QSAR study of CK2 inhibitors by GA-MLR and GA-SVM methods

In this work, the quantitative structure–activity relationship models were developed for predicting activity of a series of compounds such as CK2 inhibitors using multiple linear regressions and support vector machine methods. The data set consisted of 48 compounds was divided into two subsets of training and test set, randomly. The most relevant molecular descriptors were selected using the genetic algorithm as a feature selection tool. The predictive ability of the models was evaluated using Y-randomization test, cross-validation and external test set. The genetic algorithm-multiple linear regression model with six selected molecular descriptors was obtained and showed high statistical parameters (R2 train = 0.893, R2 test = 0.921, Q2 LOO = 0.844, F = 43.17, RMSE = 0.287). Comparison of the results between GA-MLR and GA-SVM demonstrates that GA-SVM provided better results for the training set compounds; however, the predictive quality for both models is acceptable. The results suggest that atomic mass and polarizabilities and also number of heteroatom in molecules are the main independent factors contributing to the CK2 inhibition activity. The predicted results of this study can be used to design new and potent CK2 inhibitors. © 2015 The Author

Analysis of B-RafV600Einhibitors using 2D and 3D-QSAR, molecular docking and pharmacophore studies

In the present work, a molecular modeling study was carried out using 2D and 3D quantitative structure-activity relationships for the various series of compounds known as B-RafV600E inhibitors. For 2D-QSAR analysis, a linear model was developed by MLR based on GA-OLS with appropriate results (Formula presented.)= 0.796, (Formula presented.)= 0.827), which was validated by several external validation techniques. To perform a 3D-QSAR analysis, CoMFA and CoMSIA methods were used. The selected CoMFA model could provide reliable statistical values (Formula presented.) = 0.683, r2=0.947) based on the training set in the biases of the selected alignment. Using the same selected alignment, a statistically reliable CoMSIA model, out of thirty-one different combinations, was also obtained (Formula presented.)= 0.645, r2=0.897). The predictive accuracy of the derived models was rigorously evaluated with the external test set of nineteen compounds based on several validation techniques. Molecular docking simulations and pharmacophore analyses were also performed to derive the true conformations of the most potent inhibitors with B-Raf$$^{\mathrm{V600E}}$$V600E kinase. © 2015, Springer International Publishing Switzerland

Simultaneous spectrophotometric determination of aspirin and dipyridamole in pharmaceutical formulations using the multivariate calibration methods

Background: A mixture of aspirin and dipyridamole has been proved to significantly reduce the stroke recurrence. As the use of mixture of aspirin and dipyridamole is increased, the accurate concentration of each component should be determined to avoid side effects and to improve the treatment process in pharmaceutical preparation. Objective: The goal of the present study is the simultaneous determination of aspirin and dipyridamole mixtures in pharmaceuticals using the chemometrics methods. Methods: The simultaneous spectrophotometric determination of aspirin and dipyridamole was carried out using the genetic algorithm as feature selection, coupled with partial least squares for regression analysis. Results: The linearity range of calibration curve was obtained over the range of 30-250 and 1-20 μg·mL-1 for aspirin and dipyridamole, respectively. The results indicated that the developed methods are of high accuracy and can be used as a simple and fast techniques for analyzing of these compounds. Conclusion: The developed techniques are simple and with high precision and accuracy can be used in real samples for pharmaceutical formulation. © 2018 Bentham Science Publishers

Prediction of superoxide quenching activity of fullerene (C 60) derivatives by genetic algorithm-support vector machine

In this study, the quantitative structure-activity relationship (QSAR) models for the superoxide quenching activity of fullerene derivatives were developed. The dataset was divided into a training set and test set, based on hierarchical clustering technique. Three variables were selected by the genetic algorithm (GA) variable subset selection procedure. Multiple linear regressions (MLR) and support vector machine (SVM) were used as linear and nonlinear methods. Both the linear and nonlinear models could give very satisfactory prediction results: The square correlation coefficient of R2 for the training and test sets were 0.826 and 0.981, by MLR and 0.957 and 0.965, by SVM methods, respectively. The prediction result of the SVM model was better than that obtained by MLR method, which proved SVM was a useful tool in the prediction of the superoxide quenching activity of fullerene derivatives. The results suggested that the minimum atomic partial charge, minimum valence of hydrogen atom, and minimum atomic state energy of oxygen atom, are the main independent factors contributing to the superoxide quenching activity of fullerene derivatives. © 2015 Taylor and Francis Group, LLC

3D-QSAR analysis of MCD inhibitors by CoMFA and CoMSIA

Three-dimensional quantitative structure-activity relationship was developed for series of compounds such as malonyl-CoA decarboxylase antagonists (MCD) using the CoMFA and CoMSIA methods. The statistical parameters for CoMFA (q2=0.558, r2=0.841) and CoMSIA (q2= 0.615, r2 = 0.870) models were derived based on 38 compounds as training set on the basis of the selected alignment. The external predictive abilities of the built models were evaluated by using the test set of nine compounds. From obtained results, the CoMSIA method was found to have highly predictive capability in comparison with CoMFA method. Based on the given results by CoMSIA and CoMFA contour maps, some features that can enhance the activity of compounds as MCD antagonists were introduced and used to design new compounds with a better inhibition activity. © 2015 Bentham Science Publishers

QSPR study on solubility of some fullerenes derivatives using the genetic algorithms - Multiple linear regression

A quantitative structure-property relation study was performed on the solubility of C60 and C70 fullerene derivatives. Topological and geometrical as well as quantum mechanical energy-related and charge distribution-related descriptors, generated from CODESSA, were calculated to define the molecule structures requirement for measuring their correlations with solubility. The best four variables among the other subsets were selected by the genetic algorithm variable subset selection procedure. Modeling of the relationship between selected molecular descriptors and solubility data was achieved by multiple linear regression method (R2train = 0.801, R2test = 0.792, Q2LOO = 0.716, Q2BOOT = 0.674). The robustness and the predictive performance of the proposed model were verified using both internal (cross-validation by leave one out, bootstrap, Y-scrambling) and external statistical validations (external test set by splitting the original data set into training and test sets by k-nearest neighbor (kNN) classification method). Further, the external predictive power of the developed model was examined by considering modified r2 and concordance correlation coefficient values. The reactivity, the polar interactions, the electron-electron repulsion energy, the electronuclear attraction energy, the nuclear-nuclear repulsion energy, and the rotational-vibrational energies were the main independent factors contributing to the solubility of the fullerenes. © 2015 Elsevier B.V. All rights reserved

QSAR study of prolylcarboxypeptidase inhibitors by genetic algorithm: Multiple linear regressions

The predictive analysis based on quantitative structure activity relationships (QSAR) on benzimidazolepyrrolidinyl amides as prolylcarboxypeptidase (PrCP) inhibitors was performed. Molecules were represented by chemical descriptors that encode constitutional, topological, geometrical, and electronic structure features. The hierarchical clustering method was used to classify the dataset into training and test subsets. The important descriptors were selected with the aid of the genetic algorithm method. The QSAR model was constructed, using the multiple linear regressions (MLR), and its robustness and predictability were verified by internal and external cross-validation methods. Furthermore, the calculation of the domain of applicability defines the area of reliable predictions. The root mean square errors (RMSE) of the training set and the test set for GA-MLR model were calculated to be 0.176, 0.279 and the correlation coefficients (R 2) were obtained to be 0.839, 0.923, respectively. The proposed model has good stability, robustness and predictability when verified by internal and external validation. [Figure not available: see fulltext.] © 2015 Indian Academy of Sciences