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

作　　者：Ming CHEN Yajian JIANG Xiujuan LEI Yi PAN Chunyan JI Wei JIANG 

机构地区：[1]College of Information Science and Engineering,Hunan Normal University,Changsha 410081,China [2]School of Computer Science,Shaanxi Normal University,Xi’an 710119,China [3]Faculty of Computer Science and Control Engineering,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen 518055,China [4]Computer Science Department,BNU-HKBU United International College,Zhuhai 519087,China

出　　处：《Chinese Journal of Electronics》2024年第1期231-244,共14页电子学报（英文版）

基　　金：supported by the Shenzhen Science and Technology Program(Grant No.KQTD20200820113106007);the National Natural Science Foundation of China(Grant No.U22A2041,61972451,and 62272288);the Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science,BNUHKBU United International College(Grant No.2022B1212010006);the Scientific Research Fund of Hunan Provincial Education Department of China(Grant No.22B0097);the Changsha Natural Science Foundation of China(Grant No.kq2202248)。

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 between 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 extracting interactions within drug pairs and target protein pairs.We propose signed heterogeneous graph neural networks(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 passing 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 Drug Bank and related databases,under different settings of initial inputs,embedding dimensions and training modes.The prediction results show excellent performance in terms of metric indicators,and the feasibility is further verified by the case study with two drugs on breast cancer.

关 键 词：Drug-target interactions Signed heterogeneous network Link sign prediction Graph neural networks 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] R969.2[自动化与计算机技术—控制科学与工程]