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

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...

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Bibliographic Details
Published in:BMC bioinformatics 2024-01, Vol.25 (1), p.10-10, Article 10
Main Authors: Wei, Jiahao, Lu, Linzhang, Shen, Tie
Format: Article
Language:English
Subjects:
Analysis
Artificial neural networks
Case studies
Computational biology
Deep learning
Disease
Drug discovery
Drugs
Drug–target interactions
Evaluation
Graph attention networks
Graph neural networks
Ligands
Management
Methods
Neural networks
Protein interaction
Proteins
Residual graph convolutional neural networks
Semantics
Topology
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Summary: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.
ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-023-05620-6