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Drug repositioning in drug discovery of T2DM and repositioning potential of antidiabetic agents

Repositioning or repurposing drugs account for a substantial part of entering approval pipeline drugs, which indicates that drug repositioning has huge market potential and value. Computational technologies such as machine learning methods have accelerated the process of drug repositioning in the la...

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Detalles Bibliográficos
Autores principales: Zhu, Sha, Bai, Qifeng, Li, Lanqing, Xu, Tingyang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9189996/
https://www.ncbi.nlm.nih.gov/pubmed/35765655
http://dx.doi.org/10.1016/j.csbj.2022.05.057
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author Zhu, Sha
Bai, Qifeng
Li, Lanqing
Xu, Tingyang
author_facet Zhu, Sha
Bai, Qifeng
Li, Lanqing
Xu, Tingyang
author_sort Zhu, Sha
collection PubMed
description Repositioning or repurposing drugs account for a substantial part of entering approval pipeline drugs, which indicates that drug repositioning has huge market potential and value. Computational technologies such as machine learning methods have accelerated the process of drug repositioning in the last few decades years. The repositioning potential of type 2 diabetes mellitus (T2DM) drugs for various diseases such as cancer, neurodegenerative diseases, and cardiovascular diseases have been widely studied. Hence, the related summary about repurposing antidiabetic drugs is of great significance. In this review, we focus on the machine learning methods for the development of new T2DM drugs and give an overview of the repurposing potential of the existing antidiabetic agents.
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spelling pubmed-91899962022-06-27 Drug repositioning in drug discovery of T2DM and repositioning potential of antidiabetic agents Zhu, Sha Bai, Qifeng Li, Lanqing Xu, Tingyang Comput Struct Biotechnol J Short Review Repositioning or repurposing drugs account for a substantial part of entering approval pipeline drugs, which indicates that drug repositioning has huge market potential and value. Computational technologies such as machine learning methods have accelerated the process of drug repositioning in the last few decades years. The repositioning potential of type 2 diabetes mellitus (T2DM) drugs for various diseases such as cancer, neurodegenerative diseases, and cardiovascular diseases have been widely studied. Hence, the related summary about repurposing antidiabetic drugs is of great significance. In this review, we focus on the machine learning methods for the development of new T2DM drugs and give an overview of the repurposing potential of the existing antidiabetic agents. Research Network of Computational and Structural Biotechnology 2022-06-01 /pmc/articles/PMC9189996/ /pubmed/35765655 http://dx.doi.org/10.1016/j.csbj.2022.05.057 Text en © 2022 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Short Review
Zhu, Sha
Bai, Qifeng
Li, Lanqing
Xu, Tingyang
Drug repositioning in drug discovery of T2DM and repositioning potential of antidiabetic agents
title Drug repositioning in drug discovery of T2DM and repositioning potential of antidiabetic agents
title_full Drug repositioning in drug discovery of T2DM and repositioning potential of antidiabetic agents
title_fullStr Drug repositioning in drug discovery of T2DM and repositioning potential of antidiabetic agents
title_full_unstemmed Drug repositioning in drug discovery of T2DM and repositioning potential of antidiabetic agents
title_short Drug repositioning in drug discovery of T2DM and repositioning potential of antidiabetic agents
title_sort drug repositioning in drug discovery of t2dm and repositioning potential of antidiabetic agents
topic Short Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9189996/
https://www.ncbi.nlm.nih.gov/pubmed/35765655
http://dx.doi.org/10.1016/j.csbj.2022.05.057
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