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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...

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Detalles Bibliográficos
Autores principales: Sherin, D. R., Sharanya, N., Manojkumar, T. K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer India 2021
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.
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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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