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.

Design of new molecules against cervical cancer using DFT, theoretical spectroscopy, 2D/3D-QSAR, molecular docking, pharmacophore and ADMET investigations.

Cervical cancer is a major health problem of women. Hormone therapy, via aromatase inhibition, has been proposed as a promising way of blocking estrogen production as well as treating the progression of estrogen-dependent cancer. To overcome the challenging complexities of costly drug design, in-silico strategy, integrating Structure-Based Drug Design (SBDD) and Ligand-Based Drug Design (LBDD), was applied to large representative databases of 39 quinazoline and thioquinazolinone compound derivatives. Quantum chemical and physicochemical descriptors have been investigated using density functional theory (DFT) and MM2 force fields, respectively, to develop 2D-QSAR models, while CoMSIA and CoMFA descriptors were used to build 3D-QSAR models. The robustness and predictive power of the reliable models were verified, via several validation methods, leading to the design of 6 new drug-candidates. Afterwards, 2 ligands were carefully selected using virtual screening methods, taking into account the applicability domain, synthetic accessibility, and Lipinski's criteria. Molecular docking and pharmacophore modelling studies were performed to examine potential interactions with aromatase (PDB ID: 3EQM). Finally, the ADMET properties were investigated in order to select potential drug-candidates against cervical cancer for experimental in vitro and in vivo testing.

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.

Development of novel monoamine oxidase B (MAO-B) inhibitors by combined application of docking-based alignment, 3D-QSAR, ADMET prediction, molecular dynamics simulation, and MM_GBSA binding free energy.

Unsaturated ketone derivatives are known as inhibitors of monoamine oxidase B (MAO-B), a potential drug target of Parkinson's disease. Here, docking-based alignment, 3 D-QSAR (three-dimensional quantitative structure-activity relationship) studies, ADMET (absorption, distribution, metabolism, excretion, and toxicity) prediction, molecular dynamics (MD) simulation, and MM_GBSA binding free energy were performed on a novel series of MAO-B inhibitors. The objective is to predict new MAO-B inhibitors with high potency activity. The 3 D-QSAR models were created using comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA). Molecular docking findings indicated that compounds with strong inhibitory efficacy also had a high binding affinity. 3 D-QSAR studies showed the importance of steric, electrostatic, and H-bond acceptor fields on the inhibitory activity of MAO-B. Based on the appropriate 3 D-QSAR model, a new series of MAO-B inhibitors were predicted and their pharmacokinetic characteristics were evaluated using in silico ADMET prediction. All screened compounds show good oral bioavailability without any side effects. Moreover, the dynamic behavior and stability of the most active compounds were evaluated using MD simulations. The results showed that unsaturated ketone derivatives are stable and compact during the 100 ns of MD simulation. Finally, the binding free energy of complexes was determined using the MM_GBSA method; the findings indicated that the T1 compound is more stable (ΔGbinding = -409.506 KJ/mol) than the data set's highest active compound (ΔGbinding = -31.883 KJ/mol).Communicated by Ramaswamy H. Sarma.

3D-QSAR modeling and molecular docking studies on a series of 2, 4, 5-trisubstituted imidazole derivatives as CK2 inhibitors.

Protein case in kinase II alpha subunit (CK2) plays an imperative function in treating cancer disease. Herein, we have performed a three-dimensional quantitative structure activity relationship (3D-QSAR), and molecular docking analysis on a novel series of 2, 4, 5-trisubstituted imidazole derivatives in order to design potent kinase II alpha subunit (CK2) inhibitors. The 3D-QSAR methods such as comparative molecular similarity indexes analysis (COMSIA), and the comparative molecular field analysis (COMFA) were investigate using twenty-four molecules of 2, 4, 5-trisubstituted imidazole derivatives as anticancer agent. The best COMFA and COMSIA models exhibit excellent Q2 values of 0.66 and 0.75 and R2 values of 0.98 and 0.99 respectively. To check the validity of the selected COMFA and COMSIA models, a variety of validation tests were utilized: Internal validation analyses, and externally validation beside Y-randomization according to the principles of the Organization for Economic Co-operation and Development (OECD), and the Golbraikh and Tropsha's criteria for the validation of 3D-QSAR models. The proposed models for COMFA and COMSIA analysis have been successful. The developed models, indicating that they were reliable for activity prediction. Based on the preceding results, we designed several new potent molecules. Such outcome can proffer helpful theoretical references for future experimental studies.Communicated by Ramaswamy H. Sarma.

Design of novel quinoline derivatives as antibreast cancer using 3D-QSAR, molecular docking and pharmacokinetic investigation.

Breast cancer has been one of the most challenging women's cancers and leading cause of mortality for decades. There are several studies being conducted all the time to find a cure for breast cancer. Quinoline derivatives have shown their potential as antitumor agents in breast cancer therapy. In this work, three-dimensional quantitative structure-activity relationships (3D-QSAR) and molecular docking with aromatase enzyme (Protein Data Bank: 3S7S) studies were performed to suggest the current scenario of quinoline derivatives as antitumor agents and to refine the path of these derivatives to discover and develop new drugs against breast cancer. For developing the 3D-QSAR model, comparative molecular similarity indices analysis (CoMSIA) and comparative molecular field analysis (CoMFA) were included. To attain the high level of predictability, the best CoMSIA model was applied. External validation utilizing a test set has been used in order to validate the predictive capabilities of the built model. According to the findings, electrostatic, hydrophobic and hydrogen bond donor, and acceptor fields had a significant impact on antibreast cancer activity. Thus, we generated a variety of novel effective aromatase inhibitors based on prior findings and we predicted their inhibitory activity using the built model. In addition, absorption, distribution, metabolism, elimination and toxicity properties were employed to explore the effectiveness of new drug candidates.

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.

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

Unsaturated ketone derivatives are known as monoamine oxidase B (MAO-B) inhibitors, a potential drug target for Parkinson's disease. Here, molecular modeling studies, including 2D-QSAR, ADMET prediction, molecular docking, and MD simulation, were performed on a new series of MAO-B inhibitors. The objective is to identify new MAO-B inhibitors with high inhibitory efficacy. The developed 2D-QSAR model was based on the descriptors of MOE software. The most appropriate model, using the partial least squares regression (PLS regression) method, yielded 0.88 for the determination coefficient (r2), 0.28 for the root-mean-square error (RMSE), and 0.2 for the mean absolute error (MAE). The predictive capacity of the generated model was evaluated by internal and external validations, which gave the Q2 and R2test values of 0.81 and 0.71, respectively. The ability of a compound to be orally active was determined using the drug-likeness and ADMET prediction. The results indicate that most of the compounds have moderate pharmacokinetic characteristics without any side effects. Furthermore, the affinity of the ligands (unsaturated ketone derivatives) to the MAO-B receptor was determined using molecular docking. The top conformers were then subjected to MD simulation. This research may pave the way for the development of novel unsaturated ketone derivatives capable of inhibiting the MAO-B enzyme.

2D-QSAR and docking study of a series of coumarin derivatives as inhibitors of CDK (anticancer activity) with an application of the molecular docking method.

Quantitative Structure Activity Relationship (QSAR) analysis techniques are tools largely utilized in many research fields, including drug discovery processes. In this work electronic descriptors are calculated with the Gaussian 03W software using the DFT method with the BecKe 3-parameters exchange functional and Lee-Yang-Parr correlation functional, with Kohn and Sham orbitals (KS) developed on a Gaussian Basis of type 6-31G (d), in combination with five Lipinski parameters that have been calculated with ChemOffice software, in order to develop a statistically verified 2D-QSAR model able to predict the biological activity of new molecules belonging to the same range of coumarins rather than chemical synthesis and biological evaluations that require more time and resources. Two QSAR models against both MCF-7 and HepG-2 cell lines are obtained using the multiple linear regression method. The predictive power of these models has been confirmed by internal and external validation. The Leverage method was used to determine the domain of applicability of the 2D-QSAR models developed. The results indicate that the best QSAR model is the one that links the 2D descriptors with the CDK inhibitory activity of the cell line (HepG-2) R2 = 0.748, R2cv = 0.618, MSE = 0.03 for the learning series and R2 = 0.73, MSE = 0.18 for the test series. This model implies that coumarin inhibitory activity is strongly related to dipole moment and the number of hydrogen bond donors. The results obtained suggest the importance of studying structure-activity relationships as a principal axis in drug design. The docking procedure using AutoDOCK Tools was also used to understand the mechanisms of molecular interactions and consequently, to develop new inhibitors.