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
With the artificial neural network（ANN） method combined with the multiple linear regression（MLR）,based on a series of quantum chemical descriptors and molecular connectivity indexes,quantitative structure-activity...With the artificial neural network（ANN） method combined with the multiple linear regression（MLR）,based on a series of quantum chemical descriptors and molecular connectivity indexes,quantitative structure-activity relationship（QSAR） models to predict the acute toxicity（-lgEC50） of substituted aromatic compounds to Photobacterium phosphoreum were established.Four molecular descriptors that appear in the MLR model,namely,the second order valence molecular connectivity index（2XV）,the energy of the highest occupied molecular orbital（EHOMO）,the logarithm of n-octyl alcohol/water partition coefficient（logKow） and the Connolly molecular area（MA）,were inputs of the ANN model.The root-mean-square error（RMSE） of the training and validation sets of the ANN model are 0.1359 and 0.2523,and the correlation coefficient（R） is 0.9810 and 0.8681,respectively.The leave-one-out（LOO） cross validated correlation coefficient（Q L2OO） of the MLR and ANN models is 0.6954 and 0.6708,respectively.The result showed that the two methods are complementary in the calculations.The regression method gave support to the neural network with physical explanation,and the neural network method gave a more accurate model for QSAR.In addition,some insights into the structural factors affecting the acute toxicity and toxicity mechanism of substituted aromatic compounds were discussed.展开更多
Twenty eight alkyl(1-phenylsulfonyl) cycloalkane carboxylates were computed at the B3LYP/6-31G* level. Based on linear solvation energy theory, two quantitative correlation equations of the molecular structures of alk...Twenty eight alkyl(1-phenylsulfonyl) cycloalkane carboxylates were computed at the B3LYP/6-31G* level. Based on linear solvation energy theory, two quantitative correlation equations of the molecular structures of alkyl(1-phenylsulfonyl) cycloalkane carboxylate com- pounds to their chromatographic retention (capacity factor lgKW) and the toxicity for photo- bacterium phosphoreum (–lgEC50) were developed by using the molecular structural parameters as theoretical descriptors (r2 = 0.9501, 0.9488). The two quantitative correlation equations were consequently cross validated by leave-one-out (LOO) validation method with q2 of 0.9113 and 0.9281, respectively. The result showed that the two equations achieved in this work by B3LYP/6-31G* are both more advantageous than those from AM1, and can be used to predict the lgKW and –lgEC50 of congeneric organics.展开更多
Phenylthio-carboxylates were computed at the B3LYP/6-31G* level with DFT method. Based on linear solvation energy theory, the structural parameters were firstly taken as theoretical descriptors, and the correspondin...Phenylthio-carboxylates were computed at the B3LYP/6-31G* level with DFT method. Based on linear solvation energy theory, the structural parameters were firstly taken as theoretical descriptors, and the corresponding linear solvation energy relationship (LSER) equation (r = 0.8989) to the toxicity of photobacterium phosphoreum (–lgEC50) was thus obtained. Then the structural and thermodynamic parameters were taken as theoretical descriptors, and as a result the other corresponding correlation equation (r = 0.9274) relating to –lgEC50 was provided. The two equations achieved in this work by B3LYP/6-31G* are both more advantageous than that from AM1.展开更多
A new molecular structural characterization（MSC）method called molecular vertexes correlative index（MVCI）was constructed in this paper.The index was used to describe the structures of 45 compounds and a quantitativ...A new molecular structural characterization（MSC）method called molecular vertexes correlative index（MVCI）was constructed in this paper.The index was used to describe the structures of 45 compounds and a quantitative structure-activity relationship（QSAR）model of toxicity（–lgEC50）was obtained through multiple linear regression（MLR）and stepwise multiple regression（SMR）.The correlation coefficient（R）of the model was 0.912,and the standard deviation（SD）of the model was 0.525.The estimation stability and prediction ability of the model were strictly analyzed by both internal and external validations.The Leave-One-Out（LOO）Cross-Validation（CV）correlation coefficient（RCV）was 0.816 and the standard deviation（SDCV）was 0.739,respectively.For the external validation,the correlation coefficient（Rtest）was 0.905 and the standard deviation（SDtest）was 0.520,respectively.The results showed that the index was superior in molecular structural representation.The stability and predictability of the model were good.展开更多
Polybrominated diphenyl ether congeners (PBDEs) might activate the AhR (aromatic hydrocarbon receptor) signal transduction, and thus might have an adverse effect on the health of humans and wildlife. Because of the li...Polybrominated diphenyl ether congeners (PBDEs) might activate the AhR (aromatic hydrocarbon receptor) signal transduction, and thus might have an adverse effect on the health of humans and wildlife. Because of the limited experimental data, it is important and necessary to develop structure-based models for prediction of the toxicity of the compounds. In this study, a new molecular structure representation, molecular hologram, was employed to investigate the quantitative relationship between toxicity and molecular structures for 18 PBDEs. The model with the significant correlation and robustness (r <sup>2</sup> = 0.991, q <sup>2</sup> <sub>LOO</sub> = 0.917) was developed. To verify the robustness and prediction capacity of the derived model, 14 PBDEs were randomly selected from the database as the training set, while the rest were used as the test set. The results generated under the same modeling conditions as the optimal model are as follows: r <sup>2</sup> = 0.988, q <sup>2</sup> <sub>LOO</sub> = 0.598, r <sup>2</sup> <sub>pred</sub> = 0.955, and RMSE (root-mean-square of errors) = 0.155, suggesting the excellent ability of the derived model to predict the toxicity of PBDEs. Furthermore, the structural features and molecular mechanism related to the toxicity of PBDEs were explored using HQSAR color coding.展开更多
Toxicities （-1gEC50） of 16 phenolic compounds against Q67 were determined, and structural parameters as well as thermodynamic parameters of these compounds were obtained through fully optimized calculations by using...Toxicities （-1gEC50） of 16 phenolic compounds against Q67 were determined, and structural parameters as well as thermodynamic parameters of these compounds were obtained through fully optimized calculations by using B3LYP method of density functional theory （DFT） at the 6-311G^＊＊ level. Moreover, a 3-parameter （molecular average polarizability （α）, heat energy corrected value （Eth） and the most positive hydrogen atomic charge （qH^＋）） correlation model with R^2 = 0.981 and q^2 = 0.967 to predict -1gEC50 was obtained from experimental data based on the above-mentioned parameters as theoretical descriptors. Therein a was the most significant on -1gEC50. Variance Inflation Factors （VIF）, t-value and cross-validation were applied to verify the model, confirming that the resultant model has fairly better stability and predictive ability to predict -1gEC50 of similar compounds.展开更多
In current paper, a quantitative structure-activity relationship （QSAR） study was performed for the prediction of acute toxicity of aromatic amines. A set of 56 compounds was randomly divided into a training set of ...In current paper, a quantitative structure-activity relationship （QSAR） study was performed for the prediction of acute toxicity of aromatic amines. A set of 56 compounds was randomly divided into a training set of 46 compounds and a test set of 10 compounds. The electronic and topological descriptors computed by the Scigress package and Dragon software were used as predictor variables. Multiple linear regression （MLR） and support vector machine （SVM） were utilized to build the linear and nonlinear QSAR models, respectively. The obtained models with five descriptors show strong predictive ability. The linear model fits the training set with R2 = 0.71, with higher SVM values of R2 = 0.77. The validation results obtained from the test set indicate that the SVM model is comparable or superior to that obtained by MLR, both in terms of prediction ability and robustness.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.
基金supported by the Natural Science Foundation of Fujian Province (D0710019)the Natural Science Foundation of Overseas Chinese Affairs Office of the State Council (06QZR09)
文摘With the artificial neural network（ANN） method combined with the multiple linear regression（MLR）,based on a series of quantum chemical descriptors and molecular connectivity indexes,quantitative structure-activity relationship（QSAR） models to predict the acute toxicity（-lgEC50） of substituted aromatic compounds to Photobacterium phosphoreum were established.Four molecular descriptors that appear in the MLR model,namely,the second order valence molecular connectivity index（2XV）,the energy of the highest occupied molecular orbital（EHOMO）,the logarithm of n-octyl alcohol/water partition coefficient（logKow） and the Connolly molecular area（MA）,were inputs of the ANN model.The root-mean-square error（RMSE） of the training and validation sets of the ANN model are 0.1359 and 0.2523,and the correlation coefficient（R） is 0.9810 and 0.8681,respectively.The leave-one-out（LOO） cross validated correlation coefficient（Q L2OO） of the MLR and ANN models is 0.6954 and 0.6708,respectively.The result showed that the two methods are complementary in the calculations.The regression method gave support to the neural network with physical explanation,and the neural network method gave a more accurate model for QSAR.In addition,some insights into the structural factors affecting the acute toxicity and toxicity mechanism of substituted aromatic compounds were discussed.
基金This work was financially supported by the National Basic Research Program of China (2003CB415002), the China Postdoctoral Science Foundation (No. 2003033486) and the Natural Science Research Fund of University in Jiangsu (04KJB150149)
文摘Twenty eight alkyl(1-phenylsulfonyl) cycloalkane carboxylates were computed at the B3LYP/6-31G* level. Based on linear solvation energy theory, two quantitative correlation equations of the molecular structures of alkyl(1-phenylsulfonyl) cycloalkane carboxylate com- pounds to their chromatographic retention (capacity factor lgKW) and the toxicity for photo- bacterium phosphoreum (–lgEC50) were developed by using the molecular structural parameters as theoretical descriptors (r2 = 0.9501, 0.9488). The two quantitative correlation equations were consequently cross validated by leave-one-out (LOO) validation method with q2 of 0.9113 and 0.9281, respectively. The result showed that the two equations achieved in this work by B3LYP/6-31G* are both more advantageous than those from AM1, and can be used to predict the lgKW and –lgEC50 of congeneric organics.
基金This work was supported by the China Postdoctoral Science Foundation (No. 2003033486) National Natural Science Foundation of China (No. 20177008)
文摘Phenylthio-carboxylates were computed at the B3LYP/6-31G* level with DFT method. Based on linear solvation energy theory, the structural parameters were firstly taken as theoretical descriptors, and the corresponding linear solvation energy relationship (LSER) equation (r = 0.8989) to the toxicity of photobacterium phosphoreum (–lgEC50) was thus obtained. Then the structural and thermodynamic parameters were taken as theoretical descriptors, and as a result the other corresponding correlation equation (r = 0.9274) relating to –lgEC50 was provided. The two equations achieved in this work by B3LYP/6-31G* are both more advantageous than that from AM1.
基金supported by the Foundation of Education Bureau,Sichuan Province (09ZB036)Technology Bureau,Sichuan Province (2006j13-141)
文摘A new molecular structural characterization（MSC）method called molecular vertexes correlative index（MVCI）was constructed in this paper.The index was used to describe the structures of 45 compounds and a quantitative structure-activity relationship（QSAR）model of toxicity（–lgEC50）was obtained through multiple linear regression（MLR）and stepwise multiple regression（SMR）.The correlation coefficient（R）of the model was 0.912,and the standard deviation（SD）of the model was 0.525.The estimation stability and prediction ability of the model were strictly analyzed by both internal and external validations.The Leave-One-Out（LOO）Cross-Validation（CV）correlation coefficient（RCV）was 0.816 and the standard deviation（SDCV）was 0.739,respectively.For the external validation,the correlation coefficient（Rtest）was 0.905 and the standard deviation（SDtest）was 0.520,respectively.The results showed that the index was superior in molecular structural representation.The stability and predictability of the model were good.
基金Supported by the Key Project of the National Natural Science Foundation of China (Grant No. 20737001)the National Natural Science Foundation Key Project of China (Grant No. 20737001)the Science and Technology Development Founda-tion Project of Nanjing Medical University (Grant No. 06NMUM021)
文摘Polybrominated diphenyl ether congeners (PBDEs) might activate the AhR (aromatic hydrocarbon receptor) signal transduction, and thus might have an adverse effect on the health of humans and wildlife. Because of the limited experimental data, it is important and necessary to develop structure-based models for prediction of the toxicity of the compounds. In this study, a new molecular structure representation, molecular hologram, was employed to investigate the quantitative relationship between toxicity and molecular structures for 18 PBDEs. The model with the significant correlation and robustness (r <sup>2</sup> = 0.991, q <sup>2</sup> <sub>LOO</sub> = 0.917) was developed. To verify the robustness and prediction capacity of the derived model, 14 PBDEs were randomly selected from the database as the training set, while the rest were used as the test set. The results generated under the same modeling conditions as the optimal model are as follows: r <sup>2</sup> = 0.988, q <sup>2</sup> <sub>LOO</sub> = 0.598, r <sup>2</sup> <sub>pred</sub> = 0.955, and RMSE (root-mean-square of errors) = 0.155, suggesting the excellent ability of the derived model to predict the toxicity of PBDEs. Furthermore, the structural features and molecular mechanism related to the toxicity of PBDEs were explored using HQSAR color coding.
基金supported by the Natural Science Foundation of Zhejiang Province (No. 2008Y507280)
文摘Toxicities （-1gEC50） of 16 phenolic compounds against Q67 were determined, and structural parameters as well as thermodynamic parameters of these compounds were obtained through fully optimized calculations by using B3LYP method of density functional theory （DFT） at the 6-311G^＊＊ level. Moreover, a 3-parameter （molecular average polarizability （α）, heat energy corrected value （Eth） and the most positive hydrogen atomic charge （qH^＋）） correlation model with R^2 = 0.981 and q^2 = 0.967 to predict -1gEC50 was obtained from experimental data based on the above-mentioned parameters as theoretical descriptors. Therein a was the most significant on -1gEC50. Variance Inflation Factors （VIF）, t-value and cross-validation were applied to verify the model, confirming that the resultant model has fairly better stability and predictive ability to predict -1gEC50 of similar compounds.
基金Supported by the Ministry of Environmental Protection of China(No.2011467037)
文摘In current paper, a quantitative structure-activity relationship （QSAR） study was performed for the prediction of acute toxicity of aromatic amines. A set of 56 compounds was randomly divided into a training set of 46 compounds and a test set of 10 compounds. The electronic and topological descriptors computed by the Scigress package and Dragon software were used as predictor variables. Multiple linear regression （MLR） and support vector machine （SVM） were utilized to build the linear and nonlinear QSAR models, respectively. The obtained models with five descriptors show strong predictive ability. The linear model fits the training set with R2 = 0.71, with higher SVM values of R2 = 0.77. The validation results obtained from the test set indicate that the SVM model is comparable or superior to that obtained by MLR, both in terms of prediction ability and robustness.
