About: Screening of Therapeutic Agents for COVID-19 Using Machine Learning and Ensemble Docking Studies

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Screening of Therapeutic Agents for COVID-19 Using Machine Learning and Ensemble Docking Studies
Creator	Chan, Henry Batra, Rohit Cherukara, Mathew Kamath, Ganesh Ramprasad, Rampi Sankaranarayanan, Subramanian
Source	Medline; PMC
abstract	[Image: see text] 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)χ(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.
has issue date	2020-08-05(xsd:dateTime)
bibo:doi	10.1021/acs.jpclett.0c02278
bibo:pmid	32787328
has license	no-cc
sha1sum (hex)	37d2b37a80dfe1337597497f817537606743b252
schema:url	https://doi.org/10.1021/acs.jpclett.0c02278
resource representing a document's title	Screening of Therapeutic Agents for COVID-19 Using Machine Learning and Ensemble Docking Studies
has PubMed Central identifier	PMC7430156
has PubMed identifier	32787328
schema:publication	J Phys Chem Lett
resource representing a document's body	covid:37d2b37a80dfe1337597497f817537606743b252#body_text
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