Rimegepant is a new medicine developed for the management of chronic headache due to migraine. This manuscript is an attempt to study the various structural, physical, and chemical properties of the molecules. The molecule was optimized using B3LYP functional with 6-311G + (2d,p) basis set. Excited state properties of the compound were studied using CAM-B3LYP functional with same basis sets using IEFPCM model in methanol for the implicit solvent atmosphere. The various electronic descriptors helped to identify the reactivity behavior and stability. The compound is found to possess good nonlinear optical properties in the gas phase. The various intramolecular electronic delocalizations and non-covalent interactions were analyzed and explained. As the compound contain several heterocyclic nitrogen atoms, they have potential proton abstraction features, which was analyzed energetically. The most important result from this study is from the molecular docking analysis which indicates that rimegepant binds irreversibly with three established SARS-CoV-2 proteins with ID 6LU7, 6M03, and 6W63 with docking scores − 9.2988, − 8.3629, and − 9.5421 kcal/mol respectively. Further assessment of docked complexes with molecular dynamics simulations revealed that hydrophobic interactions, water bridges, and π–π interactions play a significant role in stabilizing the ligand within the binding region of respective proteins. MMGBSA-free energies further demonstrated that rimegepant is more stable when complexed with 6LU7 among the selected PDB models. As the pharmacology and pharmacokinetics of this molecule are already established, rimegepant can be considered as an ideal candidate with potential for use in the treatment of COVID patients after clinical studies.
CITATION STYLE
Pooventhiran, T., Marondedze, E. F., Govender, P. P., Bhattacharyya, U., Rao, D. J., Aazam, E. S., … Thomas, R. (2021). Energy and reactivity profile and proton affinity analysis of rimegepant with special reference to its potential activity against SARS-CoV-2 virus proteins using molecular dynamics. Journal of Molecular Modeling, 27(10). https://doi.org/10.1007/s00894-021-04885-z
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