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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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Published in:Virusdisease 2021-12, Vol.32 (4), p.757-765
Main Authors: Sherin, D. R., Sharanya, N., Manojkumar, T. K.
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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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subjects Aerva lanata
Binding sites
Bioavailability
Biochemistry
Biomedical and Life Sciences
Cell Biology
Chloroquine
Clinical trials
Coronaviruses
COVID-19
Datasets
Drugs
Learning algorithms
Life Sciences
Ligands
Machine learning
Microbiology
Pandemics
Phytochemicals
Protein Structure
Proteins
Regression analysis
Respiratory diseases
Severe acute respiratory syndrome coronavirus 2
Short Communication
Simulation
Urinary tract diseases
title Potential drug leads for SARS-CoV2 from phytochemicals of Aerva lanata: a Machine Learning approach
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