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 <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12915_2025_2340_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="56" /> </InlineMediaObject> <EquationSource Format="TEX">$L_2-C$</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <msub> <mi>L</mi> <mn>2</mn> </msub> <mo>-</mo> <mi>C</mi> </mrow> </math></EquationSource> </InlineEquation> loss function that combines the benefits of <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12915_2025_2340_Article_IEq2.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">$L_2$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>L</mi> <mn>2</mn> </msub> </math></EquationSource> </InlineEquation> 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.

DTI-RME: a robust and multi-kernel ensemble approach for drug-target interaction prediction

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2025年7月28日

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Yuqing Qian,
Xin Zhang,
Yizheng Wang,
Quan Zou,
Chen Cao,
Yijie Ding,
Xiaoyi Guo

摘要

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.