Discovery of potential SARS-CoV 3CL protease inhibitors from approved antiviral drugs using: virtual screening, molecular docking, pharmacophore mapping evaluation and dynamics simulation.

The spread of corona-virus disease 2019 (COVID-19) has been faster than any other corona-viruses that have succeeded in crossing the animal-human barrier. This disease, caused by the severe acute respiratory syndrome corona-virus 2 (SARS-CoV-2/2019-nCoV) posing a serious threat to global public health and local economies. There are three responsible for this disease; SARS-CoV-2, SARS-CoV and MERS-CoV. Whereas our goal is to test the affinity for a new class of compounds obtained from a hybridization of Chloroquine, Amodiaquine and Mefloquine with three targets SARS-CoV-2, SARS-CoV and MERS-CoV, in order to find new compounds as new inhibitors against Covid-19. In this work, we first used: the molecular docking/dynamics methods and ADME properties to study interaction and affinity between eight new compounds against three targets involved in the Covid-19. The results of the docking simulations and dynamics revealed that inhibitor of the malaria (Ligand 87) has an affinity to interact with SARS-CoV-2, SARS-CoV and MERS-CoV targets and they can be good inhibitors for treatment of Covid-19. Moreover, they give best affinity compared to the Remdesivir and Chloroquine and other clinical tests. The Pharmacokinetics was justified by means of lipophilicity and high coefficient of skin permeability. The in silico evaluation of ADME and drug-likeness revealed that L87 has higher absorption in the intestines with good bioavailability. However, an additional in vitro and/or in vivo experimental study should make it possible to verify the theoretical results obtained in silico.Communicated by Ramaswamy H. Sarma.

DFT-based reactivity and combined QSAR, molecular docking of 1,2,4,5-Tetrazine derivatives as inhibitors of Pim-1 kinase.

In the present work we have calculated several DFT reactivity descriptors for 1,2,4,5-Tetrazine at the B3LYP/6-311++G(d,p) level of theory in order to analyze its reactivity in vacuum and solvent phases. Whereas, the influence of the solvent was taken into account employing the PCM model. DFT-based descriptors such as (electronic chemical potential, electrophilicity, condensed Fukui function….) have been determined to predict the reactivity of 1,2,4,5-Tetrazine. A series of eighteen 1,2,4,5-Tetrazine derivatives was studied by using two computational techniques, namely, quantitative structure activity relationship (QSAR) and molecular docking. QSAR models of the antitumor activity of some 1,2,4,5-Tetrazine derivatives were established in gas and solvent phases which exhibited good statistical values for both cases. Whereas, multiple linear regression (MLR) procedure was used to obtain the best QSAR models and the leave-one-out (LOO) method to estimate the predictivity of our models. The most and the least active compounds were docked with the protein (3C4E) to confirm those obtained results from QSAR models and elucidate the binding mode between this type of compounds and corresponding protein.

Combined QSAR, molecular docking and molecular dynamics study on new Acetylcholinesterase and Butyrylcholinesterase inhibitors.

BACKGROUND AND PURPOSE: This work deals with several molecular modeling methods used to discover new therapeutic agents for treating the Alzheimer's disease (AD). The cholinergic hypothesis was initially presented over 30 years ago and suggests that a dysfunction of acetylcholine containing neurons in the brain. Acetylcholinesterase (AChE) and Butyrylcholinesterase (BuChE) are of the keys targets of drugs for treating AD. METHODS: QSAR, Molecular Docking/Dynamics and ADME properties were carried out in order to study 36 compounds that belong to the 4-[(diethylamino)methyl]-phenol derivatives and test their AChE and BuChE inhibitory activities, MOE, HyperChem and others softwares were used to find the best compounds with high affinity. RESULTS: The QSAR models exhibited good statistical values for both targets AChE (R2adj = 0.660, q2 = 0.70, F-ratio = 18.008) and BuChE (R2adj = 0.726, q2 = 0.75, F-ratio = 31.864). The interactions between the studied inhibitors and our targets were further explored through molecular docking and molecular dynamics simulations. A few key residues (TRP279, TYR334, PHE330 and TRP84) at the binding site of AChE and key residues (HIS438, TYR332, PHE329 and TRP82) at the binding site of BuChE were identified. CONCLUSION: Based on this study compounds 23 and 28 have no violated Lipinski's rule of five and thus, showing the possibility of being potential candidates for further studies in drug development process against the AChE and BuChE targets respectively.