Hemmateenejad Bahram, Akhond Morteza, Miri Ramin, Shamsipur Mojtaba
Department of Chemistry, Shiraz University, Shiraz, Iran.
J Chem Inf Comput Sci. 2003 Jul-Aug;43(4):1328-34. doi: 10.1021/ci025661p.
A QSAR algorithm, principal component-genetic algorithm-artificial neural network (PC-GA-ANN), has been applied to a set of newly synthesized calcium channel blockers, which are of special interest because of their role in cardiac diseases. A data set of 124 1,4-dihydropyridines bearing different ester substituents at the C-3 and C-5 positions of the dihydropyridine ring and nitroimidazolyl, phenylimidazolyl, and methylsulfonylimidazolyl groups at the C-4 position with known Ca(2+) channel binding affinities was employed in this study. Ten different sets of descriptors (837 descriptors) were calculated for each molecule. The principal component analysis was used to compress the descriptor groups into principal components. The most significant descriptors of each set were selected and used as input for the ANN. The genetic algorithm (GA) was used for the selection of the best set of extracted principal components. A feed forward artificial neural network with a back-propagation of error algorithm was used to process the nonlinear relationship between the selected principal components and biological activity of the dihydropyridines. A comparison between PC-GA-ANN and routine PC-ANN shows that the first model yields better prediction ability.
一种定量构效关系（QSAR）算法，即主成分-遗传算法-人工神经网络（PC-GA-ANN），已应用于一组新合成的钙通道阻滞剂，这些阻滞剂因其在心脏疾病中的作用而备受关注。本研究采用了一个数据集，其中包含124种在二氢吡啶环的C-3和C-5位置带有不同酯取代基，且在C-4位置带有硝基咪唑基、苯基咪唑基和甲基磺酰基咪唑基的1,4-二氢吡啶，它们具有已知的Ca(2+)通道结合亲和力。为每个分子计算了十组不同的描述符（837个描述符）。主成分分析用于将描述符组压缩为主成分。每组中最显著的描述符被选出并用作人工神经网络的输入。遗传算法（GA）用于选择最佳的提取主成分集。使用具有误差反向传播算法的前馈人工神经网络来处理所选主成分与二氢吡啶生物活性之间的非线性关系。PC-GA-ANN与常规PC-ANN的比较表明，第一个模型具有更好的预测能力。
