👤 E Sarıpınar

The electron conformational genetic algorithm (EC-GA) method had been employed by distinguishing between enantiomers for the first time as a 4D-QSAR approach to reveal the pharmacophore (Pha) and to predict the bioactivity of the dipeptidyl boron compounds. The Electron Conformational Matrices of Congruity (ECMCs) were prepared for all conformers of compounds in the data set based on the quantum chemical calculations at HF/3-21 G level in an aqueous medium. The comparison of the ECMCs within the certain tolerances by the EMRE program revealed the pharmacophore for some dipeptidyl boron derivatives. For the selection of the most influential parameters on the activity and the calculation of theoretical activities, the genetic algorithm with the non-linear least square method was used. The final model was validated by the cross-validation method with the division of the data set into training and test items. The 12-parameter model gave excellent statistical results (R Show less

The electron conformational and genetic algorithm methods (EC-GA) were integrated for the identification of the pharmacophore group and predicting the anti HIV-1 activity of tetrahydroimidazo[4,5,1-jk][1,4]benzodiazepinone (TIBO) derivatives. To reveal the pharmacophore group, each conformation of all compounds was arranged by electron conformational matrices of congruity. Multiple comparisons of these matrices, within given tolerances for high active and low active TIBO derivatives, allow the identification of the pharmacophore group that refers to the electron conformational submatrix of activity. The effects of conformations, internal and external validation were investigated by four different models based on an ensemble of conformers and a single conformer, both with and without a test set. Model 1 using an ensemble of conformers for the training (39 compounds) and test sets (13 compounds), obtained by the optimum seven parameters, gave satisfactory results (R²(training) = 0.878, R²(test)= 0.910, q² = 0.840, q²(ext1) = 0.926 and q²(ext2) = 0.900). Show less

In this work, the EC-GA method, a hybrid 4D-QSAR approach that combines the electron conformational (EC) and genetic algorithm optimization (GA) methods, was applied in order to explain pharmacophore (Pha) and predict anti-HIV-1 activity by studying 115 compounds in the class of 1-[(2-hydroxyethoxy)-methyl]-6-(phenylthio) thymine (HEPT) derivatives as non-nucleoside reverse transcriptase inhibitors (NNRTIs). The series of NNRTIs were partitioned into four training and test sets from which corresponding quantitative structure-activity relationship (QSAR) models were constructed. Analysis of the four QSAR models suggests that the three models generated from the training and test sets used in previous works yielded comparable results with those of previous studies. Model 4, the data set of which was partitioned randomly into two training and test sets with 11 descriptors, including electronical and geometrical parameters, showed good statistics both in the regression (r2(training) )= 0.867, r2test = 0.923) and cross-validation (q (2) = 0.811, q2(ext1) = 0.909, q2(ext2) = 0.909) for the training set of 80 compounds and the test set of 27 compounds. The prediction of the anti-HIV-1 activity of HEPT compounds by means of the EC-GA method allowed for a quantitatively consistent QSAR model. In addition, eight novel compounds never tested experimentally have been designed theoretically using model 4. Show less

4D-QSAR studies were performed on a series of 87 penicillin analogues using the electron conformational-genetic algorithm (EC-GA) method. In this EC-based method, each conformation of the molecular system is described by a matrix (ECMC) with both electron structural parameters and interatomic distances as matrix elements. Multiple comparisons of these matrices within given tolerances for high active and low active penicillin compounds allow one to separate a smaller number of matrix elements (ECSA) which represent the pharmacophore groups. The effect of conformations was investigated building model 1 and 2 based on ensemble of conformers and single conformer, respectively. GA was used to select the most important descriptors and to predict the theoretical activity of the training (74 compounds) and test (13 compounds, commercial penicillins) sets. The model 1 for training and test sets obtained by optimum 12 parameters gave more satisfactory results (R(training)(2)=0.861, SE(training)=0.044, R(test)(2)=0.892, SE(test)=0.099, q(2)=0.702, q(ext1)(2)=0.777 and q(ext2)(2)=0.733) than model 2 (R(training)(2)=0.774, SE(training)=0.056, R(test)(2)=0.840, SE(test)=0.121, q(2)=0.514, q(ext1)(2)=0.641 and q(ext2)(2)=0.570). To estimate the individual influence of each of the molecular descriptors on biological activity, the E statistics technique was applied to the derived EC-GA model. Show less