Recent advances in drug repurposing using machine learning

Drug repurposing aims to find new uses for already existing and approved drugs. We now provide a brief overview of recent developments in drug repurposing using machine learning alongside other computational approaches for comparison. We also highlight several applications for cancer using kinase in...

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Podrobná bibliografie
Vydáno v:	Current opinion in chemical biology Ročník 65; s. 74 - 84
Hlavní autoři:	Urbina, Fabio, Puhl, Ana C., Ekins, Sean
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	England Elsevier Ltd 01.12.2021
Témata:	Alzheimer Disease - drug therapy Antineoplastic Agents - therapeutic use Antiviral Agents - therapeutic use Clemastine - pharmacology Computational Biology - methods COVID-19 - drug therapy Dipyridamole - pharmacology Drug discovery Drug Repositioning - trends Drug repurposing Humans Hydroxychloroquine - pharmacology Lenalidomide - pharmacology Machine Learning Neoplasms - drug therapy Neuroprotective Agents - therapeutic use Piperazines - pharmacology Piperidines - pharmacology Protein Kinase Inhibitors - pharmacology BACE1 Aβ GSK3β FDA AD DNN CMap Machine learning Drug repurposing Drug discovery
ISSN:	1367-5931, 1879-0402, 1879-0402
On-line přístup:	Získat plný text
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Popis
Shrnutí:	Drug repurposing aims to find new uses for already existing and approved drugs. We now provide a brief overview of recent developments in drug repurposing using machine learning alongside other computational approaches for comparison. We also highlight several applications for cancer using kinase inhibitors, Alzheimer’s disease as well as COVID-19. [Display omitted] •Machine learning for drug repurposing was increasing before 2020.•It has been applied to model small molecule screening data, transcriptomics and proteomics.•Computationally repurposed approved kinase inhibitors have been identified by several groups.•Computational repurposing can be used in areas of difficult drug discovery e.g. antibiotics and Alzheimer 's disease.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 ObjectType-Review-3 content type line 23
ISSN:	1367-5931 1879-0402 1879-0402
DOI:	10.1016/j.cbpa.2021.06.001