A molecular modeling approach to identify effective antiviral phytochemicals against the main protease of SARS-CoV-2
Rajib Islam, Md. Rimon Parves, Archi Sundar Paul, Nizam Uddin, Md Sajjadur Rahman, Abdulla Al Mamun, Md Nayeem Hossain, Muhammad Ali, Mohammad A. Halim
Abstract
value of 0.842 for the training set and 0.753 for the test set. Our proposed MLR model can predict the favorable binding energy compared with the binding energy detected from molecular docking. ADMET analysis demonstrates that these candidates appear to be safer inhibitors. Our comprehensive computational and statistical analysis show that these selected phytochemicals can be used as potential inhibitors against the SARS-CoV-2.Communicated by Ramaswamy H. Sarma.
Topics & Concepts
AutoDockQuantitative structure–activity relationshipDocking (animal)ChemistryProteaseRadius of gyrationStereochemistryMolecular descriptorAccessible surface areaComputational biologyComputational chemistryBiochemistryBiologyEnzymeIn silicoOrganic chemistryMedicineNursingGenePolymerComputational Drug Discovery MethodsSynthesis and biological activityPharmacological Effects of Natural Compounds