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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Zeng, Xin Zhong, Kai-Yang Meng, Pei-Yan Li, Shu-Juan Lv, Shuang-Qing Wen, Meng-Liang Li, Yi |
| Abstract | Background Accurately identifying drug-target affinity (DTA) plays a pivotal role in drug screening, design, and repurposing in pharmaceutical industry. It not only reduces the time, labor, and economic costs associated with biological experiments but also expedites drug development process. However, achieving the desired level of computational accuracy for DTA identification methods remains a significant challenge. Results We proposed a novel multi-view-based graph deep model known as MvGraphDTA for DTA prediction. MvGraphDTA employed a graph convolutional network (GCN) to extract the structural features from original graphs of drugs and targets, respectively. It went a step further by constructing line graphs with edges as vertices based on original graphs of drugs and targets. GCN was also used to extract the relationship features within their line graphs. To enhance the complementarity between the extracted features from original graphs and line graphs, MvGraphDTA fused the extracted multi-view features of drugs and targets, respectively. Finally, these fused features were concatenated and passed through a fully connected (FC) network to predict DTA. Conclusions During the experiments, we performed data augmentation on all the training sets used. Experimental results showed that MvGraphDTA outperformed the competitive state-of-the-art methods on benchmark datasets for DTA prediction. Additionally, we evaluated the universality and generalization performance of MvGraphDTA on additional datasets. Experimental outcomes revealed that MvGraphDTA exhibited good universality and generalization capability, making it a reliable tool for drug-target interaction prediction. |
| Related Links | https://bmcbiol.biomedcentral.com/counter/pdf/10.1186/s12915-024-01981-3.pdf |
| Ending Page | 13 |
| Page Count | 13 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 17417007 |
| DOI | 10.1186/s12915-024-01981-3 |
| Journal | BMC Biology |
| Issue Number | 1 |
| Volume Number | 22 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2024-08-26 |
| Access Restriction | Open |
| Subject Keyword | Life Sciences Multi-view Drug-target affinity Graph deep model Line graphs Data augmentation |
| Content Type | Text |
| Resource Type | Article |
| Subject | Ecology, Evolution, Behavior and Systematics Structural Biology Biochemistry, Genetics and Molecular Biology Plant Science Biotechnology Physiology Agricultural and Biological Sciences Cell Biology Developmental Biology |
| Journal Impact Factor | 4.4/2023 |
| 5-Year Journal Impact Factor | 5.4/2023 |
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