Drug–target interaction predication via multi-channel graph neural networks

Li, Yang; Qiao, Guanyu; Wang, Keqi; Wang, Guohua

doi:10.1093/bib/bbab346

Cited by 114Computational Drug Discovery MethodsBioinformatics and Genomic NetworksAdvanced Graph Neural Networks

Drug–target interaction predication via multi-channel graph neural networks

Li, Yang, Qiao, Guanyu, Wang, Keqi, Wang, Guohua·2022·Briefings in Bioinformatics

DOI 10.1093/bib/bbab346

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Abstract

Drug-target interaction (DTI) is an important step in drug discovery. Although there are many methods for predicting drug targets, these methods have limitations in using discrete or manual feature representations. In recent years, deep learning methods have been used to predict DTIs to improve these defects. However, most of the existing deep learning methods lack the fusion of topological structure and semantic information in DPP representation learning process. Besides, when learning the DPP node representation in the DPP network, the different influences between neighboring nodes are ignored. In this paper, a new model DTI-MGNN based on multi-channel graph convolutional network and graph attention is proposed for DTI prediction. We use two independent graph attention networks to learn the different interactions between nodes for the topology graph and feature graph with different strengths. At the same time, we use a graph convolutional network with shared weight matrices to learn the common information of the two graphs. The DTI-MGNN model combines topological structure and semantic features to improve the representation learning ability of DPPs, and obtain the state-of-the-art results on public datasets. Specifically, DTI-MGNN has achieved a high accuracy in identifying DTIs (the area under the receiver operating characteristic curve is 0.9665).

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Authors: Li, Y., Qiao, G., Wang, K., & Wang, G.
Affiliations: Northeast Forestry University (CN)
Corresponding author: Wang, Guohua
ORCID: Li, Yang
Wang, Keqi
Wang, Guohua
Venue: Briefings in Bioinformatics
Publisher: Oxford University Press (OUP)
Published: 2021-09-03
Volume: 23
Issue: 1
ISSN: 1467-5463 (ISSN-L)
1477-4054
DOI: 10.1093/bib/bbab346
OpenAlex: W3206937279
Cited by: 112
References: 49
Citations by year: 2026: 9 · 2025: 26 · 2024: 37 · 2023: 29 · 2022: 11
FWCI: 10.24
Type: journal article
Language: en
Open access: closed
Licence: academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
Locations: Briefings in Bioinformatics (publishedVersion)
PubMed (publishedVersion)
Funders: National Natural Science Foundation of China (61806049, 61771165, 62072095)
Heilongjiang Postdoctoral Science Foundation (LBH-Z20104)
Topics: Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
Keywords: drug-target interaction prediction, graph attention networks, graph convolutional networks, drug discovery, node representation learning
MeSH terms: Drug Development, Drug Interactions, Drug Delivery Systems, Neural Networks, Computer, Drug Discovery

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Drug–target interaction predication via multi-channel graph neural networks

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