The molecular electronegativity interaction vector （MEIV） was used to describe the molecular structure of 30 selected esters. Two excellent QSTR models were built up by using multiple linear regression （MLR） and p...The molecular electronegativity interaction vector （MEIV） was used to describe the molecular structure of 30 selected esters. Two excellent QSTR models were built up by using multiple linear regression （MLR） and partial least-squares regression （PLS）. The correlation coefficients （R） of the two models were 0.945 and 0.941, respectively. The models were evaluated by performing the cross validation with the leave-one-out （LOO） procedure. The cross-verification correlation coefficients （RCV） of the two models were 0.921 and 0.919, respectively. The results showed that the models constructed in this work could provide estimation stability and favorable predictive ability.展开更多
利用SAS 9.0中的最小R2增量法选择法,对卤代苯酚类化合物小鼠口服LD50的定量构效关系进行了研究.利用计算机应用程序,在AM1和PM3模式下计算了30个卤代苯酚类化合物的14种量化参数,用24个化合物研究它们对小鼠口服急性毒性(LD50)的影响,...利用SAS 9.0中的最小R2增量法选择法,对卤代苯酚类化合物小鼠口服LD50的定量构效关系进行了研究.利用计算机应用程序,在AM1和PM3模式下计算了30个卤代苯酚类化合物的14种量化参数,用24个化合物研究它们对小鼠口服急性毒性(LD50)的影响,经统计分析,又引入了多个参数的交叉项,以消除参数间的共线性.利用线性回归方法获得苯酚类化合物小鼠经口急性毒性预测模型.经模型验证及leave-one-out交互验证,筛选出下面的模型:Logtox=0.00003464*TTT+0.56640*Polar+0.00091484*LHP-0.00214*PH-0.00001903*LEC,(R-Square=0.9917 and C(p)=5.0000).用另6个化合物作为测试集,证明该模型具有很好的预测能力,取得了良好的结果,为定量评估和预测其他卤代苯酚类化合物的有机毒性提供了良好的QSAR模型.展开更多
The molecular electronegativity distance vector（MEDV） was applied to characterize the molecular structures of 30 organophosphorous compounds. Optimum MEDV descriptors were selected by using the variable selection an...The molecular electronegativity distance vector（MEDV） was applied to characterize the molecular structures of 30 organophosphorous compounds. Optimum MEDV descriptors were selected by using the variable selection and modeling method based on the prediction（VSMP） technique. The quantitative structure-toxicity relationship（QSTR） model was built for acute toxicity（96h pLC50） of organophosphorous compounds to steelhead. The developed QSTR model with strictly internal and external validations presents relatively high correlation coefficient（R2） of 0.9518, leave-one-out（LOO） cross-validated correlation coefficient（Q2LOO） of 0.9355, and leave-many-out（LMO） cross-validated correlation coefficient（Q2LMO） of 0.9290. The robustness of the model was confirmed by the y-randomization test（R2yrand = 0.0772 and Q2 yrand = –0.5313） and bootstrapping（R2bstr = 0.9502 and Q2 bstr = 0.9177） method. The result of external validation, Q2F1 = 0.9336, Q2F2 = 0.9336, Q2F3 = 0.9447, r2 m = 0.8120, and CCC = 0.9602, shows that the QSTR model has a high predictive ability.展开更多
基金supported by the Youth Foundation of Education Bureau, Sichuan Province (09ZB036)Technology Bureau, Sichuan Province (2006j13-141)
文摘The molecular electronegativity interaction vector （MEIV） was used to describe the molecular structure of 30 selected esters. Two excellent QSTR models were built up by using multiple linear regression （MLR） and partial least-squares regression （PLS）. The correlation coefficients （R） of the two models were 0.945 and 0.941, respectively. The models were evaluated by performing the cross validation with the leave-one-out （LOO） procedure. The cross-verification correlation coefficients （RCV） of the two models were 0.921 and 0.919, respectively. The results showed that the models constructed in this work could provide estimation stability and favorable predictive ability.
文摘利用SAS 9.0中的最小R2增量法选择法,对卤代苯酚类化合物小鼠口服LD50的定量构效关系进行了研究.利用计算机应用程序,在AM1和PM3模式下计算了30个卤代苯酚类化合物的14种量化参数,用24个化合物研究它们对小鼠口服急性毒性(LD50)的影响,经统计分析,又引入了多个参数的交叉项,以消除参数间的共线性.利用线性回归方法获得苯酚类化合物小鼠经口急性毒性预测模型.经模型验证及leave-one-out交互验证,筛选出下面的模型:Logtox=0.00003464*TTT+0.56640*Polar+0.00091484*LHP-0.00214*PH-0.00001903*LEC,(R-Square=0.9917 and C(p)=5.0000).用另6个化合物作为测试集,证明该模型具有很好的预测能力,取得了良好的结果,为定量评估和预测其他卤代苯酚类化合物的有机毒性提供了良好的QSAR模型.
基金the financial support from the National Natural Science Foundation of China(21207024 and 21407032)the Provincial Natural Science Foundation of Guangxi(2014GXNSFAA118060,2014GXNSFBA118233 and 2013GXNSFBA019228)
文摘The molecular electronegativity distance vector（MEDV） was applied to characterize the molecular structures of 30 organophosphorous compounds. Optimum MEDV descriptors were selected by using the variable selection and modeling method based on the prediction（VSMP） technique. The quantitative structure-toxicity relationship（QSTR） model was built for acute toxicity（96h pLC50） of organophosphorous compounds to steelhead. The developed QSTR model with strictly internal and external validations presents relatively high correlation coefficient（R2） of 0.9518, leave-one-out（LOO） cross-validated correlation coefficient（Q2LOO） of 0.9355, and leave-many-out（LMO） cross-validated correlation coefficient（Q2LMO） of 0.9290. The robustness of the model was confirmed by the y-randomization test（R2yrand = 0.0772 and Q2 yrand = –0.5313） and bootstrapping（R2bstr = 0.9502 and Q2 bstr = 0.9177） method. The result of external validation, Q2F1 = 0.9336, Q2F2 = 0.9336, Q2F3 = 0.9447, r2 m = 0.8120, and CCC = 0.9602, shows that the QSTR model has a high predictive ability.
