Drug-target interaction prediction based on graph convolutional autoencoder with dynamic weighting residual GCN
Abstract 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...
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| Format: | Article |
| Language: | English |
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BMC
2025-07-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-025-06198-x |
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| _version_ | 1849341936133996544 |
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| author | Ming Zeng Min Wang Fuqiang Xie Zhiwei Ji |
| author_facet | Ming Zeng Min Wang Fuqiang Xie Zhiwei Ji |
| author_sort | Ming Zeng |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-c9b441325e6d409dbc981ebfca8af0c6 |
| institution | Kabale University |
| issn | 1471-2105 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj-art-c9b441325e6d409dbc981ebfca8af0c62025-08-20T03:43:31ZengBMCBMC Bioinformatics1471-21052025-07-0126112910.1186/s12859-025-06198-xDrug-target interaction prediction based on graph convolutional autoencoder with dynamic weighting residual GCNMing Zeng0Min Wang1Fuqiang Xie2Zhiwei Ji3School of Mathematics and Computer Science, Gannan Normal UniversitySchool of Intelligent Manufacturing and Future Energy, Gannan Noraml UniversitySchool of Mathematics and Computer Science, Gannan Normal UniversityCollege of Artificial Intelligence, Nanjing Agricultural UniversityAbstract 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.https://doi.org/10.1186/s12859-025-06198-xDrug-target interactionDynamic weighting convolutional residual connectionDual self-supervised joint training mechanismGraph convolutional autoencoderGenerative adversarial network |
| spellingShingle | Ming Zeng Min Wang Fuqiang Xie Zhiwei Ji Drug-target interaction prediction based on graph convolutional autoencoder with dynamic weighting residual GCN BMC Bioinformatics Drug-target interaction Dynamic weighting convolutional residual connection Dual self-supervised joint training mechanism Graph convolutional autoencoder Generative adversarial network |
| title | Drug-target interaction prediction based on graph convolutional autoencoder with dynamic weighting residual GCN |
| title_full | Drug-target interaction prediction based on graph convolutional autoencoder with dynamic weighting residual GCN |
| title_fullStr | Drug-target interaction prediction based on graph convolutional autoencoder with dynamic weighting residual GCN |
| title_full_unstemmed | Drug-target interaction prediction based on graph convolutional autoencoder with dynamic weighting residual GCN |
| title_short | Drug-target interaction prediction based on graph convolutional autoencoder with dynamic weighting residual GCN |
| title_sort | drug target interaction prediction based on graph convolutional autoencoder with dynamic weighting residual gcn |
| topic | Drug-target interaction Dynamic weighting convolutional residual connection Dual self-supervised joint training mechanism Graph convolutional autoencoder Generative adversarial network |
| url | https://doi.org/10.1186/s12859-025-06198-x |
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