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A multilevel approach for screening natural compounds as an antiviral agent for COVID-19.
Vasighi, Mahdi; Romanova, Julia; Nedyalkova, Miroslava.
  • Vasighi M; Department of Computer Science and Information Technology, Institute for Advanced Studies in Basic Sciences (IASBS), 45137-66731 Zanjan, Iran. Electronic address: vasighi@iasbs.ac.ir.
  • Romanova J; Department of Inorganic Chemistry, Sofia University "St. Kl. Ohridski", Sofia, Bulgaria. Electronic address: jromanova23@gmail.com.
  • Nedyalkova M; Department of Inorganic Chemistry, Sofia University "St. Kl. Ohridski", Sofia, Bulgaria; Chemistry Department, University of Fribourg, Fribourg, Switzerland. Electronic address: nhmn@chem.uni-sofia.bg.
Comput Biol Chem ; 98: 107694, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1944667
ABSTRACT
The COVID-19 has a worldwide spread, which has prompted concerted efforts to find successful drug treatments. Drug design focused on finding antiviral therapeutic agents from plant-derived compounds which may disrupt the attachment of SARS-CoV-2 to host cells is with a pivotal need and role in the last year. Herein, we provide an approach based on drug design methods combined with machine learning approaches to classify and discover inhibitors for COVID-19 from natural products. The spike receptor-binding domain (RBD) was docked with database of 125 ligands. The docking protocol based on several steps was performed within Autodock Vina to identify the high-affinity binding mode and to reveal more insights into interaction between the phytochemicals and the RBD domain. A protein-ligand interaction analyzer has been developed. The drug-likeness properties of explored inhibitors are analyzed in the frame of exploratory data analyses. The developed computational protocol yielded a comprehensive pipeline for predicting the inhibitors to prevent the entry RBD region.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Antiviral Agents / COVID-19 Drug Treatment Type of study: Prognostic study Topics: Traditional medicine Limits: Humans Language: English Journal: Comput Biol Chem Journal subject: Biology / Medical Informatics / Chemistry Year: 2022 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Antiviral Agents / COVID-19 Drug Treatment Type of study: Prognostic study Topics: Traditional medicine Limits: Humans Language: English Journal: Comput Biol Chem Journal subject: Biology / Medical Informatics / Chemistry Year: 2022 Document Type: Article