Identification of novel compounds against three targets of SARS CoV-2 coronavirus by combined virtual screening and supervised machine learning

Publication type:
Journal article
Metadata:
Autoren
Autoren-URL
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:000656574900001&DestLinkType=FullRecord&DestApp=WOS_CPL
DOI
10.1016/j.compbiomed.2021.104359
eISSN
1879-0534
Externe Identifier
  • Clarivate Analytics Document Solution ID: SK9YV
  • PubMed Identifier: 33845270
ISSN
0010-4825
Zeitschrift
COMPUTERS IN BIOLOGY AND MEDICINE
Schlüsselwörter
  • Artificial intelligence
  • Chemotherapy
  • COVID-19
  • Infectious diseases
  • Natural products
Artikelnummer
ARTN 104359
Datum der Veröffentlichung
2021
Status
Published
Titel
Identification of novel compounds against three targets of SARS CoV-2 coronavirus by combined virtual screening and supervised machine learning
Sub types
  • Article
Ausgabe der Zeitschrift
133

Data source: Web of Science (Lite)

Other metadata sources:
Autoren
DOI
10.1016/j.compbiomed.2021.104359
ISSN
0010-4825
Zeitschrift
Computers in Biology and Medicine
Sprache
en
Artikelnummer
104359
Paginierung
104359 - 104359
Datum der Veröffentlichung
2021
Status
Published
Herausgeber
Elsevier BV
Herausgeber URL
http://dx.doi.org/10.1016/j.compbiomed.2021.104359
Datum der Datenerfassung
2024
Titel
Identification of novel compounds against three targets of SARS CoV-2 coronavirus by combined virtual screening and supervised machine learning
Ausgabe der Zeitschrift
133

Data source: Crossref

Abstract
Coronavirus disease 2019 (COVID-19) is a major threat worldwide due to its fast spreading. As yet, there are no established drugs available. Speeding up drug discovery is urgently required. We applied a workflow of combined in silico methods (virtual drug screening, molecular docking and supervised machine learning algorithms) to identify novel drug candidates against COVID-19. We constructed chemical libraries consisting of FDA-approved drugs for drug repositioning and of natural compound datasets from literature mining and the ZINC database to select compounds interacting with SARS-CoV-2 target proteins (spike protein, nucleocapsid protein, and 2'-o-ribose methyltransferase). Supported by the supercomputer MOGON, candidate compounds were predicted as presumable SARS-CoV-2 inhibitors. Interestingly, several approved drugs against hepatitis C virus (HCV), another enveloped (-) ssRNA virus (paritaprevir, simeprevir and velpatasvir) as well as drugs against transmissible diseases, against cancer, or other diseases were identified as candidates against SARS-CoV-2. This result is supported by reports that anti-HCV compounds are also active against Middle East Respiratory Virus Syndrome (MERS) coronavirus. The candidate compounds identified by us may help to speed up the drug development against SARS-CoV-2.
Addresses
  • Department of Pharmaceutical Biology, Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg University, Mainz, Germany.
Autoren
DOI
10.1016/j.compbiomed.2021.104359
eISSN
1879-0534
Externe Identifier
  • PubMed Identifier: 33845270
  • PubMed Central ID: PMC8008812
Open access
true
ISSN
0010-4825
Zeitschrift
Computers in biology and medicine
Schlüsselwörter
  • Humans
  • Antiviral Agents
  • Molecular Docking Simulation
  • Supervised Machine Learning
  • COVID-19
  • SARS-CoV-2
  • Severe acute respiratory syndrome-related coronavirus
Sprache
eng
Medium
Print-Electronic
Online publication date
2021
Open access status
Open Access
Paginierung
104359
Datum der Veröffentlichung
2021
Status
Published
Datum der Datenerfassung
2021
Titel
Identification of novel compounds against three targets of SARS CoV-2 coronavirus by combined virtual screening and supervised machine learning.
Sub types
  • research-article
  • Journal Article
Ausgabe der Zeitschrift
133

Files

https://europepmc.org/articles/PMC8008812?pdf=render

Data source: Europe PubMed Central

Abstract
Coronavirus disease 2019 (COVID-19) is a major threat worldwide due to its fast spreading. As yet, there are no established drugs available. Speeding up drug discovery is urgently required. We applied a workflow of combined in silico methods (virtual drug screening, molecular docking and supervised machine learning algorithms) to identify novel drug candidates against COVID-19. We constructed chemical libraries consisting of FDA-approved drugs for drug repositioning and of natural compound datasets from literature mining and the ZINC database to select compounds interacting with SARS-CoV-2 target proteins (spike protein, nucleocapsid protein, and 2'-o-ribose methyltransferase). Supported by the supercomputer MOGON, candidate compounds were predicted as presumable SARS-CoV-2 inhibitors. Interestingly, several approved drugs against hepatitis C virus (HCV), another enveloped (-) ssRNA virus (paritaprevir, simeprevir and velpatasvir) as well as drugs against transmissible diseases, against cancer, or other diseases were identified as candidates against SARS-CoV-2. This result is supported by reports that anti-HCV compounds are also active against Middle East Respiratory Virus Syndrome (MERS) coronavirus. The candidate compounds identified by us may help to speed up the drug development against SARS-CoV-2.
Date of acceptance
2021
Autoren
Autoren-URL
https://www.ncbi.nlm.nih.gov/pubmed/33845270
DOI
10.1016/j.compbiomed.2021.104359
eISSN
1879-0534
Externe Identifier
  • PubMed Central ID: PMC8008812
Zeitschrift
Comput Biol Med
Schlüsselwörter
  • Artificial intelligence
  • COVID-19
  • Chemotherapy
  • Infectious diseases
  • Natural products
  • Antiviral Agents
  • COVID-19
  • Humans
  • Molecular Docking Simulation
  • Severe acute respiratory syndrome-related coronavirus
  • SARS-CoV-2
  • Supervised Machine Learning
Sprache
eng
Country
United States
Paginierung
104359
PII
S0010-4825(21)00153-0
Datum der Veröffentlichung
2021
Status
Published
Datum, an dem der Datensatz öffentlich gemacht wurde
2021
Titel
Identification of novel compounds against three targets of SARS CoV-2 coronavirus by combined virtual screening and supervised machine learning.
Sub types
  • Journal Article
Ausgabe der Zeitschrift
133

Data source: PubMed

Autoren
Zeitschrift
Comput. Biol. Medicine
Paginierung
104359 - 104359
Datum der Veröffentlichung
2021
Titel
Identification of novel compounds against three targets of SARS CoV-2 coronavirus by combined virtual screening and supervised machine learning.
Ausgabe der Zeitschrift
133

Data source: DBLP

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