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Potential drug leads for SARS-CoV2 from phytochemicals of Aerva lanata: a Machine Learning approach
COVID-19 outbreak is the recently reported worldwide pandemic threat. As part of our interventions with machine learning and molecular simulation approaches, we report the inhibitory effect of thirty compounds reported from the sacred plant Aerva lanata. The predicted activity of the screened ligand...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer India
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8325545/ https://www.ncbi.nlm.nih.gov/pubmed/34368407 http://dx.doi.org/10.1007/s13337-021-00732-0 |
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author | Sherin, D. R. Sharanya, N. Manojkumar, T. K. |
author_facet | Sherin, D. R. Sharanya, N. Manojkumar, T. K. |
author_sort | Sherin, D. R. |
collection | PubMed |
description | COVID-19 outbreak is the recently reported worldwide pandemic threat. As part of our interventions with machine learning and molecular simulation approaches, we report the inhibitory effect of thirty compounds reported from the sacred plant Aerva lanata. The predicted activity of the screened ligands are comparable with the one of the present medication, hydroxy chloroquine (HCQ), on the main protease (PDB:6YB7) of SARS-CoV-2. Our studies pointed out the effectiveness of the plant with twenty seven compounds having potential activity against the main protease compared to the reference HCQ. The robustness of some of the phytochemicals such as ervoside, which is only present in Aerva lanata computed to have very high anticoronavirus activity. The results are indicative of potential natural antivirus source, which subsidizes in thwarting the invasion of coronavirus into the human body. Many phytochemicals which are computed to be effective towards SARS-CoV-2 in this study are used as drugs for various other diseases. Perhaps these compounds could be attractive for the management of COVID-19, but clinical trials must be performed in order to validate this observation. |
format | Online Article Text |
id | pubmed-8325545 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer India |
record_format | MEDLINE/PubMed |
spelling | pubmed-83255452021-08-02 Potential drug leads for SARS-CoV2 from phytochemicals of Aerva lanata: a Machine Learning approach Sherin, D. R. Sharanya, N. Manojkumar, T. K. Virusdisease Short Communication COVID-19 outbreak is the recently reported worldwide pandemic threat. As part of our interventions with machine learning and molecular simulation approaches, we report the inhibitory effect of thirty compounds reported from the sacred plant Aerva lanata. The predicted activity of the screened ligands are comparable with the one of the present medication, hydroxy chloroquine (HCQ), on the main protease (PDB:6YB7) of SARS-CoV-2. Our studies pointed out the effectiveness of the plant with twenty seven compounds having potential activity against the main protease compared to the reference HCQ. The robustness of some of the phytochemicals such as ervoside, which is only present in Aerva lanata computed to have very high anticoronavirus activity. The results are indicative of potential natural antivirus source, which subsidizes in thwarting the invasion of coronavirus into the human body. Many phytochemicals which are computed to be effective towards SARS-CoV-2 in this study are used as drugs for various other diseases. Perhaps these compounds could be attractive for the management of COVID-19, but clinical trials must be performed in order to validate this observation. Springer India 2021-07-31 2021-12 /pmc/articles/PMC8325545/ /pubmed/34368407 http://dx.doi.org/10.1007/s13337-021-00732-0 Text en © Indian Virological Society 2021 |
spellingShingle | Short Communication Sherin, D. R. Sharanya, N. Manojkumar, T. K. Potential drug leads for SARS-CoV2 from phytochemicals of Aerva lanata: a Machine Learning approach |
title | Potential drug leads for SARS-CoV2 from phytochemicals of Aerva lanata: a Machine Learning approach |
title_full | Potential drug leads for SARS-CoV2 from phytochemicals of Aerva lanata: a Machine Learning approach |
title_fullStr | Potential drug leads for SARS-CoV2 from phytochemicals of Aerva lanata: a Machine Learning approach |
title_full_unstemmed | Potential drug leads for SARS-CoV2 from phytochemicals of Aerva lanata: a Machine Learning approach |
title_short | Potential drug leads for SARS-CoV2 from phytochemicals of Aerva lanata: a Machine Learning approach |
title_sort | potential drug leads for sars-cov2 from phytochemicals of aerva lanata: a machine learning approach |
topic | Short Communication |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8325545/ https://www.ncbi.nlm.nih.gov/pubmed/34368407 http://dx.doi.org/10.1007/s13337-021-00732-0 |
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