Computational investigation of unsaturated ketone derivatives as MAO-B inhibitors by using QSAR, ADME/Tox, molecular docking, and molecular dynamics simulations

Chemoinformatics Study of Benzodiazepine-1, 2, 3-triazole Derivatives Targeting Butyrylcholinesterase

This study aims to assess the potential bioactivity of newly designed benzodiazepine-1,2,3-triazole derivatives using in-silico methodologies, with a primary focus on elucidating their inhibitory interactions with the butyrylcholinesterase (BuChE) enzyme, which is implicated in Alzheimer's disease. We employed multiple linear regression (MLR) methods to conduct a quantitative structure-activity relationship (QSAR) analysis on a collection of 31 benzodiazepine-1,2,3-triazole derivatives, with the goal of investigating, assessing, and predicting their activities, as well as designing novel compounds. This approach yielded highly accurate results, with coefficients of determination (R²) of 0.77 and 0.81 for the training and test datasets, respectively. Additionally, the optimized compounds were subjected to an Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) analysis, demonstrating their potential as non-hepatotoxic agents with enhanced absorption and blood-brain barrier permeability. To further validate these findings, the most favorable docking conformations were analyzed using molecular dynamics (MD) simulations with GROMACS software, predicting the stability of the formed complexes. These simulations underscored the critical role of hydrogen bonds in stabilizing the compounds at the BuChE receptor binding site. The results hold great promise for the development of innovative benzodiazepine-1,2,3-triazole derivatives as effective BuChE inhibitors, potentially leading to therapeutic interventions for Alzheimer's disease.

Structure-based virtual screening of novel natural products as chalcone derivatives against SARS-CoV-2 Mpro

Coronavirus disease 2019 (COVID-19), which is caused by SARS-CoV-2, has spread quickly around the world, causing a global pandemic. It has infected more than 500 million people as of April 28, 2022. Much research has been reported to stop the virus from spreading, but there are currently no approved medicines to treat COVID-19. In this work, a dataset of 142 natural products collected from various medicinal plants was used to perform structure-based virtual screening (SBVS) through the combined application of molecular docking and molecular dynamics (MD) simulation methods. First, the dataset of compounds was optimized using the density functional theory (DFT) approach. The optimized compounds were then submitted to the first screening, which was done by the pKCM web server to look for drug-likeness and the PyRx to look for binding affinity. Among the 142 natural substances, 10 compounds were selected for docking validation. Compounds that interact with CYS145 and LEU141, the essential catalytic residues, as well as compounds with binding affinities less than -8.0 kcal/mol, are considered promising anti-SARS-CoV-2 drug candidates. The top-ranked compounds were then evaluated by MD simulations and MM-GBSA method. These results could help researchers come up with new natural compounds that could be used to treat SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Homology modeling, virtual screening, molecular docking, molecular dynamic (MD) simulation, and ADMET approaches for identification of natural anti-Parkinson agents targeting MAO-B protein

Monoamine oxidase-B (MAO-B) is a flavin-dependent enzyme involved in various neurodegenerative disorders. Here, a dataset of 142 chalcone derivatives, collected from various natural plants, was screened by combining structure-based virtual screening and ADMET approaches. The goal is to discover novel natural chalcones as potential MAO-B inhibitors. With the help of the Gaussian 09.5 software, the 3D chemical structures of compounds were optimized using the DFT method. The 3D structure of the hMAO-B enzyme was built using the Modeller software. The optimized structures were subjected to virtual screening by Autodock Vina, implicated in PyRx software. Among the 142 natural substances, 43 were selected based on their binding affinity. Then, the pharmacokinetic proprieties and toxicity of these compounds were evaluated using ADMET analysis. Ten compounds were predicted to have ADMET characteristics with no side effects. The binding modes and interactions of the top selected compounds were then evaluated using AutoDock 4.2. Compounds P60 and P81 were found to be potential inhibitors of MAO-B compared to rasagiline, safinamid, and selegiline, the reference drugs. The stability of the selected compounds was confirmed by MD simulation. Based on this finding, compounds P60 and P81 could be considered potential hMAO-B inhibitors.

Computer-aided drug design applied to a series of pyridinyl imidazole derivatives targeting p38α MAP kinase: 2D-QSAR, docking, MD simulation, and ADMET investigations

The p38 mitogen-activated protein kinase (MAPK) is a crucial target for chronic inflammation.