Molecular design, molecular docking and ADMET study of cyclic sulfonamide derivatives as SARS-CoV-2 inhibitors

2D-QSAR-guided design of potent carbamate-based inhibitors of acetylcholinesterase

Alzheimer's disease (AD) causes a progressive decline in memory, along with impairments in other cognitive abilities. The main pharmacological target for Alzheimer's disease (AD) treatment is acetylcholinesterase (AChE), a biochemical enzyme belonging to the cholinesterase (ChE) family. In the search for novel hit compoundswith potential as future Alzheimer's therapies, a series of carbamates derivatives were designed and evaluated using computational approaches including QSAR modeling, molecular docking, ADMET profiling, and molecular dynamics simulations. The following study focused on the development of a QSAR model with satisfactory statistical properties. ADMET analysis on the designed ligands, demonstrated good pharmacokinetic properties. Molecular docking identified M6 as a promising AChE binder with a docking score of -11.200 kcal/mol, while the Donepezil control returned a docking score of -10.800 kcal/mol. The validity of the docked complex was confirmed using molecular dynamics simulations, where the trajectory plots of M6 were found to be stable and consistent over 100 ns intervals. The enclosed study highlights M6 as a novel chemical starting point (CSP) (i.e., hit compound) targeting AChE as a potential therapeutic strategy against AD.

Designing highly efficient heterocyclic inhibitors for SARS-CoV-2 3C-like proteinase: a comprehensive in silico study

To address the limitations of current COVID-19 treatments, we conducted an integrated in-silico investigation to design potential drugs with proven efficacy against the virus. We developed Quantitative Structure-Activity Relationship (QSAR) models using a database of 63 Aromatic heterocyclic compounds, focusing on key parameters Effective Diameter (ED) and Diameter Maximum (DM). Our models, utilizing multi-linear regression (MLR) and Artificial Neural Network (ANN), were validated according to OECD principles and successfully used to predict unexplored aromatic heterocyclic compounds with Pyridine Cores. Compound 4 (Dexbrompheniramine) exhibited high inhibition against the SARS coronavirus 3 C-like protease, leading to the design of two new molecules (compounds 15 and 16) with enhanced activity based on structural enhancements from the QSAR model. Docking studies and molecular dynamics simulations confirmed the improved binding energies and stability of compounds 15 and 16, with compound 15 showing remarkable stability and strong binding affinity with the 3 C-like proteinase (1P9U). This comprehensive in-silico review identifies compound 15 as a promising candidate for experimental evaluation as a potential COVID-19 drug, highlighting a significant advancement in our battle against the pandemic.

In silico design of novel CDK2 inhibitors through QSAR, ADMET, molecular docking and molecular dynamics simulation studies

The present study aims to investigate about the quantitative structure-activity relationship (QSAR) of a series of Thiazole derivatives reported as anticancer agents (hepatocellular carcinoma), using principally the electronic descriptors calculated by the DFT method and by applying the multiple linear regression method. The developed model showed good statistical parameters (R2 = 0.725, R2adj = 0.653, MSE = 0.060, R2test = 0.827, Q2cv = 0.536). The energy EHOMO orbital, electronic energy (TE), shape coefficient (I), number of rotatable bonds (NROT), and index of refraction (n) were revealed to be the main descriptors influencing the anti-cancer activity. Further, new Thiazole derivatives have been designed and their activities and pharmacokinetic properties have been predicted using the validated QSAR model. The designed molecules were then assessed to molecular docking (MD), and molecular dynamic (MDs) simulation accompanied by the calculation of the binding affinity using MMPBSA script according to 100 ns a simulation trajectory, to study both their affinity and their stability towards CDK2 as a target protein for the cancer disease treatment. This research concluded with the identification of four new CDK2 inhibitors which are A1, A3, A5, and A6 showing good pharmacokinetic properties. The MDs results revealed that the newly designed compound A5 remained stable in the active center of the discovered CDK2 protein, indicating its potential as a novel inhibitor for the treatment of hepatocellular carcinoma. The current findings may eventually contribute to the development of robust CDK2 inhibitors in the future.Communicated by Ramaswamy H. Sarma.

Computational approach investigation bioactive molecules from Saussurea Costus plant as SARS-CoV-2 main protease inhibitors using reverse docking, molecular dynamics simulation, and pharmacokinetic ADMET parameters

SARS-COV-2 virus causes (COVID-19) disease; it has become a global pandemic since 2019 and has negatively affected all aspects of human life. Scientists have made great efforts to find a reliable cure, vaccine, or treatment for this emerging disease. Efforts have been directed towards using medicinal plants as alternative medicines, as the active chemical compounds in them have been discovered as potential antiviral or anti-inflammatory agents. In this research, the potential of Saussurea costus (S. Costus) or QUST Al Hindi chemical consistent as potential antiviral agents was investigated by using computational methods such as Reverse Docking, ADMET, and Molecular Dynamics with different proteases COVID-19 such as PDB: 2GZ9; 6LU7; 7AOL, 6Y2E, 6Y84. The results of Reverse Docking the complex between 6LU7 proteases and Cynaropicrin compound being the best complex, as the same result, is achieved by molecular dynamics. Also, the toxicity testing result from ADMET method proved that the complex is the least toxic and the safest possible drug. In addition, 6LU7-Cynaropicrin complex obeyed Lipinski rule; it formed ≤5 H-bond donors and ≤10 H bond acceptors, MW < 500 Daltons, and octanol/water partition coefficient <5.