Evidence map›Paper›PMID 40717088›Full record

ArticleBMC biology2025

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

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

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Article in BMC biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Yuqing Qian *Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Xin Zhang *Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, 324003, China.
Yizheng WangInstitute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Quan ZouInstitute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Chen CaoSchool of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, 211166, China. caochen@njmu.edu.cn.
Yijie DingYangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, 324003, China. wuxi_dyj@csj.uestc.edu.cn.
Xiaoyi GuoQuzhou People's Hospital, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou, 324000, China. kerry.guoxiaoyi@163.com.

Funding

China Scholarship Council program 202406070101Municipal Government of Quzhou 2023D018Municipal Government of Quzhou 2023D038National Natural Science Foundation of China 62172076Zhejiang Provincial Natural Science Foundation of China LY23F020003
6 · The paper itself

Abstract

backgroundDrug-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.

resultsWe propose a novel method termed DTI-RME. The DTI-RME introduces an innovative

conclusionsWe 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.

Indexed as

Computational BiologyPharmaceutical PreparationsPharmaceutical PreparationsDrug-target interactionEnsemble learningMulti-kernel learningRobustness loss

Identifiers

PMID40717088
PMCPMC12302742

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.