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Machine Learning Models Identify Inhibitors of SARS-CoV-2.
Gawriljuk, Victor O; Zin, Phyo Phyo Kyaw; Puhl, Ana C; Zorn, Kimberley M; Foil, Daniel H; Lane, Thomas R; Hurst, Brett; Tavella, Tatyana Almeida; Costa, Fabio Trindade Maranhão; Lakshmanane, Premkumar; Bernatchez, Jean; Godoy, Andre S; Oliva, Glaucius; Siqueira-Neto, Jair L; Madrid, Peter B; Ekins, Sean.
  • Gawriljuk VO; São Carlos Institute of Physics, University of São Paulo, Av. João Dagnone, 1100-Santa Angelina, São Carlos, São Paulo 13563-120, Brazil.
  • Zin PPK; Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States.
  • Puhl AC; Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States.
  • Zorn KM; Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States.
  • Foil DH; Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States.
  • Lane TR; Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States.
  • Hurst B; Institute for Antiviral Research, Utah State University, Logan, Utah 84322-5600, United States.
  • Tavella TA; Department of Animal, Dairy and Veterinary Sciences, Utah State University, Logan, Utah 84322-4815, United States.
  • Costa FTM; Laboratory of Tropical Diseases-Prof. Dr. Luiz Jacinto da Silva, Department of Genetics, Evolution, Microbiology and Immunology, University of Campinas-UNICAMP, Campinas, São Paulo, Brazil.
  • Lakshmanane P; Laboratory of Tropical Diseases-Prof. Dr. Luiz Jacinto da Silva, Department of Genetics, Evolution, Microbiology and Immunology, University of Campinas-UNICAMP, Campinas, São Paulo, Brazil.
  • Bernatchez J; Department of Microbiology and Immunology, University of North Carolina School of Medicine, Chapel Hill North Carolina 27599, United States.
  • Godoy AS; Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, San Diego, California 92093, United States.
  • Oliva G; São Carlos Institute of Physics, University of São Paulo, Av. João Dagnone, 1100-Santa Angelina, São Carlos, São Paulo 13563-120, Brazil.
  • Siqueira-Neto JL; São Carlos Institute of Physics, University of São Paulo, Av. João Dagnone, 1100-Santa Angelina, São Carlos, São Paulo 13563-120, Brazil.
  • Madrid PB; Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, San Diego, California 92093, United States.
  • Ekins S; SRI International, 333 Ravenswood Avenue, Menlo Park, California 94025, United States.
J Chem Inf Model ; 61(9): 4224-4235, 2021 09 27.
Article in English | MEDLINE | ID: covidwho-1356531
ABSTRACT
With the rapidly evolving SARS-CoV-2 variants of concern, there is an urgent need for the discovery of further treatments for the coronavirus disease (COVID-19). Drug repurposing is one of the most rapid strategies for addressing this need, and numerous compounds have already been selected for in vitro testing by several groups. 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, lumefantrine, an antimalarial was selected for testing and showed limited antiviral activity in cell-based assays while demonstrating binding (Kd 259 nM) to the spike protein using microscale thermophoresis. Several other compounds which we prioritized have since been tested by others and were also found to be active in vitro. This combined machine learning and in vitro testing approach can be expanded to virtually screen available molecules with predicted activity against SARS-CoV-2 reference WIV04 strain and circulating variants of concern. In the process of this work, we have created multiple iterations of machine learning models that can be used as a prioritization tool for SARS-CoV-2 antiviral drug discovery programs. The very latest model for SARS-CoV-2 with over 500 compounds is now freely available at www.assaycentral.org.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Experimental Studies / Prognostic study / Randomized controlled trials Topics: Variants Limits: Humans Language: English Journal: J Chem Inf Model Journal subject: Medical Informatics / Chemistry Year: 2021 Document Type: Article Affiliation country: Acs.jcim.1c00683

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Full text: Available Collection: International databases Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Experimental Studies / Prognostic study / Randomized controlled trials Topics: Variants Limits: Humans Language: English Journal: J Chem Inf Model Journal subject: Medical Informatics / Chemistry Year: 2021 Document Type: Article Affiliation country: Acs.jcim.1c00683