About: Machine Learning Models Identify Inhibitors of SARS-CoV-2

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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
has title	Machine Learning Models Identify Inhibitors of SARS-CoV-2
Creator	Ekins, Sean Lane, Thomas Madrid, Peter Zorn, Kimberley Rogers, Thomas Beutler, Nathan Bernatchez, Jean Foil, Daniel Godoy, Andre Oliva, Glaucius Ricketts, James Yang, Linlin Beck, Gawriljuk, Victor Phyo, Phyo Puhl, Ana Siqueira-Neto, Zin, Kyaw
Source	BioRxiv
abstract	With the ongoing SARS-CoV-2 pandemic there is an urgent need for the discovery of a treatment for the coronavirus disease (COVID-19). Drug repurposing is one of the most rapid strategies for addressing this need and numerous compounds have been selected for in vitro testing by several groups already. These have led to a growing database of molecules with in vitro activity against the virus. Machine learning models can assist drug discovery through prediction of the best compounds based on previously published data. Herein we have implemented several machine learning methods to develop predictive models from recent SARS-CoV-2 in vitro inhibition data and used them to prioritize additional FDA approved compounds for in vitro testing selected from our in-house compound library. From the compounds predicted with a Bayesian machine learning model, CPI1062 and CPI1155 showed antiviral activity in HeLa-ACE2 cell-based assays and represent potential repurposing opportunities for COVID-19. This approach can be greatly expanded to exhaustively virtually screen available molecules with predicted activity against this virus as well as a prioritization tool for SARS-CoV-2 antiviral drug discovery programs. The very latest model for SARS-CoV-2 is available at www.assaycentral.org.
has issue date	2020-06-16(xsd:dateTime)
bibo:doi	10.1101/2020.06.16.154765
has license	biorxiv
sha1sum (hex)	d22e080257056d45f782199c16bbb20ad4d4e252
schema:url	https://doi.org/10.1101/2020.06.16.154765
resource representing a document's title	Machine Learning Models Identify Inhibitors of SARS-CoV-2
schema:publication	bioRxiv
resource representing a document's body	covid:d22e080257056d45f782199c16bbb20ad4d4e252#body_text
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