Machine Learning Models Identify Inhibitors of SARS-CoV-2 | Zendy

Victor O. Gawriljuk | Zendy; Phyo Phyo Kyaw Zin | Zendy; Ana C. Puhl | Zendy; Kimberley M. Zorn | Zendy; Daniel H. Foil | Zendy; Thomas R. Lane | Zendy; Brett L. Hurst | Zendy; Tatyana Almeida Tavella | Zendy; Fábio Trindade Maranhão Costa | Zendy; Lakshmanane Premkumar | Zendy; Jean Bernatchez | Zendy; André S. Godoy | Zendy; Glaucius Oliva | Zendy; Jair L. Siqueira-Neto | Zendy; Peter B. Madrid | Zendy; Sean Ekins | Zendy

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Machine Learning Models Identify Inhibitors of SARS-CoV-2

Author(s) -

Victor O. Gawriljuk,

Phyo Phyo Kyaw Zin,

Ana C. Puhl,

Kimberley M. Zorn,

Daniel H. Foil,

Thomas R. Lane,

Brett L. Hurst,

Tatyana Almeida Tavella,

Fábio Trindade Maranhão Costa,

Lakshmanane Premkumar,

Jean Bernatchez,

André S. Godoy,

Glaucius Oliva,

Jair L. Siqueira-Neto,

Peter B. Madrid,

Sean Ekins

Publication year - 2021

Publication title -

journal of chemical information and modeling

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.24

H-Index - 160

eISSN - 1549-960X

pISSN - 1549-9596

DOI - 10.1021/acs.jcim.1c00683

Subject(s) - drug discovery , machine learning , microscale thermophoresis , artificial intelligence , repurposing , drug repositioning , computer science , computational biology , in vitro toxicology , coronavirus , computational model , covid-19 , in vitro , drug , bioinformatics , pharmacology , biology , medicine , infectious disease (medical specialty) , biochemistry , disease , ecology , pathology

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