A novel quantitative structure-property relationship （QSPR） model for estimating the solution surface tension of 92 organic compounds at 20℃ was developed based on newly introduced atom-type topological indices. Th...A novel quantitative structure-property relationship （QSPR） model for estimating the solution surface tension of 92 organic compounds at 20℃ was developed based on newly introduced atom-type topological indices. The data set contained non-polar and polar liquids, and saturated and unsaturated compounds. The regression analysis shows that excellent result is obtained with multiple linear regression. The predictive power of the proposed model was discussed using the leave-one-out （LOO） cross-validated （CV） method. The correlation coefficient （R） and the leave-one-out cross-validation correlation coefficient （Rcv） of multiple linear regression model are 0.991 4 and 0.991 3, respectively. The new model gives the average absolute relative deviation of 1.81% for 92 substances. The result demonstrates that novel topological indices based on the equilibrium electro-negativity of atom and the relative bond length are useful model parameters for QSPR analysis of compounds.展开更多
A quantitative structure-spectrum relationship (QSSR) model was developed to simulate 13C nuclear magnetic resonance (NMR) spectra of carbinol carbon atoms for 55 alcohols. The proposed model,using multiple linear reg...A quantitative structure-spectrum relationship (QSSR) model was developed to simulate 13C nuclear magnetic resonance (NMR) spectra of carbinol carbon atoms for 55 alcohols. The proposed model,using multiple linear regression,contained four descriptors solely extracted from the molecular structure of compounds. The statistical results of the final model show that R2= 0.982 4 and S=0.869 8 (where R is the correlation coefficient and S is the standard deviation). To test its predictive ability,the model was further used to predict the 13C NMR spectra of the carbinol carbon atoms of other nine compounds which were not included in the developed model. The average relative errors are 0.94% and 1.70%,respectively,for the training set and the predictive set. The model is statistically significant and shows good stability for data variation as tested by the leave-one-out (LOO) cross-validation. The comparison with other approaches also reveals good performance of this method.展开更多
It is important to calibrate micro-parameters for applying partied flow code(PFC)to study mechanical characteristics and failure mechanism of rock materials.Uniform design method is firstly adopted to determine the mi...It is important to calibrate micro-parameters for applying partied flow code(PFC)to study mechanical characteristics and failure mechanism of rock materials.Uniform design method is firstly adopted to determine the microscopic parameters of parallel-bonded particle model for three-dimensional discrete element particle flow code(PFC3D).Variation ranges of microscopic of the microscopic parameters are created by analyzing the effects of microscopic parameters on macroscopic parameters(elastic modulus E,Poisson ratio v,uniaxial compressive strengthσc,and ratio of crack initial stress to uniaxial compressive strengthσci/σc)in order to obtain the actual uniform design talbe.The calculation equations of the microscopic and macroscopic parameters of rock materials can be established by the actual uniform design table and the regression analysis and thus the PFC3D microscopic parameters can be quantitatively determined.The PFC3D simulated results of the intact and pre-cracked rock specimens under uniaxial and triaxial compressions(including the macroscopic mechanical parameters,stress−strain curves and failure process)are in good agreement with experimental results,which can prove the validity of the calculation equations of microscopic and macroscopic parameters.展开更多
The quantitative structure-property relationship(QSPR) of anabolic androgenic steroids was studied on the half-wave reduction potential(E1/2) using quantum and physico-chemical molecular descriptors. The descriptors w...The quantitative structure-property relationship(QSPR) of anabolic androgenic steroids was studied on the half-wave reduction potential(E1/2) using quantum and physico-chemical molecular descriptors. The descriptors were calculated by semi-empirical calculations. Models were established using partial least square(PLS) regression and back-propagation artificial neural network(BP-ANN). The QSPR results indicate that the descriptors of these derivatives have significant relationship with half-wave reduction potential. The stability and prediction ability of these models were validated using leave-one-out cross-validation and external test set.展开更多
The thermal decomposition temperature is one of the most important parameters to evaluate fire hazard of organic peroxide. A quantitative structure-property relationship model was proposed for estimating the thermal d...The thermal decomposition temperature is one of the most important parameters to evaluate fire hazard of organic peroxide. A quantitative structure-property relationship model was proposed for estimating the thermal decomposition temperatures of organic peroxides. The entire set of 38 organic peroxides was at random divided into a training set for model development and a prediction set for external model validation. The novel local molecular descriptors of AT1, AT2, AT3, AT4, AT5, AT6 and global molecular descriptor of ATC have been proposed in order to character organic peroxides’ molecular structures. An accurate quantitative structure-property relationship （QSPR） equation is developed for the thermal decomposition temperatures of organic peroxides. The statistical results showed that the QSPR model was obtained using the multiple linear regression （MLR） method with correlation coefficient （R）, standard deviation （S）, leave-one-out validation correlation coefficient （RCV） values of 0.9795, 6.5676 ℃ and 0.9328, respectively. The average absolute relative deviation （AARD） is only 3.86% for the experimental values. Model test by internal leave-one-out cross validation and external validation and molecular descriptor interpretation were discussed. Comparison with literature results demonstrated that novel local and global descriptors were useful molecular descriptors for predicting the thermal decomposition temperatures of organic peroxides.展开更多
A quantitative structure–activity relationship(QSAR) was performed to analyze antimalarial activities against the D10 strains of Plasmodium falciparum of triazole-linked chalcone and dienone hybrid derivatives using ...A quantitative structure–activity relationship(QSAR) was performed to analyze antimalarial activities against the D10 strains of Plasmodium falciparum of triazole-linked chalcone and dienone hybrid derivatives using partial least squares regression coupled with stepwise forward–backward variable selection method. QSAR analyses were performed on the available IC50 D10 strains of Plasmodium falciparum data based on theoretical molecular descriptors. The QSAR model developed gave good predictive correlation coefficient(r2) of 0.8994, significant cross validated correlation coefficient(q2) of 0.7689, r2 for external test set)(2predr of 0.8256, coefficient of correlation of predicted data set)(2sepred,r of 0.3276. The model shows that antimalarial activity is greatly affected by donor and electron-withdrawing substituents. The study implicates that chalcone and dienone rings should have strong donor and electron-withdrawing substituents as they increase the activity of chalcone. Results show that the predictive ability of the model is satisfactory, and it can be used for designing similar group of antimalarial compounds. The findings derived from this analysis along with other molecular modeling studies will be helpful in designing of the new potent antimalarial activity of clinical utility.展开更多
Flash point is a primary property used to determine the fire and explosion hazards of a liquid. New group contribution-based models were presented for estimation of the flash point of alkanes by the use of multiple li...Flash point is a primary property used to determine the fire and explosion hazards of a liquid. New group contribution-based models were presented for estimation of the flash point of alkanes by the use of multiple linear regression(MLR)and artificial neural network(ANN). This simple linear model shows a low average relative deviation(AARD) of 2.8% for a data set including 50(40 for training set and 10 for validation set) flash points. Furthermore, the predictive ability of the model was evaluated using LOO cross validation. The results demonstrate ANN model is clearly superior both in fitness and in prediction performance.ANN model has only the average absolute deviation of 2.9 K and the average relative deviation of 0.72%.展开更多
基金Projects(20775010,21075011) supported by the National Natural Science Foundation of ChinaProject(2008AA05Z405) supported by the National High Technology Research and Development Program of China+2 种基金Project(09JJ3016) supported by Hunan Provincial Natural Science Foundation,ChinaProject(09C066) supported by Scientific Research Fund of Hunan Provincial Education Department,ChinaProject(2010CL01) supported by the Foundation of Hunan Provincial Key Laboratory of Materials Protection for Electric Power and Transportation,China
文摘A novel quantitative structure-property relationship （QSPR） model for estimating the solution surface tension of 92 organic compounds at 20℃ was developed based on newly introduced atom-type topological indices. The data set contained non-polar and polar liquids, and saturated and unsaturated compounds. The regression analysis shows that excellent result is obtained with multiple linear regression. The predictive power of the proposed model was discussed using the leave-one-out （LOO） cross-validated （CV） method. The correlation coefficient （R） and the leave-one-out cross-validation correlation coefficient （Rcv） of multiple linear regression model are 0.991 4 and 0.991 3, respectively. The new model gives the average absolute relative deviation of 1.81% for 92 substances. The result demonstrates that novel topological indices based on the equilibrium electro-negativity of atom and the relative bond length are useful model parameters for QSPR analysis of compounds.
基金Projects(20775010, 21075011) supported by the National Natural Science Foundation of ChinaProject(2008AA05Z405) supported by the National High-tech Research and Development Program of China+2 种基金Project(09JJ3016) supported by the Natural Science Foundation of Hunan Province, ChinaProject(09C066) supported by the Scientific Research Fund of Hunan Provincial Education Department, ChinaProject(2010CL01) supported by the Foundation of Hunan Provincial Key Laboratory of Materials Protection for Electric Power and Transportation, China
文摘A quantitative structure-spectrum relationship (QSSR) model was developed to simulate 13C nuclear magnetic resonance (NMR) spectra of carbinol carbon atoms for 55 alcohols. The proposed model,using multiple linear regression,contained four descriptors solely extracted from the molecular structure of compounds. The statistical results of the final model show that R2= 0.982 4 and S=0.869 8 (where R is the correlation coefficient and S is the standard deviation). To test its predictive ability,the model was further used to predict the 13C NMR spectra of the carbinol carbon atoms of other nine compounds which were not included in the developed model. The average relative errors are 0.94% and 1.70%,respectively,for the training set and the predictive set. The model is statistically significant and shows good stability for data variation as tested by the leave-one-out (LOO) cross-validation. The comparison with other approaches also reveals good performance of this method.
基金Projects(51474251,51874351)supported by the National Natural Science Foundation,China。
文摘It is important to calibrate micro-parameters for applying partied flow code(PFC)to study mechanical characteristics and failure mechanism of rock materials.Uniform design method is firstly adopted to determine the microscopic parameters of parallel-bonded particle model for three-dimensional discrete element particle flow code(PFC3D).Variation ranges of microscopic of the microscopic parameters are created by analyzing the effects of microscopic parameters on macroscopic parameters(elastic modulus E,Poisson ratio v,uniaxial compressive strengthσc,and ratio of crack initial stress to uniaxial compressive strengthσci/σc)in order to obtain the actual uniform design talbe.The calculation equations of the microscopic and macroscopic parameters of rock materials can be established by the actual uniform design table and the regression analysis and thus the PFC3D microscopic parameters can be quantitatively determined.The PFC3D simulated results of the intact and pre-cracked rock specimens under uniaxial and triaxial compressions(including the macroscopic mechanical parameters,stress−strain curves and failure process)are in good agreement with experimental results,which can prove the validity of the calculation equations of microscopic and macroscopic parameters.
基金Project supported by the Postdoctoral Science Foundation of Central South University,ChinaProject(2015SK20823)supported by Science and Technology Project of Hunan Province,China+2 种基金Project(15A001)supported by Scientific Research Fund of Hunan Provincial Education Department,ChinaProject(CX2015B372)supported by Hunan Provincial Innovation Foundation for Postgraduate,ChinaProject supported by Innovation Experiment Program for University Students of Changsha University of Science and Technology,China
文摘The quantitative structure-property relationship(QSPR) of anabolic androgenic steroids was studied on the half-wave reduction potential(E1/2) using quantum and physico-chemical molecular descriptors. The descriptors were calculated by semi-empirical calculations. Models were established using partial least square(PLS) regression and back-propagation artificial neural network(BP-ANN). The QSPR results indicate that the descriptors of these derivatives have significant relationship with half-wave reduction potential. The stability and prediction ability of these models were validated using leave-one-out cross-validation and external test set.
基金Project(2015SK20823) supported by Science and Technology Project of Hunan Province,ChinaProject(15A001) supported by Scientific Research Fund of Hunan Provincial Education Department,China+2 种基金Project(2017CL06) supported by Hunan Provincial Key Laboratory of Materials Protection for Electric Power and Transportation,ChinaProject(k1403029-11) supported by Science and Technology Project of Changsha City,ChinaProject(CX2015B372) supported by the Hunan Provincial Innovation Foundation for Postgraduate,China
文摘The thermal decomposition temperature is one of the most important parameters to evaluate fire hazard of organic peroxide. A quantitative structure-property relationship model was proposed for estimating the thermal decomposition temperatures of organic peroxides. The entire set of 38 organic peroxides was at random divided into a training set for model development and a prediction set for external model validation. The novel local molecular descriptors of AT1, AT2, AT3, AT4, AT5, AT6 and global molecular descriptor of ATC have been proposed in order to character organic peroxides’ molecular structures. An accurate quantitative structure-property relationship （QSPR） equation is developed for the thermal decomposition temperatures of organic peroxides. The statistical results showed that the QSPR model was obtained using the multiple linear regression （MLR） method with correlation coefficient （R）, standard deviation （S）, leave-one-out validation correlation coefficient （RCV） values of 0.9795, 6.5676 ℃ and 0.9328, respectively. The average absolute relative deviation （AARD） is only 3.86% for the experimental values. Model test by internal leave-one-out cross validation and external validation and molecular descriptor interpretation were discussed. Comparison with literature results demonstrated that novel local and global descriptors were useful molecular descriptors for predicting the thermal decomposition temperatures of organic peroxides.
文摘A quantitative structure–activity relationship(QSAR) was performed to analyze antimalarial activities against the D10 strains of Plasmodium falciparum of triazole-linked chalcone and dienone hybrid derivatives using partial least squares regression coupled with stepwise forward–backward variable selection method. QSAR analyses were performed on the available IC50 D10 strains of Plasmodium falciparum data based on theoretical molecular descriptors. The QSAR model developed gave good predictive correlation coefficient(r2) of 0.8994, significant cross validated correlation coefficient(q2) of 0.7689, r2 for external test set)(2predr of 0.8256, coefficient of correlation of predicted data set)(2sepred,r of 0.3276. The model shows that antimalarial activity is greatly affected by donor and electron-withdrawing substituents. The study implicates that chalcone and dienone rings should have strong donor and electron-withdrawing substituents as they increase the activity of chalcone. Results show that the predictive ability of the model is satisfactory, and it can be used for designing similar group of antimalarial compounds. The findings derived from this analysis along with other molecular modeling studies will be helpful in designing of the new potent antimalarial activity of clinical utility.
基金Projects(21376031,21075011)supported by the National Natural Science Foundation of ChinaProject(2012GK3058)supported by the Foundation of Hunan Provincial Science and Technology Department,China+2 种基金Project supported by the Postdoctoral Science Foundation of Central South University,ChinaProject(2014CL01)supported by the Foundation of Hunan Provincial Key Laboratory of Materials Protection for Electric Power and Transportation,ChinaProject supported by the Innovation Experiment Program for University Students of Changsha University of Science and Technology,China
文摘Flash point is a primary property used to determine the fire and explosion hazards of a liquid. New group contribution-based models were presented for estimation of the flash point of alkanes by the use of multiple linear regression(MLR)and artificial neural network(ANN). This simple linear model shows a low average relative deviation(AARD) of 2.8% for a data set including 50(40 for training set and 10 for validation set) flash points. Furthermore, the predictive ability of the model was evaluated using LOO cross validation. The results demonstrate ANN model is clearly superior both in fitness and in prediction performance.ANN model has only the average absolute deviation of 2.9 K and the average relative deviation of 0.72%.
