迷幻性苯烷基胺类化合物的QSAR研究  

The QSAR study of hallucinogenic phenylalkylamines

作　　者：张佳瑛[1] 范英芳[1] 成素丽[1] 

机构地区：[1]山西大学分子科学研究所,山西太原030006

出　　处：《计算机与应用化学》2009年第4期495-498,共4页Computers and Applied Chemistry

基　　金：山西省自然科学基金资助项目(2007011025);山西省留学归国基金(2006)资助项目

摘　　要：采用量子化学密度泛函B3LYP法,用6-311+G(d,p)基组,计算38个苯烷基胺类化合物的电子结构参数;利用多元线性回归(multiple linear regression,MLR)法,筛选出影响化合物迷幻活性显著的6个变量,并建立其结构参数与迷幻活性之间的定量关系(MLR模型);同时,利用人工神经网络(artificial neural network,ANN)法建立相应的QSAR模型(ANN模型)以资对比。所建MLR模型的相关系数R=0.9340,标准误差Se=0.2068;ANN模型的相关系数R=0.9992,标准误差Se=0.0036。结果表明人工神经网络法获得了比多元线性回归方法更精密的拟合效果,可望在QSAR研究中发挥重要作用。For 38 hallucinogenic phenylalkylamines, quantum chemistry calculation of electronic properties were carried out at density functional theory （DFT） B3LYP/6-311 ＋ G（d,p） level. For 33 compounds as training set, 6 important parameters were selected and the quantitative structure-activity relationship （QSAR） model was set up by multiple linear regressions （MLR） method. Furthermore, using artificial neural network （ANN） , the QSAR model was obtained in order to make contrast. For the artificial neural network method, the correlation coefficient R = 0. 9992 and the standard error Se = 0.0036, while for the multiple linear regression analysis R = 0.9340 and Se = 0.2068. This indicates that the fitted performance of neural network method is better than that of regression model and ANN method could play an important role in QSAR study.

关 键 词：人工神经网络 多元线性回归 定量结构一活性关系 苯烷基胺 

分 类 号：O641.12[理学—物理化学]