Predicting drug-protein interactions by preserving the graph information of multi source data

被引:3
|
作者
Wei, Jiahao [1 ]
Lu, Linzhang [1 ,2 ]
Shen, Tie [3 ]
机构
[1] Guizhou Normal Univ, Sch Math Sci, Guiyang 550025, Peoples R China
[2] Xiamen Univ, Sch Math Sci, Xiamen 361005, Peoples R China
[3] Guizhou Normal Univ, Key Lab Informat & Comp Sci Guizhou Prov, Guiyang 550001, Guizhou, Peoples R China
基金
中国国家自然科学基金;
关键词
Drug-target interactions; Graph attention networks; Residual graph convolutional neural networks; TARGET INTERACTIONS; PHARMACOLOGY;
D O I
10.1186/s12859-023-05620-6
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Examining potential drug-target interactions (DTIs) is a pivotal component of drug discovery and repurposing. Recently, there has been a significant rise in the use of computational techniques to predict DTIs. Nevertheless, previous investigations have predominantly concentrated on assessing either the connections between nodes or the consistency of the network's topological structure in isolation. Such one-sided approaches could severely hinder the accuracy of DTI predictions. In this study, we propose a novel method called TTGCN, which combines heterogeneous graph convolutional neural networks (GCN) and graph attention networks (GAT) to address the task of DTI prediction. TTGCN employs a two-tiered feature learning strategy, utilizing GAT and residual GCN (R-GCN) to extract drug and target embeddings from the diverse network, respectively. These drug and target embeddings are then fused through a mean-pooling layer. Finally, we employ an inductive matrix completion technique to forecast DTIs while preserving the network's node connectivity and topological structure. Our approach demonstrates superior performance in terms of area under the curve and area under the precision-recall curve in experimental comparisons, highlighting its significant advantages in predicting DTIs. Furthermore, case studies provide additional evidence of its ability to identify potential DTIs.
引用
收藏
页数:17
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