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A Computational Approach to Identify Potential Novel Inhibitors against the Coronavirus SARS‐CoV‐2
The current pandemic threat of COVID‐19, caused by the novel coronavirus SARS‐CoV‐2, not only gives rise to a high number of deaths around the world but also has immense consequences for the worldwide health systems and global economy. Given the fact that this pandemic is still ongoing and there are...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7583376/ https://www.ncbi.nlm.nih.gov/pubmed/32721082 http://dx.doi.org/10.1002/minf.202000090 |
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author | Battisti, Verena Wieder, Oliver Garon, Arthur Seidel, Thomas Urban, Ernst Langer, Thierry |
author_facet | Battisti, Verena Wieder, Oliver Garon, Arthur Seidel, Thomas Urban, Ernst Langer, Thierry |
author_sort | Battisti, Verena |
collection | PubMed |
description | The current pandemic threat of COVID‐19, caused by the novel coronavirus SARS‐CoV‐2, not only gives rise to a high number of deaths around the world but also has immense consequences for the worldwide health systems and global economy. Given the fact that this pandemic is still ongoing and there are currently no drugs or vaccines against this novel coronavirus available, this in silico study was conducted to identify a potential novel SARS‐CoV‐2‐inhibitor. Two different approaches were pursued: 1) The Docking Consensus Approach (DCA) is a novel approach, which combines molecular dynamics simulations with molecular docking. 2) The Common Hits Approach (CHA) in contrast focuses on the combination of the feature information of pharmacophore modeling and the flexibility of molecular dynamics simulations. The application of both methods resulted in the identification of 10 compounds with high coronavirus inhibition potential. |
format | Online Article Text |
id | pubmed-7583376 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-75833762020-10-29 A Computational Approach to Identify Potential Novel Inhibitors against the Coronavirus SARS‐CoV‐2 Battisti, Verena Wieder, Oliver Garon, Arthur Seidel, Thomas Urban, Ernst Langer, Thierry Mol Inform Full Papers The current pandemic threat of COVID‐19, caused by the novel coronavirus SARS‐CoV‐2, not only gives rise to a high number of deaths around the world but also has immense consequences for the worldwide health systems and global economy. Given the fact that this pandemic is still ongoing and there are currently no drugs or vaccines against this novel coronavirus available, this in silico study was conducted to identify a potential novel SARS‐CoV‐2‐inhibitor. Two different approaches were pursued: 1) The Docking Consensus Approach (DCA) is a novel approach, which combines molecular dynamics simulations with molecular docking. 2) The Common Hits Approach (CHA) in contrast focuses on the combination of the feature information of pharmacophore modeling and the flexibility of molecular dynamics simulations. The application of both methods resulted in the identification of 10 compounds with high coronavirus inhibition potential. John Wiley and Sons Inc. 2020-07-28 2020-10 /pmc/articles/PMC7583376/ /pubmed/32721082 http://dx.doi.org/10.1002/minf.202000090 Text en © 2020 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Full Papers Battisti, Verena Wieder, Oliver Garon, Arthur Seidel, Thomas Urban, Ernst Langer, Thierry A Computational Approach to Identify Potential Novel Inhibitors against the Coronavirus SARS‐CoV‐2 |
title | A Computational Approach to Identify Potential Novel Inhibitors against the Coronavirus SARS‐CoV‐2 |
title_full | A Computational Approach to Identify Potential Novel Inhibitors against the Coronavirus SARS‐CoV‐2 |
title_fullStr | A Computational Approach to Identify Potential Novel Inhibitors against the Coronavirus SARS‐CoV‐2 |
title_full_unstemmed | A Computational Approach to Identify Potential Novel Inhibitors against the Coronavirus SARS‐CoV‐2 |
title_short | A Computational Approach to Identify Potential Novel Inhibitors against the Coronavirus SARS‐CoV‐2 |
title_sort | computational approach to identify potential novel inhibitors against the coronavirus sars‐cov‐2 |
topic | Full Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7583376/ https://www.ncbi.nlm.nih.gov/pubmed/32721082 http://dx.doi.org/10.1002/minf.202000090 |
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