Exploration of the correlation between GPCRs and drugs based on a learning to rank algorithm.

Algorithms Machine Learning Pharmaceutical Preparations Receptors, G-Protein-Coupled
Candidate compound Drug correlation GPCR Learning to rank Potential target

Journal

Computers in biology and medicine
ISSN: 1879-0534
Titre abrégé: Comput Biol Med
Pays: United States
ID NLM: 1250250

Informations de publication

Date de publication:
04 2020
Historique:
received: 18 12 2019
revised: 04 02 2020
accepted: 12 02 2020
pubmed: 25 2 2020
medline: 22 6 2021
entrez: 25 2 2020
Statut: ppublish

Résumé

Exploring the protein - drug correlation can not only solve the problem of selecting candidate compounds but also solve related problems such as drug redirection and finding potential drug targets. Therefore, many researchers have proposed different machine learning methods for prediction of protein-drug correlations. However, many existing models simply divide the protein-drug relationship into related or irrelevant categories and do not deeply explore the most relevant target (or drug) for a given drug (or target). In order to solve this problem, this paper applies the ranking concept to the prediction of the GPCR (G Protein-Coupled Receptors)-drug correlation. This study uses two different types of data sets to explore candidate compound and potential target problems, and both sets achieved good results. In addition, this study also found that the family to which a protein belongs is not an inherent factor that affects the ranking of GPCR-drug correlations; however, if the drug affects other family members of the protein, then the protein is likely to be a potential target of the drug. This study showed that the learning to rank algorithm is a good tool for exploring protein-drug correlations.

Identifiants

DOI: 10.1016/j.compbiomed.2020.103660 PMID: 32090901
pubmed: 32090901
pii: S0010-4825(20)30054-8
doi: 10.1016/j.compbiomed.2020.103660
pii:
doi:

Substances chimiques

Pharmaceutical Preparations 0
Receptors, G-Protein-Coupled 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

103660

Informations de copyright

Copyright © 2020 Elsevier Ltd. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest None Declared.

Auteurs

Xiaoqing Ru (X)

Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China; School of Information and Electrical Engineering, Hebei University of Engineering, Handan, China.

Lida Wang (L)

Scientific Research Department, Heilongjiang Agricultural Recalmation General Hospital, Harbin, China. Electronic address: 427334@qq.com.

Lihong Li (L)

School of Information and Electrical Engineering, Hebei University of Engineering, Handan, China.

Hui Ding (H)

Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.

Xiucai Ye (X)

Department of Computer Science, University of Tsukuba, Tsukuba Science City, Japan.

Quan Zou (Q)

Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China; Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China. Electronic address: zouquan@nclab.net.

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Classifications MeSH

questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Exploration of the correlation between GPCRs...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Apprentissage machine › Exploration of the correlation between GPCRs...
questionsmedicales.fr › Préparations pharmaceutiques › Exploration of the correlation between GPCRs...
questionsmedicales.fr › Acides aminés, peptides et protéines › Protéines › Protéines membranaires › Récepteurs de surface cellulaire › Récepteurs couplés aux protéines G › Exploration of the correlation between GPCRs...