About: Repositioning of 8565 existing drugs for COVID-19

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About: Repositioning of 8565 existing drugs for COVID-19 Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Repositioning of 8565 existing drugs for COVID-19
Creator	Wang, Rui Chen, Jiahui Duy Nguyen, Duc Gao, Kaifu Wei, Guo-Wei
topic	covid:40a68f640f974063a657ba646e5884d7c0d9c943#this
Source	ArXiv
abstract	The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected near 5 million people and led to over 0.3 million deaths. Currently, there is no specific anti-SARS-CoV-2 medication. New drug discovery typically takes more than ten years. Drug repositioning becomes one of the most feasible approaches for combating COVID-19. This work curates the largest available experimental dataset for SARS-CoV-2 or SARS-CoV main protease inhibitors. Based on this dataset, we develop validated machine learning models with relatively low root mean square error to screen 1553 FDA-approved drugs as well as other 7012 investigational or off-market drugs in DrugBank. We found that many existing drugs might be potentially potent to SARS-CoV-2. The druggability of many potent SARS-CoV-2 main protease inhibitors is analyzed. This work offers a foundation for further experimental studies of COVID-19 drug repositioning.
has issue date	2020-05-20(xsd:dateTime)
has license	arxiv
sha1sum (hex)	40a68f640f974063a657ba646e5884d7c0d9c943
resource representing a document's title	Repositioning of 8565 existing drugs for COVID-19
resource representing a document's body	covid:40a68f640f974063a657ba646e5884d7c0d9c943#body_text
is http://vocab.deri.ie/void#inDataset of	proxy:http/ns.inria.fr/covid19/40a68f640f974063a657ba646e5884d7c0d9c943
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