QSAR, ADMET, molecular docking, and dynamics studies of 1,2,4-triazine-3(2H)-one derivatives as tubulin inhibitors for breast cancer therapy.

Breast cancer remains a leading cause of cancer-related deaths among women globally, necessitating the development of more effective therapeutic agents with minimal side effects. This study explores novel 1,2,4-triazine-3(2H)-one derivatives as potential inhibitors of Tubulin, a pivotal protein in cancer cell division, highlighting a targeted approach in cancer therapy. Using an integrated computational approach, we combined quantitative structure-activity relationship (QSAR) modeling, ADMET profiling, molecular docking, and molecular dynamics simulations to evaluate and predict the efficacy and stability of these compounds. Our QSAR models, developed through rigorous statistical analysis, revealed that descriptors such as absolute electronegativity and water solubility significantly influence inhibitory activity, achieving a predictive accuracy (R2) of 0.849. Molecular docking studies identified compounds with high binding affinities, particularly Pred28, which exhibited the best docking score of - 9.6 kcal/mol. Molecular dynamics simulations conducted over 100 ns provided further insights into the stability of these interactions. Pred28 demonstrated notable stability, with the lowest root mean square deviation (RMSD) of 0.29 nm and root mean square fluctuation (RMSF) values indicative of a tightly bound conformation to Tubulin. The novelty of this work lies in its methodological rigor and the integration of multiple advanced computational techniques to pinpoint compounds with promising therapeutic potential. Our findings advance the current understanding of Tubulin inhibitors and open avenues for the synthesis and experimental validation of these compounds, aiming to offer new solutions for breast cancer treatment.

Multi-combined QSAR, molecular docking, molecular dynamics simulation, and ADMET of Flavonoid derivatives as potent cholinesterase inhibitors.

In searching for a new and efficient therapeutic agent against Alzheimer's disease, a Quantitative structure-activity relationship (QSAR) was derived for 45 Flavonoid derivatives recently synthesized and evaluated as cholinesterase inhibitors. The multiple linear regression method (MLR) was adopted to develop an adequate mathematical model that describes the relationship between a variety of molecular descriptors of the studied compounds and their biological activities (cholinesterase inhibitors). Golbraikh and Tropsha criteria were applied to verify the validity of the built model. The built MLR model was statistically reliable, robust, and predictive (R2 = 0.801, Q2cv = 0.876, R2test = 0.824). Dreiding energy and Molar Refractivity were the major factors that govern the Anti-cholinesterase activity. These results were further exploited to design a new series of Flavonoid derivatives with higher Anti-cholinesterase activities than the existing ones. Thereafter, molecular docking and molecular dynamic studies were performed to predict the binding types of the designed compounds and to investigate their stability at the active site of the Butyrylcholinestérase BuChE protein. The negative and low binding affinity calculated for all designed compounds shows that designed compound 1 has a favorable affinity for the 4TPK. Moreover, molecular dynamics simulation studies confirmed the stability of designed compound 1 in the active pocket of 4TPK over 100 ns. Finally, the ADMET analysis was incorporated to analyze the pharmacokinetics and toxicity parameters. The designed compounds were found to meet the ADMET descriptor criteria at an acceptable level having respectable intestinal permeability and water solubility and can reach the intended destinations.Communicated by Ramaswamy H. Sarma.

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.

Towards designing of a potential new HIV-1 protease inhibitor using QSAR study in combination with Molecular docking and Molecular dynamics simulations.

Human Immunodeficiency Virus type 1 protease (HIV-1 PR) is one of the most challenging targets of antiretroviral therapy used in the treatment of AIDS-infected people. The performance of protease inhibitors (PIs) is limited by the development of protease mutations that can promote resistance to the treatment. The current study was carried out using statistics and bioinformatics tools. A series of thirty-three compounds with known enzymatic inhibitory activities against HIV-1 protease was used in this paper to build a mathematical model relating the structure to the biological activity. These compounds were designed by software; their descriptors were computed using various tools, such as Gaussian, Chem3D, ChemSketch and MarvinSketch. Computational methods generated the best model based on its statistical parameters. The model's applicability domain (AD) was elaborated. Furthermore, one compound has been proposed as efficient against HIV-1 protease with comparable biological activity to the existing ones; this drug candidate was evaluated using ADMET properties and Lipinski's rule. Molecular Docking performed on Wild Type, and Mutant Type HIV-1 proteases allowed the investigation of the interaction types displayed between the proteases and the ligands, Darunavir (DRV) and the new drug (ND). Molecular dynamics simulation was also used in order to investigate the complexes' stability allowing a comparative study on the performance of both ligands (DRV & ND). Our study suggested that the new molecule showed comparable results to that of darunavir and maybe used for further experimental studies. Our study may also be used as pipeline to search and design new potential inhibitors of HIV-1 proteases.

Design of novel benzimidazole derivatives as potential α-amylase inhibitors using QSAR, pharmacokinetics, molecular docking, and molecular dynamics simulation studies.

In the present study, a quantitative relationship between the biological inhibitory activity of alpha-amylase and molecular structures of novel benzimidazole derivatives is analyzed in silico. The best QSAR model screened via MLR technique indicated that the exact mass, topological diameter and numerical rotational bonding structural properties of benzimidazole derivatives highly affect the bioactivity of these compounds against α-amylase. Based on the structural properties identified via linear QSAR model favorable for improving pIC50 of benzimidazole derivatives, fourteen new molecules bearing benzimidazole radicals were designed and their biological inhibitory activity against α-amylase was improved. QSAR model predictions showed that the designed molecules exhibited a higher potential biological level activity IC50 than acarbose used in positive control (IC50= 1.46 µM). Screening of drug-like properties, pharmacokinetics and toxicity of the proposed molecules led to select three molecules as candidates for use as drug aid to ingest starch and glycogen. As a result, using molecular docking simulations, the docking poses of the three molecules inside the α-amylase receptor pocket (PDB code: 1HNY) were predicted. Also, the most important potential interactions between the active amino acid sites in α-amylase protein pocket and the proposed drug molecules were described. The obtained hypotheses regarding the stability of the proposed molecules inside α-amylase pocket were validated by carrying out molecular dynamic simulations in aqueous background similar to the ones of proteins. The DM results confirmed the optimal stability of the α-amylase backbone with the drug molecules proposed in this computational investigation.

Multi-combined 3D-QSAR, docking molecular and ADMET prediction of 5-azaindazole derivatives as LRRK2 tyrosine kinase inhibitors.

The enzyme Leucine-rich repeat kinase 2 (LRRK2) has become a target of therapeutic interest in Parkinson research.Athree-dimensional quantitative structure-activity relationships(3D-QSAR) study was performed on twenty six azaindazole derivatives as LRRK2 inhibitorsobtained using rigid body alignment (Distill). CoMFA and CoMSIA model shave achieved high activity-descriptor relationship efficiency of 96% and 93% as shown by the regression-coefficient (R2=0.961 and 0.933) and were found statistically significant with cross validated coefficient (Q2CV= 0.625 and 0.554), respectively.3D-QSAR models were externally validated by a test set of sixbioactive compounds showing satisfactory predicted correlation coefficient (R2pred) of 0.865 and 0.853 for CoMFA and CoMSIA models, respectively. Besides, Y-randomization test was also performed to ensure the robustness of the obtained3D-QSAR models. This study provides valuable clues to design new compounds against LRRK2. Docking studies suggested that the ligand (new designed compound C2) has more potential than the ligand of reference 4K4 and confirm the obtained results from 3D-QSAR studies. Furthermore, the newly designed compounds and ligand of reference 4K4 were analyzed for their ADMET properties and drug likeness. These results would be of great help in leading optimization for developing new anti-Parkinson drug. Communicated by Ramaswamy H. Sarma.

QSAR study of unsymmetrical aromatic disulfides as potent avian SARS-CoV main protease inhibitors using quantum chemical descriptors and statistical methods.

In silico research was executed on forty unsymmetrical aromatic disulfide derivatives as inhibitors of the SARS Coronavirus (SARS-CoV-1). Density functional theory (DFT) calculation with B3LYP functional employing 6-311 â€‹+ â€‹G(d,p) basis set was used to calculate quantum chemical descriptors. Topological, physicochemical and thermodynamic parameters were calculated using ChemOffice software. The dataset was divided randomly into training and test sets consisting of 32 and 8 compounds, respectively. In attempt to explore the structural requirements for bioactives molecules with significant anti-SARS-CoV activity, we have built valid and robust statistics models using QSAR approach. Hundred linear pentavariate and quadrivariate models were established by changing training set compounds and further applied in test set to calculate predicted IC50 values of compounds. Both built models were individually validated internally as well as externally along with Y-Randomization according to the OECD principles for the validation of QSAR model and the model acceptance criteria of Golbraikh and Tropsha's. Model 34 is chosen with higher values of R2, R2 test and Q2cv (R2 â€‹= â€‹0.838, R2 test â€‹= â€‹0.735, Q2 cv â€‹= â€‹0.757). It is very important to notice that anti-SARS-CoV main protease of these compounds appear to be mainly governed by five descriptors, i.e. highest occupied molecular orbital energy (EHOMO), energy of molecular orbital below HOMO energy (EHOMO-1), Balaban index (BI), bond length between the two sulfur atoms (S1S2) and bond length between sulfur atom and benzene ring (S2Bnz). Here the possible action mechanism of these compounds was analyzed and discussed, in particular, important structural requirements for great SARS-CoV main protease inhibitor will be by substituting disulfides with smaller size electron withdrawing groups. Based on the best proposed QSAR model, some new compounds with higher SARS-CoV inhibitors activities have been designed. Further, in silico prediction studies on ADMET pharmacokinetics properties were conducted.

Data of infrared vibration spectroscopy of cyclotriphosphates.

By taking the IR spectra of several cyclotriphosphates of a resolved structure, has subsequently shown that it is possible to characterize the P3O9 ring by its IR spectrum and, in some favorable cases, to make them Predicted symmetry of the cycle by examining the number, profile and position of the observed infrared bands in the symmetric valence vibration of the POP (νs POP) groups. He identified criteria for each type of symmetry and discussed, using concrete examples, the limits of the infrared method in determining the symmetry of the cycle (all the possible symmetries that a P3O9 cycle can have). Recently, at the Laboratory, studies have been undertaken by A. ABOUIMRANE et al. [1] for the calculation of the normal IR frequencies of the P3O9 cycle for the ideal and real symmetries: D3h, Cs and C3 (Tables 1,2 and 3). Published by Elsevier Inc. This is an open access article under the CC BY license https://doi.org/10.1080/10426507.2017.1333507.