Objective To measure the acute toxicity of halogenated benzenes to bacteria in natural waters and to study quantitative relationships between the structure and activity of chemicals. Methods The concentration values c...Objective To measure the acute toxicity of halogenated benzenes to bacteria in natural waters and to study quantitative relationships between the structure and activity of chemicals. Methods The concentration values causing 50% inhibition of bacteria growth （24h-IC50） were determined according to the bacterial growth inhibition test method. The energy of the lowest unoccupied molecular orbital and the net charge of carbon atom of 20 halogenated benzenes were calculated by the quantum chemical MOPAC program. Results The logl/IC50 values ranged from 4.79 for 2,4-dinitrochlorobenzene to 3.65 for chlorobenzene. A quantitative structure-activity relationship model was derived from the toxicity and structural parameters： logl/IC50 =-0.531（ELUMO）＋1.693（Qc）＋0.163（logP）＋3.375. This equation was found to fit well （r^2=0.860, s=0.106）, and the average percentage error was only 1.98%. Conclusion Halogenated benzenes and alkyl halogenated benzenes are non-polar narcotics, and have hydrophobicity-dependent toxicity. The halogenated phenols and anilines exhibit a higher toxic potency than their hydrophobicity, whereas 2,4-dinitrochlorobenzene is electrophile with the halogen acting as the leaving group.展开更多
The two-dimensional Quantitative Structure-Activity Relationship （2D-QSAR） models have been developed to estimate and predict the inhibitory activities of a series of HEPT analogues against HIV-1 by using quantum ch...The two-dimensional Quantitative Structure-Activity Relationship （2D-QSAR） models have been developed to estimate and predict the inhibitory activities of a series of HEPT analogues against HIV-1 by using quantum chemical parameters and physicochemical parameters. The best model of three parameters yields r = 0.908, r^2A = 0.800 and s = 0.467 based on stepwise multiple regression （SMR） method. The stability of the model has been verified by t-test, and the results show that the model has perfect robustness. The predictive power of QSAR models has been tested by Leave-One-Out （LOO） and Leave-Group（regularly random set）-Out（LGO） procedure Cross-Validation methodology. The r^2cv of 0.755 and r^2pred of 0.759 were obtained, respectively.展开更多
Quantitative structure–activity relationship study using artificial neural network (ANN) methodology were conducted to predict the inhibition constants of 127 symmetrical and unsymmetrical cyclic urea and cyclic cyan...Quantitative structure–activity relationship study using artificial neural network (ANN) methodology were conducted to predict the inhibition constants of 127 symmetrical and unsymmetrical cyclic urea and cyclic cyanoguanidine derivatives containing different substituent groups such as: benzyl, isopropyl, 4-hydroxybenzyl, ketone, oxime, pyrazole, imidazole, triazole and having anti-HIV-1 protease activities. The results obtained by artificial neural network give advanced regression models with good prediction ability. The two optimal artificial neural network models obtained have coefficients of determination of 0.746 and 0.756. The lowest prediction’s root mean square error obtained is 0.607. Artificial neural networks provide improved models for heterogeneous data sets without splitting them into families. Both the external and cross-validation methods are used to validate the performances of the resulting models. Randomization test is employed to check the suitability of the models.展开更多
The molecular electronegativity-distance vector (MEDV) is employed todescribe the chemical structure of organic pollutants.Quantitative linear relationships between themolecular descriptors and BCF values are develope...The molecular electronegativity-distance vector (MEDV) is employed todescribe the chemical structure of organic pollutants.Quantitative linear relationships between themolecular descriptors and BCF values are developed by best subset regression and partial leastsquare regression analysis.The main structural factors influencing the bioactivities are -CH2,-X,-Cnot 【,- C not 【,-O-.The high values of r2 and q2_(LOO) present good estimation ability and stabilityof models.The prediction power for external samples is validated by the model developed from thetraining set.展开更多
基金This work was supported by the National 973 Great Foundation Research Item of China (2002CB412303) and the National Natural Science Foundation of Jiangsu Province (BK2004118).
文摘Objective To measure the acute toxicity of halogenated benzenes to bacteria in natural waters and to study quantitative relationships between the structure and activity of chemicals. Methods The concentration values causing 50% inhibition of bacteria growth （24h-IC50） were determined according to the bacterial growth inhibition test method. The energy of the lowest unoccupied molecular orbital and the net charge of carbon atom of 20 halogenated benzenes were calculated by the quantum chemical MOPAC program. Results The logl/IC50 values ranged from 4.79 for 2,4-dinitrochlorobenzene to 3.65 for chlorobenzene. A quantitative structure-activity relationship model was derived from the toxicity and structural parameters： logl/IC50 =-0.531（ELUMO）＋1.693（Qc）＋0.163（logP）＋3.375. This equation was found to fit well （r^2=0.860, s=0.106）, and the average percentage error was only 1.98%. Conclusion Halogenated benzenes and alkyl halogenated benzenes are non-polar narcotics, and have hydrophobicity-dependent toxicity. The halogenated phenols and anilines exhibit a higher toxic potency than their hydrophobicity, whereas 2,4-dinitrochlorobenzene is electrophile with the halogen acting as the leaving group.
文摘The two-dimensional Quantitative Structure-Activity Relationship （2D-QSAR） models have been developed to estimate and predict the inhibitory activities of a series of HEPT analogues against HIV-1 by using quantum chemical parameters and physicochemical parameters. The best model of three parameters yields r = 0.908, r^2A = 0.800 and s = 0.467 based on stepwise multiple regression （SMR） method. The stability of the model has been verified by t-test, and the results show that the model has perfect robustness. The predictive power of QSAR models has been tested by Leave-One-Out （LOO） and Leave-Group（regularly random set）-Out（LGO） procedure Cross-Validation methodology. The r^2cv of 0.755 and r^2pred of 0.759 were obtained, respectively.
文摘Quantitative structure–activity relationship study using artificial neural network (ANN) methodology were conducted to predict the inhibition constants of 127 symmetrical and unsymmetrical cyclic urea and cyclic cyanoguanidine derivatives containing different substituent groups such as: benzyl, isopropyl, 4-hydroxybenzyl, ketone, oxime, pyrazole, imidazole, triazole and having anti-HIV-1 protease activities. The results obtained by artificial neural network give advanced regression models with good prediction ability. The two optimal artificial neural network models obtained have coefficients of determination of 0.746 and 0.756. The lowest prediction’s root mean square error obtained is 0.607. Artificial neural networks provide improved models for heterogeneous data sets without splitting them into families. Both the external and cross-validation methods are used to validate the performances of the resulting models. Randomization test is employed to check the suitability of the models.
基金Supported by the Natural Science Foundation of the Education Commission of Jiangsu Province (Grant No. 07KJB610061)the National Natural Science Founda-tion of China (Grant No. 20577023)+1 种基金the "973" Program (Grant No. 2003CB415002)the "863" Program (Grant No. 2001AA640601-4)
文摘The molecular electronegativity-distance vector (MEDV) is employed todescribe the chemical structure of organic pollutants.Quantitative linear relationships between themolecular descriptors and BCF values are developed by best subset regression and partial leastsquare regression analysis.The main structural factors influencing the bioactivities are -CH2,-X,-Cnot 【,- C not 【,-O-.The high values of r2 and q2_(LOO) present good estimation ability and stabilityof models.The prediction power for external samples is validated by the model developed from thetraining set.
