DTI-RME: a robust and multi-kernel ensemble approach for drug-target interaction prediction
Abstract Background Drug-target interaction (DTI) refers to the specific mechanisms by which drug molecules interact with biological targets within a biological system. Computational methods are widely employed for DTI prediction, as they are time-efficient and resource-saving compared to experiment...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-07-01
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| Series: | BMC Biology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12915-025-02340-6 |
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| Summary: | Abstract Background Drug-target interaction (DTI) refers to the specific mechanisms by which drug molecules interact with biological targets within a biological system. Computational methods are widely employed for DTI prediction, as they are time-efficient and resource-saving compared to experimental approaches. Although numerous DTI prediction methods have achieved promising results, accurately modeling DTIs remains challenging due to three key issues: noisy interaction labels, ineffective multi-view fusion, and incomplete structural modeling. Results We propose a novel method termed DTI-RME. The DTI-RME introduces an innovative $$L_2-C$$ L 2 - C loss function that combines the benefits of $$L_2$$ L 2 loss to reduce prediction errors and the robustness of C-loss in handling outliers. This method fuses multiple views through multi-kernel learning that assigns weights to different kernels. DTI-RME uses ensemble learning to assume and learn multiple structures, including the drug-target pair, drug, target, and low-rank structures. Conclusions We evaluated DTI-RME on five real-world DTI datasets and conducted experiments focusing on three key scenarios. In all experiments, DTI-RME demonstrated superior performance compared to existing methods. Furthermore, the case study confirmed DTI-RME’s ability to identify novel drug-target interactions accurately, with 17 of the top 50 predicted interactions being validated. |
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| ISSN: | 1741-7007 |