Design, DFT, docking and in silico analysis of alkaloids acyl derivatives as potential inhibitors of SARS-CоV-2 main protease Mpro
Abstract
In the present work, at first, density functional theory calculations were performed to investigate the molecular structure anabasine, cytisine, quinine alkaloids acyl derivatives by CAM-B3LYP/MidiX level of theory. A detail of quantum molecular descriptors of the title compounds such as Ionization Potential (IP) and Electron Affinities (EA), Hardness (η), Softness (S), Electronegativity (μ), Electrophilic Index (ω), Electron Donating Power (ω
–
), Electron Accepting Power (ω
+
) and Energy Gap (
E
g
) have been calculated. Pharmacokinetic properties of the title compounds and their bioactivity were investigated. In the following, a molecular docking study was carried out to screen for effective available compound which may work as a strong inhibitor for the SARS-CoV-2 main protease M
pro.
The binding energy between SARS-CoV-2 main protease M
pro
and derivatives of natural alkaloids showed a good binding affinity. Therefore, studied derivatives of natural alkaloids can be used for potential application against the SARS-CoV-2 main protease M
pro
.
How strong is the evidence?
Evidence Hub reads this as a simulation / modelling, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
Read the paper
Related studies
- Chaos and quantum thermalization
- The Dalton quantum chemistry program system
- Cord Blood Leptin and Adiponectin as Predictors of Adiposity in Children at 3 Years of Age: A Prospective Cohort Study
- Quantitative metabolomics by H-NMR and LC-MS/MS confirms altered metabolic pathways in diabetes
- Stable adiabatic quantum batteries
- A novel self-lipid antigen targets human T cells against CD1c(+) leukemias
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.