Drug-target interaction prediction based on graph convolutional autoencoder with dynamic weighting residual GCN

Background The exploration of drug-target interactions (DTIs) is a critical step in drug discovery and drug repurposing. Recently, network-based methods have emerged as a prominent research area for predicting DTIs. These methods excel by extracting both topological and feature information from DTIs...

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Veröffentlicht in:	BMC bioinformatics Jg. 26; H. 1; S. 200 - 29
Hauptverfasser:	Zeng, Ming, Wang, Min, Xie, Fuqiang, Ji, Zhiwei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 29.07.2025 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Algorithms Autoencoder Bioinformatics Bioinformatics and chemoinformatics in drug discovery Biomedical and Life Sciences Case studies Computational Biology - methods Computational Biology/Bioinformatics Computer Appl. in Life Sciences Datasets Deep learning Drug development Drug discovery Drug Discovery - methods Drug interactions Drug Repositioning Drug-target interaction Dual self-supervised joint training mechanism Dynamic weighting convolutional residual connection Generative adversarial network Graph convolutional autoencoder Graph neural networks Humans Learning Life Sciences Ligands Machine learning Microarrays Neural networks Neural Networks, Computer Optimization Pandemics Pharmaceutical research Predictions Proteins R&D Representations Research & development Technology application Training Weighting China New York Dynamic weighting convolutional residual connection Graph convolutional autoencoder Dual self-supervised joint training mechanism Drug-target interaction Generative adversarial network
ISSN:	1471-2105, 1471-2105
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Zusammenfassung:	Background The exploration of drug-target interactions (DTIs) is a critical step in drug discovery and drug repurposing. Recently, network-based methods have emerged as a prominent research area for predicting DTIs. These methods excel by extracting both topological and feature information from DTIs networks, thereby achieving superior DTIs prediction performance. However, the majority of existing GCN-based methods utilize shallow graph neural networks, which are incapable of extracting higher-level semantic information. Additionally, the current training of models lacks an effective guiding mechanism, leading to the insufficient improvement of network’s representation capabilities. Results In this paper, we propose a graph convolutional autoencoder model, named DDGAE, for DTIs prediction. We develop a DWR-GCN module, which incorporates dynamic weighting graph convolution with residual connection, to improve the representation capability for DTI heterogeneous networks. Further, to improve the learning efficiency of the model, we devise a dual self-supervised joint training mechanism. Specifically, this mechanism integrates DWR-GCN and a graph convolutional autoencoder into a cohesive system, enhancing both the learning performance and stability of DDGAE. Conclusion Experimental results show that DDGAE significantly outperforms several SOTA models in DTIs prediction, achieving optimal performance and the reliability of our method is verified by case study.
Bibliographie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1471-2105 1471-2105
DOI:	10.1186/s12859-025-06198-x