Octanol/water partition coefficient （Kow） is a crucial property for evaluating the environmental behavior and fate of organic compound. Herein, some quantitative structure-property relationship （QSPR） studies were...Octanol/water partition coefficient （Kow） is a crucial property for evaluating the environmental behavior and fate of organic compound. Herein, some quantitative structure-property relationship （QSPR） studies were performed to estimate and predict the lgK ow of substituted anilines. 2D method （multiple linear regression, MLR） and 3D method （comparative molecular field analysis, CoMFA） were applied in this study. Successful 2D and 3D models yielded the correlation coefficient （R2） values of 0.981 and 0.966 and the Leave-One-Out （LOO） cross-validated correlation coefficient （q2） values of 0.933 and 0.820, respectively. The developed models have a highly predictive ability in both internal and external validation. In addition, the results were interpreted in terms of physical and chemical meanings of descriptors and field contribution maps. It showed that the steric and electrostatic properties are the primary factors that govern the lgK ow of substituted anilines. The information obtained from the QSPR models would be helpful to the interpretation of structural features pertinent to the lgK ow of substituted anilines, which may be helpful in estimating the organic compounds＇ potential harm to the environment.展开更多
In this work, some chemometrics methods are applied for the modeling and prediction of the Hildebrand solubility parameter of some polymers. A genetic algorithm （GA） method is designed for the selection of variables...In this work, some chemometrics methods are applied for the modeling and prediction of the Hildebrand solubility parameter of some polymers. A genetic algorithm （GA） method is designed for the selection of variables to construct two models using the multiple linear regression （MLR） and least square-support vector machine （LS-SVM） methods in order to predict the Hildebrand solubility parameter. The MLR method is used to build a linear relationship between the molecular descriptors and the Hildebrand solubility parameter for these compounds. Then the LS-SVM method is utilized to construct the non-linear quantitative structure-activity relationship （QSAR） models. The results obtained using the LS-SVM method are then compared with those obtained for the MLR method; it was revealed that the LS-SVM model was much better than the MLR one. The root-mean-square errors of the training set and the test set for the LS-SVM model were 0.2912 and 0.2427, and the correlation coefficients were 0.9662 and 0.9518, respectively. This paper provides a new and effective method for predicting the Hildebrand solubility parameter for some polymers, and also reveals that the LS-SVM method can be used as a powerful chemometrics tool for the quantitative structure-property relationship （QSPR） studies.展开更多
基金Supported by the NNSF of China (No. 20737001)Program for Environment Protection in Jiangsu Province (201140)
文摘Octanol/water partition coefficient （Kow） is a crucial property for evaluating the environmental behavior and fate of organic compound. Herein, some quantitative structure-property relationship （QSPR） studies were performed to estimate and predict the lgK ow of substituted anilines. 2D method （multiple linear regression, MLR） and 3D method （comparative molecular field analysis, CoMFA） were applied in this study. Successful 2D and 3D models yielded the correlation coefficient （R2） values of 0.981 and 0.966 and the Leave-One-Out （LOO） cross-validated correlation coefficient （q2） values of 0.933 and 0.820, respectively. The developed models have a highly predictive ability in both internal and external validation. In addition, the results were interpreted in terms of physical and chemical meanings of descriptors and field contribution maps. It showed that the steric and electrostatic properties are the primary factors that govern the lgK ow of substituted anilines. The information obtained from the QSPR models would be helpful to the interpretation of structural features pertinent to the lgK ow of substituted anilines, which may be helpful in estimating the organic compounds＇ potential harm to the environment.
文摘In this work, some chemometrics methods are applied for the modeling and prediction of the Hildebrand solubility parameter of some polymers. A genetic algorithm （GA） method is designed for the selection of variables to construct two models using the multiple linear regression （MLR） and least square-support vector machine （LS-SVM） methods in order to predict the Hildebrand solubility parameter. The MLR method is used to build a linear relationship between the molecular descriptors and the Hildebrand solubility parameter for these compounds. Then the LS-SVM method is utilized to construct the non-linear quantitative structure-activity relationship （QSAR） models. The results obtained using the LS-SVM method are then compared with those obtained for the MLR method; it was revealed that the LS-SVM model was much better than the MLR one. The root-mean-square errors of the training set and the test set for the LS-SVM model were 0.2912 and 0.2427, and the correlation coefficients were 0.9662 and 0.9518, respectively. This paper provides a new and effective method for predicting the Hildebrand solubility parameter for some polymers, and also reveals that the LS-SVM method can be used as a powerful chemometrics tool for the quantitative structure-property relationship （QSPR） studies.
