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
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
29 aromatic compounds were computed at the HF/6-31G^＊ level. Based on linear solvation energy theory firstly, the parameters of molecular structure were taken as theoretical descriptors, and the corresponding linear ...29 aromatic compounds were computed at the HF/6-31G^＊ level. Based on linear solvation energy theory firstly, the parameters of molecular structure were taken as theoretical descriptors, and the corresponding linear solvation energy relationship （LSER） （r^2= 0.8993, q^2=0.8559） between the structural parameters and inhibition phytotoxicity to the seed germination rate of cucumis （-lgGC50） was thus obtained. Then the parameters of molecular structure and thermodynamics were taken as theoretical descriptors, and as a result the other corresponding correlation equation （r^2=0.9268, q^2=0.8960） relating to -lgGC50 was achieved. The two equations obtained in this work by HF/6-31G^＊ are both more advantageous than that from AM 1.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
Predicting the logarithm of hexadecane/air partition coefficient(L)for organic compounds is crucial for understanding the environmental behavior and fate of organic compounds and developing prediction models with poly...Predicting the logarithm of hexadecane/air partition coefficient(L)for organic compounds is crucial for understanding the environmental behavior and fate of organic compounds and developing prediction models with polyparameter linear free energy relationships.Herein,two quantitative structure activity relationship(QSAR)models were developed with 1272 L values for the organic compounds by using multiple linear regression(MLR)and support vector machine(SVM)algorithms.On the basis of the OECD principles,the goodness of fit,robustness and predictive ability for the developed models were evaluated.The SVM model was first developed,and the predictive capability for the SVM model is slightly better than that for the MLR model.The applicability domain(AD)of these two models has been extended to include more kinds of emerging pollutants,i.e.,oraganosilicon compounds.The developed QSAR models can be used for predicting L values of various organic compounds.The van derWaals interactions between the organic compound and the hexadecane have a significant effect on the L value of the compound.These in silico models developed in current study can provide an alternative to experimental method for high-throughput obtaining L values of organic 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
The relationship between chemical structures and biodegradation rates （k b） of 22 polycyclic aromatic hydrocarbons （PAHs） was studied using density functional theory （DFT） and stepwise multiple linear regression...The relationship between chemical structures and biodegradation rates （k b） of 22 polycyclic aromatic hydrocarbons （PAHs） was studied using density functional theory （DFT） and stepwise multiple linear regression analysis （SMLR） method.The equilibrium geometries and vibration frequency have been investigated at the B3LYP/6-31＋G（d,p） level by thinking Solvent effects using a selfconsistent reaction field （SCRF） based on the polarizable continuum model （PCM）.It was concluded that the biodegradation rate was closely related to its molecular structure,and there is one high correlation coefficient between the in-plane bending vibration frequency of the conjugated ring of PAHs （Freq） and k b.By means of regression analysis,the main factors affecting the biodegradation rate were obtained and the equation of quantitative structure-activity relationship （QSAR） was successfully established kb =-0.653＋0.001Freq＋0.068CQ＋0.049N1.Statistical evaluation of the developed QSAR showed that the relationships were statistically significant and the model had good predictive ability.The fact that a bending frequency is more important than the HOMO or LUMO energies in predicting k b suggests that the bending of benzene ring might play an important role in the enzymatic catalysis of the initial oxidation step.展开更多
The relationship between chemical structures and photodegradation activity of 12 PAHs is studied using DFT and HF methods, and stepwise multiple linear regression analysis method. The equilibrium geometries and vibrat...The relationship between chemical structures and photodegradation activity of 12 PAHs is studied using DFT and HF methods, and stepwise multiple linear regression analysis method. The equilibrium geometries and vibration frequency have been investigated by considering Solvent effects using a selfconsistent reaction field based on the polarizable continuum model. With DFT and HF methods, different quantum chemical structural descriptors are obtained by quantum chemical calculation and the results with DFT method are better for QSAR model. It is concluded that the photodegradation activity is closely related to its molecular structure. In the regression analysis, the main factors affecting photodegradation rate include the energy of the highest occupied orbital EHOMO and the number of six-carbon benzene ring N1, and the QSAR model successfully established is logkb = 6.046 + 54.830EHOMO + 0.272N1. Statistical evaluation of the developed QSAR shows that the relationships are statistically significant and the model has good predictive ability. EHOMO is the most important factor influcing the photodegradation of PAHs, because the higher EHOMO is, the more easily electron will be excited and the more easily molecular will be degraded. Comparison of the photodegradation of PAHs with their biodegradation shows that the committed step of biodegradation is that the effects of microorganisms make the chemical bond break, while in the committed step of photodegradation PAHs eject electrons.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 (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 Province (04KJB150149)
文摘29 aromatic compounds were computed at the HF/6-31G^＊ level. Based on linear solvation energy theory firstly, the parameters of molecular structure were taken as theoretical descriptors, and the corresponding linear solvation energy relationship （LSER） （r^2= 0.8993, q^2=0.8559） between the structural parameters and inhibition phytotoxicity to the seed germination rate of cucumis （-lgGC50） was thus obtained. Then the parameters of molecular structure and thermodynamics were taken as theoretical descriptors, and as a result the other corresponding correlation equation （r^2=0.9268, q^2=0.8960） relating to -lgGC50 was achieved. The two equations obtained in this work by HF/6-31G^＊ are both more advantageous than that from AM 1.
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 National Natural Science Foundation of China (No.21936005)
文摘Predicting the logarithm of hexadecane/air partition coefficient(L)for organic compounds is crucial for understanding the environmental behavior and fate of organic compounds and developing prediction models with polyparameter linear free energy relationships.Herein,two quantitative structure activity relationship(QSAR)models were developed with 1272 L values for the organic compounds by using multiple linear regression(MLR)and support vector machine(SVM)algorithms.On the basis of the OECD principles,the goodness of fit,robustness and predictive ability for the developed models were evaluated.The SVM model was first developed,and the predictive capability for the SVM model is slightly better than that for the MLR model.The applicability domain(AD)of these two models has been extended to include more kinds of emerging pollutants,i.e.,oraganosilicon compounds.The developed QSAR models can be used for predicting L values of various organic compounds.The van derWaals interactions between the organic compound and the hexadecane have a significant effect on the L value of the compound.These in silico models developed in current study can provide an alternative to experimental method for high-throughput obtaining L values of organic 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.
基金supported by the National Natural Science Foundation of China (No. 40976041,20775074)
文摘The relationship between chemical structures and biodegradation rates （k b） of 22 polycyclic aromatic hydrocarbons （PAHs） was studied using density functional theory （DFT） and stepwise multiple linear regression analysis （SMLR） method.The equilibrium geometries and vibration frequency have been investigated at the B3LYP/6-31＋G（d,p） level by thinking Solvent effects using a selfconsistent reaction field （SCRF） based on the polarizable continuum model （PCM）.It was concluded that the biodegradation rate was closely related to its molecular structure,and there is one high correlation coefficient between the in-plane bending vibration frequency of the conjugated ring of PAHs （Freq） and k b.By means of regression analysis,the main factors affecting the biodegradation rate were obtained and the equation of quantitative structure-activity relationship （QSAR） was successfully established kb =-0.653＋0.001Freq＋0.068CQ＋0.049N1.Statistical evaluation of the developed QSAR showed that the relationships were statistically significant and the model had good predictive ability.The fact that a bending frequency is more important than the HOMO or LUMO energies in predicting k b suggests that the bending of benzene ring might play an important role in the enzymatic catalysis of the initial oxidation step.
基金supported by the National Natural Science Foundation of China(Nos.40976041 and 20775074)
文摘The relationship between chemical structures and photodegradation activity of 12 PAHs is studied using DFT and HF methods, and stepwise multiple linear regression analysis method. The equilibrium geometries and vibration frequency have been investigated by considering Solvent effects using a selfconsistent reaction field based on the polarizable continuum model. With DFT and HF methods, different quantum chemical structural descriptors are obtained by quantum chemical calculation and the results with DFT method are better for QSAR model. It is concluded that the photodegradation activity is closely related to its molecular structure. In the regression analysis, the main factors affecting photodegradation rate include the energy of the highest occupied orbital EHOMO and the number of six-carbon benzene ring N1, and the QSAR model successfully established is logkb = 6.046 + 54.830EHOMO + 0.272N1. Statistical evaluation of the developed QSAR shows that the relationships are statistically significant and the model has good predictive ability. EHOMO is the most important factor influcing the photodegradation of PAHs, because the higher EHOMO is, the more easily electron will be excited and the more easily molecular will be degraded. Comparison of the photodegradation of PAHs with their biodegradation shows that the committed step of biodegradation is that the effects of microorganisms make the chemical bond break, while in the committed step of photodegradation PAHs eject electrons.
