About: Rapid Identification of Potential Inhibitors of SARS‐CoV‐2 Main Protease by Deep Docking of 1.3 Billion Compounds

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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
title	Rapid Identification of Potential Inhibitors of SARS‐CoV‐2 Main Protease by Deep Docking of 1.3 Billion Compounds
Creator	Cherkasov, Artem Gentile, Francesco Hsing, Michael Ton, Anh-Tien Fuqiang, ]
source	Medline; PMC
abstract	The recently emerged 2019 Novel Coronavirus (SARS‐CoV‐2) and associated COVID‐19 disease cause serious or even fatal respiratory tract infection and yet no approved therapeutics or effective treatment is currently available to effectively combat the outbreak. This urgent situation is pressing the world to respond with the development of novel vaccine or a small molecule therapeutics for SARS‐CoV‐2. Along these efforts, the structure of SARS‐CoV‐2 main protease (Mpro) has been rapidly resolved and made publicly available to facilitate global efforts to develop novel drug candidates. Recently, our group has developed a novel deep learning platform – Deep Docking (DD) which provides fast prediction of docking scores of Glide (or any other docking program) and, hence, enables structure‐based virtual screening of billions of purchasable molecules in a short time. In the current study we applied DD to all 1.3 billion compounds from ZINC15 library to identify top 1,000 potential ligands for SARS‐CoV‐2 Mpro protein. The compounds are made publicly available for further characterization and development by scientific community.
has issue date	2020-03-23(xsd:dateTime)
bibo:doi	10.1002/minf.202000028
bibo:pmid	32162456
has license	no-cc
sha1sum (hex)	c6d069ed9776bfdb6416aa24c09d3c3e25221d75
schema:url	https://doi.org/10.1002/minf.202000028
resource representing a document's title	Rapid Identification of Potential Inhibitors of SARS‐CoV‐2 Main Protease by Deep Docking of 1.3 Billion Compounds
has PubMed Central identifier	PMC7228259
has PubMed identifier	32162456
schema:publication	Mol Inform
resource representing a document's body	covid:c6d069ed9776bfdb6416aa24c09d3c3e25221d75#body_text
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