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作 者:顾云兰[1,2] 陶建清[1] 费正皓[1] 张根成[2]
机构地区:[1]盐城师范学院江苏省滩涂生物资源与环境保护重点建设实验室,江苏盐城224051 [2]盐城师范学院应用化学与环境工程研究所,江苏盐城224051
出 处:《武汉大学学报(理学版)》2009年第3期268-272,共5页Journal of Wuhan University:Natural Science Edition
基 金:江苏省重点建设实验室开放基金资助项目(JLCBE07025);江苏省高校自然科学基础研究项目(08KJD150021);盐城师范学院自然科学研究项目(08YCKL077)
摘 要:用DFT-B3LYP方法分别在STO-3G,6-31G*,6-311G**基组水平上对25种硝基芳烃化合物进行全优化计算获得了相应量子化学参数,利用线性逐步回归法(LSR)建立硝基芳烃对斜生栅列藻毒性的定量构效关系(QSAR)模型,采用内部及外部双重验证的办法深入分析和检验模型的稳健性,选出最佳模型.与此同时,利用人工神经网络误差反传算法(BP网络)建立了非线性QSAR模型.LSR和BP建模的复相关系数(R2),去一法(LOO)交互检验复相关系数(R2cv),外部预测样本复相关系数(R2ext)分别为0.926,0.866,0.843和0.938,0.763,0.843,表明所建立的QSAR模型的稳定性和预测能力良好.结果表明:硝基芳烃对斜生栅列藻的毒性与次最低空轨道能、最正的硝基净电荷和前沿轨道能级差的相关性较好.The DFT-B3LYP method with the basis set STO-3G, 6-31G^* ,6-311G^** was employed to calculate the molecular geometric and electronic structures of 25 nitroaromatic compounds. Here three quantitative structure-activity relationship (QSAR) models were built by multiple linear stepwise regressions (LSR). The estimation stability and generalization ability of these models were strictly analyzed by both internal and external validation and the best one was selected. Meanwhile, standard back-propagation algorithm Of artificial neural network (BP) was used to establish a nonlinear QSAR model. The correlation coefficient (RZ), leave-one-out(LOO) cross validation Rcv^2, predicted values versus experimental ones of external samples R2ex, of established LSR and BP models are 0. 926, 0. 866, 0. 843 and 0. 938, 0. 763, 0. 843, respectively. These show that the QSAR models have both favorable estimation stability and good prediction capability. The results indicate that there is a good multivariate linear relationship between the experimental values of the toxicity to Scenedesmus obliquus and the secondary lowest unoccupied orbital energy, the most positive net charge of the NO2, the energy difference between the highest occupied orbit and the lowest unoccupied orbit .
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