Molecular docking, molecular dynamics simulation, and ADMET analysis of levamisole derivatives against the SARS-CoV-2 main protease (MPro).

Introduction: The new species of coronaviruses (CoVs), SARS-CoV-2, was reported as responsible for an outbreak of respiratory disease. Scientists and researchers are endeavoring to develop new approaches for the effective treatment against of the COVID-19 disease. There are no finally targeted antiviral agents able to inhibit the SARS-CoV-2 at present. Therefore, it is of interest to investigate the potential uses of levamisole derivatives, which are reported to be antiviral agents targeting the influenza virus. Methods: In the present study, 12 selected levamisole derivatives containing imidazo[2,1-b]thiazole were subjected to molecular docking in order to explore the binding mechanisms between these derivatives and the SARS-CoV-2 Mpro (PDB: 7BQY). The levamisole derivatives were evaluated for in silico ADMET properties for wet-lab applicability. Further, the stability of the best-docked complex was checked using molecular dynamics (MD) simulation at 20 ns. Results: Levamisole derivatives and especially molecule N°6 showed more promising docking results, presenting favorable binding interactions as well as better docking energy compared to chloroquine and mefloquine. The results of ADMET prediction and MD simulation support the potential of the molecule N°6 to be further developed as a novel inhibitor able to stop the newly emerged SARS-CoV-2. Conclusion: This research provided an effective first line in the rapid discovery of drug leads against the novel CoV (SARS-CoV-2).

Identification of novel acetylcholinesterase inhibitors through 3D-QSAR, molecular docking, and molecular dynamics simulation targeting Alzheimer's disease.

Acetylcholinesterase (AChE) is a potential target for the development of small molecules as inhibitors for the therapy of Alzheimer's disease (AD). To design highly active acetylcholinesterase inhibitors, a three-dimensional quantitative structure-activity relationship (3D-QSAR) approach was performed on a series of N-benzylpyrrolidine derivatives previously evaluated for acetylcholinesterase inhibitory activity. The developed two models, CoMFA and CoMSIA, were statistically validated, and good predictability was achieved for both models. The information generated from 3D-QSAR contour maps may provide a better understanding of the structural features required for acetylcholinesterase inhibition and help to design new potential anti-acetylcholinesterase molecules. Consequently, six novel acetylcholinesterase inhibitors were designed, among which compound A1 with the highest predicted activity was subjected to detailed molecular docking and compared to the most active compound. Extra-precision molecular dynamics (MD) simulation of 50 ns and binding free energy calculations using MM-GBSA were performed for the selected compounds to validate the stability. These results may afford important structural insights needed to identify novel acetylcholinesterase inhibitors and other promising strategies in drug discovery.

Integrated 3D-QSAR, molecular docking, and molecular dynamics simulation studies on 1,2,3-triazole based derivatives for designing new acetylcholinesterase inhibitors.

Alzheimer's disease (AD) is a multifactorial and polygenic disease. It is the most prevalent reason for dementia in the aging population. A dataset of twenty-six 1,2,3-triazole-based derivatives previously synthetized and evaluated for acetylcholinesterase inhibitory activity were subjected to the three-dimensional quantitative structure-activity relationship (3D-QSAR) study. Good predictability was achieved for comparative molecular field analysis (CoMFA) (Q2 = 0.604, R2 = 0.863, rext 2 = 0.701) and comparative molecular similarity indices analysis (CoMSIA) (Q2 = 0.606, R2 = 0.854, rext 2 = 0.647). The molecular features characteristics provided by the 3D-QSAR contour plots were quite useful for designing and improving the activity of acetylcholinesterase of this class. Based on these findings, a new series of 1,2,3-triazole based derivatives were designed, among which compound A1 with the highest predictive activity was subjected to detailed molecular docking and compared to the most active compound. The selected compounds were further subjected to 20 ns molecular dynamics (MD) simulations to study the comparative conformation dynamics of the protein after ligand binding, revealing promising results for the designed molecule. Therefore, this study could provide worthy guidance for further experimental analysis of highly effective acetylcholinesterase inhibitors.