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Please use this identifier to cite or link to this item: http://hdl.handle.net/2289/7869
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dc.contributor.authorNedyalkova, Miroslava-
dc.contributor.authorVasighi, Mahdi-
dc.contributor.authorSappati, Subrahmanyam-
dc.contributor.authorKumar, Anmol-
dc.contributor.authorMadurga, Sergio-
dc.date.accessioned2022-01-10T04:09:58Z-
dc.date.available2022-01-10T04:09:58Z-
dc.date.issued2021-12-
dc.identifier.citationPharmaceuticals, 2021, Vol. 14, p1328en_US
dc.identifier.issn1424-8247-
dc.identifier.urihttp://hdl.handle.net/2289/7869-
dc.descriptionOpen Accessen_US
dc.description.abstractThe lack of medication to treat COVID-19 is still an obstacle that needs to be addressed by all possible scientific approaches. It is essential to design newer drugs with varied approaches. A receptor-binding domain (RBD) is a key part of SARS-CoV-2 virus, located on its surface, that allows it to dock to ACE2 receptors present on human cells, which is followed by admission of virus into cells, and thus infection is triggered. Specific receptor-binding domains on the spike protein play a pivotal role in binding to the receptor. In this regard, the in silico method plays an important role, as it is more rapid and cost effective than the trial and error methods using experimental studies. A combination of virtual screening, molecular docking, molecular simulations and machine learning techniques are applied on a library of natural compounds to identify ligands that show significant binding affinity at the hydrophobic pocket of the RBD. A list of ligands with high binding affinity was obtained using molecular docking and molecular dynamics (MD) simulations for protein–ligand complexes. Machine learning (ML) classification schemes have been applied to obtain features of ligands and important descriptors, which help in identification of better binding ligands. A plethora of descriptors were used for training the self-organizing map algorithm. The model brings out descriptors important for protein–ligand interactionsen_US
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.relation.urihttps://doi.org/10.3390/ph14121328en_US
dc.rights2021 MDPIen_US
dc.subjectSARS-CoV-2: RBDen_US
dc.subjectnatural compoundsen_US
dc.subjectdockingen_US
dc.subjectmachine learningen_US
dc.subjectcomputer-aided drug designen_US
dc.subjectmolecular dynamics (MD) simulationsen_US
dc.titleInhibition ability of Natural Compounds of Receptor-Binding Domain of SARA-CoV2 : An In Silico Approachen_US
dc.typeArticleen_US
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