About: Attacking COVID-19 Progression using Multi-Drug Therapy for Synergetic Target Engagement

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Attacking COVID-19 Progression using Multi-Drug Therapy for Synergetic Target Engagement
Creator	Affiliations, Freeman, William Caulfield, Thomas Coban, Mathew Le Roch, Karine Morrison Phd, Juliet Radisky, Evette
Source	ArXiv
abstract	COVID-19 is a devastating respiratory and inflammatory illness caused by a new coronavirus that is rapidly spreading throughout the human population. Over the past 6 months, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for COVID-19, has already infected over 11.6 million (25% located in United States) and killed more than 540K people around the world. As we face one of the most challenging times in our recent history, there is an urgent need to identify drug candidates that can attack SARS-CoV-2 on multiple fronts. We have therefore initiated a computational dynamics drug pipeline using molecular modeling, structure simulation, docking and machine learning models to predict the inhibitory activity of several million compounds against two essential SARS-CoV-2 viral proteins and their host protein interactors; S/Ace2, Tmprss2, Cathepsins L and K, and Mpro to prevent binding, membrane fusion and replication of the virus, respectively. All together we generated an ensemble of structural conformations that increase high quality docking outcomes to screen over>6 million compounds including all FDA-approved drugs, drugs under clinical trial (>3000) and an additional>30 million selected chemotypes from fragment libraries. Our results yielded an initial set of 350 high value compounds from both new and FDA-approved compounds that can now be tested experimentally in appropriate biological model systems. We anticipate that our results will initiate screening campaigns and accelerate the discovery of COVID-19 treatments.
has issue date	2020-07-06(xsd:dateTime)
has license	arxiv
sha1sum (hex)	77c7d139f5671638ee69482cb56ad8d31d2efc42
resource representing a document's title	Attacking COVID-19 Progression using Multi-Drug Therapy for Synergetic Target Engagement
resource representing a document's body	covid:77c7d139f5671638ee69482cb56ad8d31d2efc42#body_text
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