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10.1021/acs.jpclett.0c02278

http://scihub22266oqcxt.onion/10.1021/acs.jpclett.0c02278
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32787328!ä!32787328

suck abstract from ncbi


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pmid32787328      J+Phys+Chem+Lett 2020 ; 11 (17): 7058-7065
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  • Screening of Therapeutic Agents for COVID-19 Using Machine Learning and Ensemble Docking Studies #MMPMID32787328
  • Batra R; Chan H; Kamath G; Ramprasad R; Cherukara MJ; Sankaranarayanan SKRS
  • J Phys Chem Lett 2020[Sep]; 11 (17): 7058-7065 PMID32787328show ga
  • The current pandemic demands a search for therapeutic agents against the novel coronavirus SARS-CoV-2. Here, we present an efficient computational strategy that combines machine learning (ML)-based models and high-fidelity ensemble docking studies to enable rapid screening of possible therapeutic ligands. Targeting the binding affinity of molecules for either the isolated SARS-CoV-2 S-protein at its host receptor region or the S-protein:human ACE2 interface complex, we screen ligands from drug and biomolecule data sets that can potentially limit and/or disrupt the host-virus interactions. Top scoring one hundred eighty-seven ligands (with 75 approved by the Food and Drug Administration) are further validated by all atom docking studies. Important molecular descriptors ((2)chi(n), topological surface area, and ring count) and promising chemical fragments (oxolane, hydroxy, and imidazole) are identified to guide future experiments. Overall, this work expands our knowledge of small-molecule treatment against COVID-19 and provides a general screening pathway (combining quick ML models with expensive high-fidelity simulations) for targeting several chemical/biochemical problems.
  • |*Machine Learning[MESH]
  • |*Molecular Docking Simulation[MESH]
  • |Antiviral Agents/metabolism/*pharmacology[MESH]
  • |Betacoronavirus/*drug effects[MESH]
  • |Drug Evaluation, Preclinical[MESH]
  • |Humans[MESH]
  • |Hydrogen Bonding[MESH]
  • |Protein Conformation[MESH]
  • |SARS-CoV-2[MESH]


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