Department of Chemistry, Science and Art Faculty, Siirt Universty, Siirt, Turkey.
J Mol Model. 2012 Jan;18(1):65-82. doi: 10.1007/s00894-011-1024-5. Epub 2011 Mar 31.
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(2)(training)= 0.848, R(2)(test))= 0.904, with a cross-validated q(2) = 0.780).
两种不同的方法，即电子构象和遗传算法方法（EC-GA），被结合起来识别药效团并从分子结构描述符预测 1,4-二氢吡啶（已知的钙通道拮抗剂）的拮抗剂活性。为了识别药效团，对符合的电子构象矩阵（ECMC）进行了排列，其中包括原子电荷作为对角元素，键序和原子间距离作为非对角元素。选择活性最高的化合物的 ECMC 作为模板，并与给定公差内的其他化合物的 ECMC 进行比较，以揭示与药效团相关的电子构象子矩阵（ECSA）。遗传算法用于搜索对活性贡献最大的最佳参数组合子集。应用具有最佳 10 个参数的模型对训练（50 个化合物）和测试（22 个化合物）集进行预测，得到了令人满意的结果（训练集的 R²（training）= 0.848，测试集的 R²（test）= 0.904，交叉验证 q² = 0.780）。
