882022B121201000622B0097kq2202248

出版年：

2024

DOI：

10.23919/cje.2022.00.384

电子学报(英文)

CSTPCDEI

ISSN：1022-4653

年,卷(期)：2024.33(1)

参考文献量49