10 quantum chemical descriptors of 21 aromatic compounds have been calculated by the semi-empirical quantum chemical method AM1. The Quantitative Structure-Biodegradability Relationships (QSBR) studies were performe...10 quantum chemical descriptors of 21 aromatic compounds have been calculated by the semi-empirical quantum chemical method AM1. The Quantitative Structure-Biodegradability Relationships (QSBR) studies were performed by the multiple linear regression (MLR), principal component regression (PCR) and back propagation artificial neural network (BP-ANN), respectively. The root mean square error (RMSE) of the training and validation sets of the BP-ANN model are 0.1363 and 0.0244, the mean absolute percentage errors (MAPE) are 0.1638 and 0.0326, the squared correlation coefficients (R^2) are 0.9853 and 0.9996, respectively. The results show that the BP-ANN model achieved a better prediction result than those of MLR and PCR. In addition, some insights into the structural factors affecting the aerobic biodegradation mechanism were discussed in detail.展开更多
Quantitative structure-activity relationship (QSAR) model was developed for pre- dicting the mutagenicity of aromatic compounds. The log revertants data of S. typhimurium TA98 strain from Ames test have been collect...Quantitative structure-activity relationship (QSAR) model was developed for pre- dicting the mutagenicity of aromatic compounds. The log revertants data of S. typhimurium TA98 strain from Ames test have been collected. 225 aromatic compounds were randomly divided into the training set with 186 molecules and test set with 39 molecules. Multiple linear regression (MLR) analysis was used to select six descriptors from thousands of descriptors calculated by semi- empirical AM l and E-dragon methods. The final QSAR model with six descriptors was internal and external validated. In addition, to validate the utility of our QSAR model for the chemical evaluation, three aromatic compounds were taken to test the predictive ability and reliability of the model experimentally. The compounds selected for testing were not based on the predictions, thus spanning the range of predicted probabilities. The subsequently generated results of the Ames test were in good correspondence with the predictions and confirmed this approach as a useful means of predicting likely mutagenic risk of aromatic compounds.展开更多
Structural and thermodynamic parameters of 56 oxygen-containing and 56 sulfur- containing organic compounds were computed at the B3LPY/6-311G** level using density functional theory (DFT) method. Furthermore,the d...Structural and thermodynamic parameters of 56 oxygen-containing and 56 sulfur- containing organic compounds were computed at the B3LPY/6-311G** level using density functional theory (DFT) method. Furthermore,the dependent equations between the experimental data of boiling points (Tb) and theoretical parameters were proposed with SPSS12.0 for windows software,whose correlation coefficients R2 are 0.933 and 0.945. These dependent equations were validated by cross-validation method (q2 are 0.923 and 0.929,respectively). VIF (variance inflation factors) and t-value methods were also used to verify the significance and self-correlationship of each variable. Results indicate that our dependent equation exhibits good prediction ability,and molecular polarizability (α) is the main factor affecting the Tb of oxygen- and sulfur-containing organic compounds. To our interest,obvious dependence could also be found among the oxygen- and sulfur-containing organic compounds' experimental data of boiling points (Tb) with R^2 of 0.857.展开更多
A new molecular structural characterization(MSC) method was constructed in this paper.The structure descriptors were used to describe the structures of 149 compounds.Through multiple linear regression(MLR) and ste...A new molecular structural characterization(MSC) method was constructed in this paper.The structure descriptors were used to describe the structures of 149 compounds.Through multiple linear regression(MLR) and stepwise multiple regression(SMR),a quantitative structure-retention relationship(QSRR) model with 6 variables was obtained.The correlation coefficient(R) of the model was 0.944.Through partial least-squares regression(PLS),another QSRR model with 5 principal components was obtained.The correlation coefficient(R) of the model was 0.941.The estimation stability and prediction ability of the two models was strictly analyzed by both internal and external validations.For the internal validation,the Cross-Validation(CV) correlation coefficients(RCV) for Leave-One-Out(LOO) were 0.931 and 0.932,respectively.For the external validation,the correlation coefficients(Rtest) of the two models were 0.907 and 0.932.The results suggested good stability and predictability of the model.The prediction results are in very good agreement with the experimental values.This paper provided a new and effective method for predicting the chromatography retention time.展开更多
Quantitative structure-property relationships(QSPRs) have been developed to predict the thermal stability for a set of 22 nitroaromatic compounds by means of the theoretical descriptors derived from electrostatic po...Quantitative structure-property relationships(QSPRs) have been developed to predict the thermal stability for a set of 22 nitroaromatic compounds by means of the theoretical descriptors derived from electrostatic potentials on molecular surface. Several techniques, including partial least squares regression(PLS), least-squares support vector machine(LSSVM) and Gaussian process(GP) have been utilized to establish the relationships between the structural descriptor and the decomposition enthalpy. The nonlinear LSSVM and GP models have proven to own a better predictive ability than the linear PLS method. Moreover, owing to its ability to handle both linear- and nonlinear-hybrid relationship, GP gives a stronger fitting ability and a better predictive power than LSSVM, and therefore could be well applied to developing QSPR models for the thermal stability of nitroaromatic explosives.展开更多
New descriptors were constructed and structures of some oxygen-containing organic compounds were parameterized. The multiple linear regression(MLR) and partial least squares regression(PLS) methods were employed t...New descriptors were constructed and structures of some oxygen-containing organic compounds were parameterized. The multiple linear regression(MLR) and partial least squares regression(PLS) methods were employed to build two relationship models between the structures and octanol/water partition coefficients(LogP) of the compounds. The modeling correlation coefficients(R) were 0.976 and 0.922, and the "leave one out" cross validation correlation coefficients(R(CV)) were 0.973 and 0.909, respectively. The results showed that the structural descriptors could well characterize the molecular structures of the compounds; the stability and predictive power of the models were good.展开更多
Optimized calculation of 35 dialkyl phenyl phosphate compounds (OPs) was carded out at the B3LYP/6-31G^* level in Gaussian 98 program. Based on the theoretical linear solvation energy relationship (TLSER) model, ...Optimized calculation of 35 dialkyl phenyl phosphate compounds (OPs) was carded out at the B3LYP/6-31G^* level in Gaussian 98 program. Based on the theoretical linear solvation energy relationship (TLSER) model, the obtained parameters were taken as theoretical descriptors to establish the novel QSPR model for predicting n-octanol/water partition coefficients (lgKow) of OPs. The new model achieved in this work contains three variables, i.e., molecular volume (Vm), dipole moment of the molecules (μ) and enthalpy (H^0). For this model, R^2 = 0.9167 and SD = 0.31 at large t values. In addition, the variation inflation factors (VIF) of variables are all close to 1.0, suggesting high accuracy of the predicting model. And the results of cross-validation test (q^2 = 0.8993) and method validation also showed the model of this study exhibited optimum stability and better predictive power than that from semi-empirical method. The model achieved can be used to predict IgKow of congeneric compounds.展开更多
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 (09QZR07)
文摘10 quantum chemical descriptors of 21 aromatic compounds have been calculated by the semi-empirical quantum chemical method AM1. The Quantitative Structure-Biodegradability Relationships (QSBR) studies were performed by the multiple linear regression (MLR), principal component regression (PCR) and back propagation artificial neural network (BP-ANN), respectively. The root mean square error (RMSE) of the training and validation sets of the BP-ANN model are 0.1363 and 0.0244, the mean absolute percentage errors (MAPE) are 0.1638 and 0.0326, the squared correlation coefficients (R^2) are 0.9853 and 0.9996, respectively. The results show that the BP-ANN model achieved a better prediction result than those of MLR and PCR. In addition, some insights into the structural factors affecting the aerobic biodegradation mechanism were discussed in detail.
基金Supported by the Ministry of Environmental Protection of China(No.2011467037)
文摘Quantitative structure-activity relationship (QSAR) model was developed for pre- dicting the mutagenicity of aromatic compounds. The log revertants data of S. typhimurium TA98 strain from Ames test have been collected. 225 aromatic compounds were randomly divided into the training set with 186 molecules and test set with 39 molecules. Multiple linear regression (MLR) analysis was used to select six descriptors from thousands of descriptors calculated by semi- empirical AM l and E-dragon methods. The final QSAR model with six descriptors was internal and external validated. In addition, to validate the utility of our QSAR model for the chemical evaluation, three aromatic compounds were taken to test the predictive ability and reliability of the model experimentally. The compounds selected for testing were not based on the predictions, thus spanning the range of predicted probabilities. The subsequently generated results of the Ames test were in good correspondence with the predictions and confirmed this approach as a useful means of predicting likely mutagenic risk of aromatic compounds.
基金Supported by the State Science Foundation of China (No. 20737001)
文摘Structural and thermodynamic parameters of 56 oxygen-containing and 56 sulfur- containing organic compounds were computed at the B3LPY/6-311G** level using density functional theory (DFT) method. Furthermore,the dependent equations between the experimental data of boiling points (Tb) and theoretical parameters were proposed with SPSS12.0 for windows software,whose correlation coefficients R2 are 0.933 and 0.945. These dependent equations were validated by cross-validation method (q2 are 0.923 and 0.929,respectively). VIF (variance inflation factors) and t-value methods were also used to verify the significance and self-correlationship of each variable. Results indicate that our dependent equation exhibits good prediction ability,and molecular polarizability (α) is the main factor affecting the Tb of oxygen- and sulfur-containing organic compounds. To our interest,obvious dependence could also be found among the oxygen- and sulfur-containing organic compounds' experimental data of boiling points (Tb) with R^2 of 0.857.
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 was constructed in this paper.The structure descriptors were used to describe the structures of 149 compounds.Through multiple linear regression(MLR) and stepwise multiple regression(SMR),a quantitative structure-retention relationship(QSRR) model with 6 variables was obtained.The correlation coefficient(R) of the model was 0.944.Through partial least-squares regression(PLS),another QSRR model with 5 principal components was obtained.The correlation coefficient(R) of the model was 0.941.The estimation stability and prediction ability of the two models was strictly analyzed by both internal and external validations.For the internal validation,the Cross-Validation(CV) correlation coefficients(RCV) for Leave-One-Out(LOO) were 0.931 and 0.932,respectively.For the external validation,the correlation coefficients(Rtest) of the two models were 0.907 and 0.932.The results suggested good stability and predictability of the model.The prediction results are in very good agreement with the experimental values.This paper provided a new and effective method for predicting the chromatography retention time.
基金Supported by the National Natural Science Foundation of China(No.20502022)
文摘Quantitative structure-property relationships(QSPRs) have been developed to predict the thermal stability for a set of 22 nitroaromatic compounds by means of the theoretical descriptors derived from electrostatic potentials on molecular surface. Several techniques, including partial least squares regression(PLS), least-squares support vector machine(LSSVM) and Gaussian process(GP) have been utilized to establish the relationships between the structural descriptor and the decomposition enthalpy. The nonlinear LSSVM and GP models have proven to own a better predictive ability than the linear PLS method. Moreover, owing to its ability to handle both linear- and nonlinear-hybrid relationship, GP gives a stronger fitting ability and a better predictive power than LSSVM, and therefore could be well applied to developing QSPR models for the thermal stability of nitroaromatic explosives.
基金supported by the Youth Foundation of Education Bureau,Sichuan Province(13ZB0003)
文摘New descriptors were constructed and structures of some oxygen-containing organic compounds were parameterized. The multiple linear regression(MLR) and partial least squares regression(PLS) methods were employed to build two relationship models between the structures and octanol/water partition coefficients(LogP) of the compounds. The modeling correlation coefficients(R) were 0.976 and 0.922, and the "leave one out" cross validation correlation coefficients(R(CV)) were 0.973 and 0.909, respectively. The results showed that the structural descriptors could well characterize the molecular structures of the compounds; the stability and predictive power of the models were good.
基金the State Science Foundation of China (No. 20477018)
文摘Optimized calculation of 35 dialkyl phenyl phosphate compounds (OPs) was carded out at the B3LYP/6-31G^* level in Gaussian 98 program. Based on the theoretical linear solvation energy relationship (TLSER) model, the obtained parameters were taken as theoretical descriptors to establish the novel QSPR model for predicting n-octanol/water partition coefficients (lgKow) of OPs. The new model achieved in this work contains three variables, i.e., molecular volume (Vm), dipole moment of the molecules (μ) and enthalpy (H^0). For this model, R^2 = 0.9167 and SD = 0.31 at large t values. In addition, the variation inflation factors (VIF) of variables are all close to 1.0, suggesting high accuracy of the predicting model. And the results of cross-validation test (q^2 = 0.8993) and method validation also showed the model of this study exhibited optimum stability and better predictive power than that from semi-empirical method. The model achieved can be used to predict IgKow of congeneric compounds.