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    Öğe
    4D-QSAR analysis and pharmacophore modeling: Electron conformational-genetic algorithm approach for penicillins
    (Pergamon-Elsevier Science Ltd, 2011) Yanmaz, Ersin; Saripinar, Emin; Sahin, Kader; Gecen, Nazmiye; Copur, Fatih
    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, SEtraining = 0.044, R-test(2) = 0.892, SEtest = 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, SEtraining = 0.056, R-test(2) = 0.840, SEtest = 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. (C) 2011 Elsevier Ltd. All rights reserved.
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    Öğe
    Application of electron conformational-genetic algorithm approach to 1,4-dihydropyridines as calcium channel antagonists: pharmacophore identification and bioactivity prediction
    (Springer, 2012) Gecen, Nazmiye; Saripinar, Emin; Yanmaz, Ersin; Sahin, Kader
    Two different approaches, namely the electron conformational and genetic algorithm methods (EC-GA), were combined to identify a pharmacophore group and to predict the antagonist activity of 1,4-dihydropyridines (known calcium channel antagonists) from molecular structure descriptors. To identify the pharmacophore, electron conformational matrices of congruity (ECMC)-which include atomic charges as diagonal elements and bond orders and interatomic distances as off-diagonal elements-were arranged for all compounds. The ECMC of the compound with the highest activity was chosen as a template and compared with the ECMCs of other compounds within given tolerances to reveal the electron conformational submatrix of activity (ECSA) that refers to the pharmacophore. The genetic algorithm was employed to search for the best subset of parameter combinations that contributes the most to activity. Applying the model with the optimum 10 parameters to training (50 compounds) and test (22 compounds) sets gave satisfactory results (R-training(2) = 0.848, R-test(2) = 0.904, with a cross-validated q(2) = 0.780).