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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 |
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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. |
doi_str_mv | 10.1007/s13337-021-00732-0 |
format | article |
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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.</description><identifier>ISSN: 2347-3584</identifier><identifier>EISSN: 2347-3517</identifier><identifier>DOI: 10.1007/s13337-021-00732-0</identifier><identifier>PMID: 34368407</identifier><language>eng</language><publisher>New Delhi: Springer India</publisher><subject>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</subject><ispartof>Virusdisease, 2021-12, Vol.32 (4), p.757-765</ispartof><rights>Indian Virological Society 2021</rights><rights>Indian Virological Society 2021.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4560-3c87270baae9d96309e4a085d61330c6c29cd7c3804ca9b26adfefa5e60734573</citedby><cites>FETCH-LOGICAL-c4560-3c87270baae9d96309e4a085d61330c6c29cd7c3804ca9b26adfefa5e60734573</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8325545/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8325545/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34368407$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Sherin, D. R.</creatorcontrib><creatorcontrib>Sharanya, N.</creatorcontrib><creatorcontrib>Manojkumar, T. K.</creatorcontrib><title>Potential drug leads for SARS-CoV2 from phytochemicals of Aerva lanata: a Machine Learning approach</title><title>Virusdisease</title><addtitle>VirusDis</addtitle><addtitle>Virusdisease</addtitle><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.</description><subject>Aerva lanata</subject><subject>Binding sites</subject><subject>Bioavailability</subject><subject>Biochemistry</subject><subject>Biomedical and Life Sciences</subject><subject>Cell Biology</subject><subject>Chloroquine</subject><subject>Clinical trials</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>Datasets</subject><subject>Drugs</subject><subject>Learning algorithms</subject><subject>Life Sciences</subject><subject>Ligands</subject><subject>Machine learning</subject><subject>Microbiology</subject><subject>Pandemics</subject><subject>Phytochemicals</subject><subject>Protein Structure</subject><subject>Proteins</subject><subject>Regression analysis</subject><subject>Respiratory diseases</subject><subject>Severe acute respiratory syndrome coronavirus 2</subject><subject>Short Communication</subject><subject>Simulation</subject><subject>Urinary tract diseases</subject><issn>2347-3584</issn><issn>2347-3517</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9kUtv1DAUha0K1Falf4BFZYkNm8D1O2GBNBq1tNIgqhbYWnccZyZVEgc7qdR_X8OUKbBg5cf9zrGPDiGvGbxjAOZ9YkIIUwBnRT4KXsABOeZCmkIoZl7s96U8Iqcp3QEAY0ZKXR2SIyGFLiWYY-Kuw-SHqcWO1nHe0M5jnWgTIr1d3NwWy_Cd0yaGno7bhym4re9bh12ioaELH--RdjjghB8o0s_otu3g6cpjHNphQ3EcY8iXr8jLJmv86dN6Qr5dnH9dXharL5-ulotV4aTSUAhXGm5gjeirutICKi8RSlXrHBWcdrxytXGiBOmwWnONdeMbVF7n_FIZcUI-7nzHed372uVcETs7xrbH-GADtvbvydBu7Sbc21JwpaTKBm-fDGL4Mfs02b5Nznc5ow9zspmqtOaKi4y--Qe9C3MccjzLKw5cGWA6U3xHuRhSir7Zf4aB_Vmj3dVoc432V40Wsujszxh7ye_SMiB2QMqjYePj89v_sX0EBQ6nVA</recordid><startdate>20211201</startdate><enddate>20211201</enddate><creator>Sherin, D. 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R. ; Sharanya, N. ; Manojkumar, T. K.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4560-3c87270baae9d96309e4a085d61330c6c29cd7c3804ca9b26adfefa5e60734573</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Aerva lanata</topic><topic>Binding sites</topic><topic>Bioavailability</topic><topic>Biochemistry</topic><topic>Biomedical and Life Sciences</topic><topic>Cell Biology</topic><topic>Chloroquine</topic><topic>Clinical trials</topic><topic>Coronaviruses</topic><topic>COVID-19</topic><topic>Datasets</topic><topic>Drugs</topic><topic>Learning algorithms</topic><topic>Life Sciences</topic><topic>Ligands</topic><topic>Machine learning</topic><topic>Microbiology</topic><topic>Pandemics</topic><topic>Phytochemicals</topic><topic>Protein Structure</topic><topic>Proteins</topic><topic>Regression analysis</topic><topic>Respiratory diseases</topic><topic>Severe acute respiratory syndrome coronavirus 2</topic><topic>Short Communication</topic><topic>Simulation</topic><topic>Urinary tract diseases</topic><toplevel>online_resources</toplevel><creatorcontrib>Sherin, D. 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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.</abstract><cop>New Delhi</cop><pub>Springer India</pub><pmid>34368407</pmid><doi>10.1007/s13337-021-00732-0</doi><tpages>9</tpages><oa>free_for_read</oa></addata></record> |
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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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