دورية أكاديمية
أعرض في EDS

Machine Learning Models Identify Inhibitors of SARS-CoV-2.

التفاصيل البيبلوغرافية
العنوان: Machine Learning Models Identify Inhibitors of SARS-CoV-2.
المؤلفون: 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.; Department of Animal, Dairy and Veterinary Sciences, Utah State University, Logan, Utah 84322-4815, United States., Tavella TA; 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., 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; Department of Microbiology and Immunology, University of North Carolina School of Medicine, Chapel Hill North Carolina 27599, United States., Bernatchez J; Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, San Diego, California 92093, United States., Godoy AS; São Carlos Institute of Physics, University of São Paulo, Av. João Dagnone, 1100-Santa Angelina, São Carlos, São Paulo 13563-120, Brazil., 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; Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, San Diego, California 92093, United States., Madrid PB; SRI International, 333 Ravenswood Avenue, Menlo Park, California 94025, United States., Ekins S; Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States.
المصدر: Journal of chemical information and modeling [J Chem Inf Model] 2021 Sep 27; Vol. 61 (9), pp. 4224-4235. Date of Electronic Publication: 2021 Aug 13.
نوع المنشور: Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: American Chemical Society Country of Publication: United States NLM ID: 101230060 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1549-960X (Electronic) Linking ISSN: 15499596 NLM ISO Abbreviation: J Chem Inf Model Subsets: MEDLINE
أسماء مطبوعة: Original Publication: Washington, D.C. : American Chemical Society, c2005-
مواضيع طبية MeSH: COVID-19* , SARS-CoV-2*, Bayes Theorem ; Humans ; Machine Learning ; Molecular Docking Simulation
مستخلص: 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 ( K d 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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معلومات مُعتمدة: R44 GM122196 United States GM NIGMS NIH HHS; R43 AT010585 United States AT NCCIH NIH HHS
تواريخ الأحداث: Date Created: 20210813 Date Completed: 20210929 Latest Revision: 20220928
رمز التحديث: 20231215
مُعرف محوري في PubMed: PMC8574161
DOI: 10.1021/acs.jcim.1c00683
PMID: 34387990
قاعدة البيانات: MEDLINE
الوصف
تدمد:1549-960X
DOI:10.1021/acs.jcim.1c00683