QSAR and DFT Studies of Some Tacrine-Hydroxamate Derivatives as Inhibitors of Cholinesterase (AChEs) in the Treatment of Alzheimer's Disease

Introduction: This work was devoted to an in silico investigation conducted on twenty-eight Tacrine-hydroxamate derivatives as a potential treatment for Alzheimer's disease using DFT and QSAR modeling techniques. Method(s): The data set was randomly partitioned into a training set (22 compounds) and a test set (6 compounds). Then, fourteen models were built and were used to compute the predicted pIC50 of compounds belonging to the test set. Result(s): Al built models were individualy validated using both internal and external validation methods, including the Y-Randomization test and Golbraikh and Tropsha's model acceptance criteria. Then, one model was selected for its higher R2, R2test, and Q2cv values (R2 = 0.768, R2adj = 0.713, MSE = 0.304, R2test=0.973, Q2cv = 0.615). From these outcomes, the activity of the studied compounds toward the main protease of Cholinesterase (AChEs) seems to be influenced by 4 descriptors, i.e., the total dipole moment of the molecule (mu), number of rotatable bonds (RB), molecular topology radius (MTR) and molecular topology polar surface area (MTPSA). The effect of these descriptors on the activity was studied, in particular, the increase in the total dipole moment and the topological radius of the molecule and the reduction of the rotatable bond and topology polar surface area increase the activity. Conclusion(s): Some newly designed compounds with higher AChEs inhibitory activity have been designed based on the best-proposed QSAR model. In addition, ADMET pharmacokinetic properties were carried out for the proposed compounds, the toxicity results indicate that 7 molecules are nontoxic.Copyright © 2023 Bentham Science Publishers.

QSAR study of a series of peptidomimetic derivatives towards MERS-CoV inhibitors

In this study, we report the quantitative structure activity relationships (QSAR) investigation to determine the relationship between the anti-MERS-CoV activity and a set of chemical descriptors computed using ChemSketch, MarvinSketch and ChemOffice software. Herein, the principal components analysis (PCA), multiple linear regression (MLR) and multiple non-linear regression (MNLR) methods were used with the intention to obtain a reliable QSAR model with good predictive capacity. The original data set of 43 peptidomimetic compounds was randomly divided into training and test set of 35 and 8 compounds, respectively. The values obtained by MLR and MNLR for the determination coefficient are 0.777 and 0.813, respectively. The predictive ability of the MLR model was assessed by external validation using the eight compounds of the test set with predicted determination coefficients R2test of 0.655.

QSAR Modelling of Peptidomimetic Derivatives towards HKU4-CoV 3CL(pro) Inhibitors against MERS-CoV

In this paper, we report the relationship between the anti-MERS-CoV activities of the HKU4 derived peptides for some peptidomimetic compounds and various descriptors using the quantitative structure activity relationships (QSAR) methods. The used descriptors were computed using ChemSketch, Marvin Sketch and ChemOffice software. The principal components analysis (PCA) and the multiple linear regression (MLR) methods were used to propose a model with reliable predictive capacity. The original data set of 41 peptidomimetic derivatives was randomly divided into training and test sets of 34 and 7 compounds, respectively. The predictive ability of the best MLR model was assessed by determination coefficient R-2 = 0.691, cross-validation parameter Q(cv)(2) = 0.528 and the external validation parameter R-test(2) = 0.794.