In the present study,（QSRR） study had been carried out for volatile components from Rosa banksiae Ait.based on various quantum-chemical and physicochemical descriptors derived by B3LYP method.To build QSRR models,a ...In the present study,（QSRR） study had been carried out for volatile components from Rosa banksiae Ait.based on various quantum-chemical and physicochemical descriptors derived by B3LYP method.To build QSRR models,a multiple linear regression （MLR） stepwise method was used.The generated models have good predictive ability and are of high statistical significance with good correlation coefficients （R2≥0.734） and p values far less than 0.05.Preliminary results indicated that the application of the models,especially the prediction of GC retention time and linear retention index of volatile components from Rosa banksiae Ait.,will be helpful.The models contribute also to the identification of important quantum-chemical and physicochemical descriptors responsible for the retention time and linear retention index.It was found that the shape attribute （ShpA） and logP value play a vital role in determining component’s GC retention time and linear retention index which increase with the lipophilicity of volatile components.The larger the shape attribute of analyte is,the larger the deformability is,the stronger the interaction between analyte and stationary phase is,and the longer the GC retention time is,the larger the linear retention index is.The importance of E HOMO,q＋,and SEV is also embodied in models,but they are not dominant.展开更多
Polychlorinated dibenzothiophenes（PCDTs） are classified as persistent organic pollutants in the environment,so the analysis of PCDTs by their gas chromatographic behaviors is of great significance.Quantitative struc...Polychlorinated dibenzothiophenes（PCDTs） are classified as persistent organic pollutants in the environment,so the analysis of PCDTs by their gas chromatographic behaviors is of great significance.Quantitative structure-retention relationship（QSRR） analysis is a useful technique capable of relating chromatographic retention time to the molecular structure.In this paper,a QSRR study of 37 PCDTs was carried out by using molecular electronegativity distance vector（MEDV） descriptors and multiple linear regression（MLR） and partial least-squares regression（PLS） methods.The correlation coefficient R of established MLR,PLS models,leave-one-out（LOO） cross-validation（CV）,Q2ext were 0.9951,0.9942,0.9839（MLR） and 0.9925,0.9915,0.9833（PLS）,respectively.Results showed that the model exhibited excellent estimate capability for internal sample set and good predictive capability for external sample set.By using MEDV descriptors,the QSRR model can provide a simple and rapid way to predict the gas-chromatographic retention indices of polychlorinated dibenzothiophenes in conditions of lacking standard samples or poor experimental conditions.展开更多
Polychlorinated dibenzothiophenes（PCDTs）and their corresponding sulfone（PCDTO2）compounds are a group of important persistent organic pollutants.In the present study,geometrical optimization and subsequent calculat...Polychlorinated dibenzothiophenes（PCDTs）and their corresponding sulfone（PCDTO2）compounds are a group of important persistent organic pollutants.In the present study,geometrical optimization and subsequent calculations of electrostatic potentials（ESPs）on molecular surface have been performed for all 135 PCDTs and 135 PCDTO2 congeners at the HF/6-31G*level of theory.A number of statistically-based parameters have been extracted.Linear relationship between gas-chromatographic retention index（RI）and the structural descriptors have been established by multiple linear regression.The result shows that two descriptors derived from positive electrostatic potential on molecular surface, ■ and π,together with the molecular volume(Vmc)and the energy of the lowest unoccupied molecular orbital(ELUMO)can be well used to express the quantitative structure-retention relationship（QSRR）of PCDTs and PCDTO2.Predictive capability of the two models has been demonstrated by leave-one-out cross-validation with the cross-validated correlation coefficient(RCV)of 0.996 and 0.997,respectively.Furthermore,the predictive power of the models is further examined for the external test set.Correlation coefficients（R）between the observed and predicted RI values for the external test set are 0.997 and0.998,respectively,validating the robustness and good prediction of our model.The QSRR model established may provide again a powerful method for predicting chromatographic properties of aromatic organosulfur compounds.展开更多
The molecular electronegativity-distance vector （MEDV） was used to describe the molecular structure of volatile components of Rosa banksiae Ait, and QSRR model was built up by use of multiple linear regression （MLR...The molecular electronegativity-distance vector （MEDV） was used to describe the molecular structure of volatile components of Rosa banksiae Ait, and QSRR model was built up by use of multiple linear regression （MLR）. Furthermore, in virtue of variable screening by the stepwise multiple regression technique, the QSRR models of 10 and 6 variables and linear retention index （LRI） 10, 7 and 6 varieables were built up by combinating MEDV with the Ultra2 column GC retention time （tR） of 53 volatile components of Rosa Banksiae Air. The multiple correlation coefficients （R） of modeling calculation values of QSRR model were 0.906, 0.906, 0.949, 0.943 and 0.949, respectively. The cross-verification multiple correlation coefficients （RCV） were 0.903, 0.904, 0.867, 0.901 and 0.904, respectively. The results show that the models constructed could provide estimation stability and favorable predictive ability.展开更多
A newly developed descriptor, three- dimensional holographic vector of atomic interaction field (3D-HoVAIF), was used to describe the chemical structures of purine bases. After variable screening by stepwise multiple ...A newly developed descriptor, three- dimensional holographic vector of atomic interaction field (3D-HoVAIF), was used to describe the chemical structures of purine bases. After variable screening by stepwise multiple regression (SMR) technique, a partial least square (PLS) regression model was built with 3D-HoVAIF. The model was satisfactory com- paring to reference since correlation coefficients of molecular modeling ( Rc 2um), cross- validation ( Qc 2um) and standard deviation of estimation (SD) were 0.966, 0.860 and 0.112, respectively, showing that the model had favorable estimation and prediction capa- bilities. It was illustrated that information related to retention data of purine bases could preferably be expressed by 3D-HoVAIF with definite physico- chemical meanings and easy structural interpretation for purine bases. It was illustrated that 3D-HoVAIF was to preferably express retention data of purine bases and had definite physicochemical significance. So 3D-HoVAIF was a useful structural expression technique for quantitative structure activity (or prop- erty or retention) relationships (QSAR/QSPR/QSRR) study, such as structural characterization and chro- matographic retention prediction.展开更多
Based on the identical group as a pseudo atom instead of a typical atom, a novel modified molecular dis-tance-edge (MDE) vector μ was developed in our laboratory to characterize chemical structure of polychlorinated ...Based on the identical group as a pseudo atom instead of a typical atom, a novel modified molecular dis-tance-edge (MDE) vector μ was developed in our laboratory to characterize chemical structure of polychlorinated diben-zofurans (PCDFs) congeners and/or isomers. Quantitative structure-retention relationships (QSRRs) between the new VMDE parameters and gas chromatographic (GC) retention behavior of PCDFs were then generated by multiple linear regression (MLR) method for non-polar, moderately polar, and polar stationary phases. Four excellent models with high correlation coefficients, R=0.984-0.995, were proposed for non-polar columns (DB-5, SE-54, OV-101). For the moder-ately polar columns (OV-1701), the correlation coefficient of the developed good model is only 0.958. For the polar col-umns (SP-2300), the QSRR model is poor with R=0.884. Then cross validation with leave-one out of procedure (CV) is performed in high correlation with the non-polar (Rcv=992-0.974) and weakly polar (Rcv=921) columns and in little cor-relation (Rcv=0.834) with the polar columns. These results show that the new μ vector is suitable for describing the re-tention behaviors of PCDFs on non-polar and moderately polar stationary phases and not for the various gas chroma-tographic retention behaviors of PCDFs on the different po-larity-varying stationary phases.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
The logarithms of retention factors normalized to a hypothetical pure water eluent(log k w) were determined on a reversed phase high performance liquid chromatography(RP HPLC) column (Li Chrosorb RP 18 column...The logarithms of retention factors normalized to a hypothetical pure water eluent(log k w) were determined on a reversed phase high performance liquid chromatography(RP HPLC) column (Li Chrosorb RP 18 column) for 20 new α\|branched phenylsulfonyl acetates. The atomic charge method was applied to develop quantitative structure retention relationships(QSRRs). Among the available geometric and electronic descriptors, surface area (S), ovality (O), and the charge of carboxyl group(Q OC ) are significant. In the model, the contribution of surface area (S) is the greatest. The molecular mechanism of retention was demonstrated through the model. With the correlation coefficient ( r 2 adj , adjusted for degrees of freedom) of 0.964, the standard error of 0.164 and the F value of 170.39, the model has good predictive capacity.展开更多
Polychlorinated dibenzothiophenes（PCDTs） are a group of important persistent organic pollutants.In the present study,geometrical optimization and electrostatic potential calculations have been performed for all 135 ...Polychlorinated dibenzothiophenes（PCDTs） are a group of important persistent organic pollutants.In the present study,geometrical optimization and electrostatic potential calculations have been performed for all 135 PCDTs congeners at the B3LYP/6-31G＊ level of theory.By means of the VSMP（variable selection and modeling based on prediction） program,one optimal descriptor（molecular polarizability,α） was selected to develop a QSRR model for the prediction of gas chromatographic retention indices（GC-RI） of PCDTs.The estimated correlation coefficients（r2） and LOO-validated correlation coefficients（q2）,all more than 0.99,were built by multiple linear regression,which shows a good estimation ability and stability of the models.A prediction power for the external samples was validated by the model built from the training set with 17 polychlorinated dibenzothiophenes.展开更多
将食用植物油中的脂肪酸转化为相应的脂肪酸甲酯,并采用立体结构参数Steric and Electronic Descriptors(SEDs)表征其分子结构,然后运用多元线性回归(MLR)方法,建立了预测食用植物油中脂肪酸(甲酯)的定量结构-色谱保留相关(QSRR)模型,...将食用植物油中的脂肪酸转化为相应的脂肪酸甲酯,并采用立体结构参数Steric and Electronic Descriptors(SEDs)表征其分子结构,然后运用多元线性回归(MLR)方法,建立了预测食用植物油中脂肪酸(甲酯)的定量结构-色谱保留相关(QSRR)模型,同时采用内部及外部双重验证的方法对所建模型的稳定性能和预测能力进行了分析和验证。建模计算值、留一法(LOO)交互检验(CV)预测值和外部样本预测值的相关系数R、R LOO、Q2ext分别为0.9990、0.9970、0.9860。结果表明,SEDs参数能较好地表征食用植物油中的脂肪酸甲酯分子的结构信息,所建立的QSRR模型具有良好的稳定性和预测能力,为间接分析鉴定食用植物油中脂肪酸提供了一种方便有效的新途径。展开更多
基金Supported by Shanghai Education Committee Project (No. 11YZ224)Shanghai Leading Academic Discipline Project (No. J51503)
文摘In the present study,（QSRR） study had been carried out for volatile components from Rosa banksiae Ait.based on various quantum-chemical and physicochemical descriptors derived by B3LYP method.To build QSRR models,a multiple linear regression （MLR） stepwise method was used.The generated models have good predictive ability and are of high statistical significance with good correlation coefficients （R2≥0.734） and p values far less than 0.05.Preliminary results indicated that the application of the models,especially the prediction of GC retention time and linear retention index of volatile components from Rosa banksiae Ait.,will be helpful.The models contribute also to the identification of important quantum-chemical and physicochemical descriptors responsible for the retention time and linear retention index.It was found that the shape attribute （ShpA） and logP value play a vital role in determining component’s GC retention time and linear retention index which increase with the lipophilicity of volatile components.The larger the shape attribute of analyte is,the larger the deformability is,the stronger the interaction between analyte and stationary phase is,and the longer the GC retention time is,the larger the linear retention index is.The importance of E HOMO,q＋,and SEV is also embodied in models,but they are not dominant.
基金supported by the Foundation of Returned Scholars (Main Program) of Shanxi Province (200902)
文摘Polychlorinated dibenzothiophenes（PCDTs） are classified as persistent organic pollutants in the environment,so the analysis of PCDTs by their gas chromatographic behaviors is of great significance.Quantitative structure-retention relationship（QSRR） analysis is a useful technique capable of relating chromatographic retention time to the molecular structure.In this paper,a QSRR study of 37 PCDTs was carried out by using molecular electronegativity distance vector（MEDV） descriptors and multiple linear regression（MLR） and partial least-squares regression（PLS） methods.The correlation coefficient R of established MLR,PLS models,leave-one-out（LOO） cross-validation（CV）,Q2ext were 0.9951,0.9942,0.9839（MLR） and 0.9925,0.9915,0.9833（PLS）,respectively.Results showed that the model exhibited excellent estimate capability for internal sample set and good predictive capability for external sample set.By using MEDV descriptors,the QSRR model can provide a simple and rapid way to predict the gas-chromatographic retention indices of polychlorinated dibenzothiophenes in conditions of lacking standard samples or poor experimental conditions.
基金supported by the Science and Technology Project of Zhejiang Province(2016C33039)the Public Technology Research Project(Analysis and Measurement)of Zhejiang Province(LGC19B070004)+1 种基金State Key Laboratory of Environmental Chemistry and Ecotoxicology,Research Center for Eco-Environmental Sciences,Chinese Academy of Sciences(KF2018-15)Natural Science Foundation of Zhejiang Province(LY18C030003)
文摘Polychlorinated dibenzothiophenes（PCDTs）and their corresponding sulfone（PCDTO2）compounds are a group of important persistent organic pollutants.In the present study,geometrical optimization and subsequent calculations of electrostatic potentials（ESPs）on molecular surface have been performed for all 135 PCDTs and 135 PCDTO2 congeners at the HF/6-31G*level of theory.A number of statistically-based parameters have been extracted.Linear relationship between gas-chromatographic retention index（RI）and the structural descriptors have been established by multiple linear regression.The result shows that two descriptors derived from positive electrostatic potential on molecular surface, ■ and π,together with the molecular volume(Vmc)and the energy of the lowest unoccupied molecular orbital(ELUMO)can be well used to express the quantitative structure-retention relationship（QSRR）of PCDTs and PCDTO2.Predictive capability of the two models has been demonstrated by leave-one-out cross-validation with the cross-validated correlation coefficient(RCV)of 0.996 and 0.997,respectively.Furthermore,the predictive power of the models is further examined for the external test set.Correlation coefficients（R）between the observed and predicted RI values for the external test set are 0.997 and0.998,respectively,validating the robustness and good prediction of our model.The QSRR model established may provide again a powerful method for predicting chromatographic properties of aromatic organosulfur compounds.
文摘The molecular electronegativity-distance vector （MEDV） was used to describe the molecular structure of volatile components of Rosa banksiae Ait, and QSRR model was built up by use of multiple linear regression （MLR）. Furthermore, in virtue of variable screening by the stepwise multiple regression technique, the QSRR models of 10 and 6 variables and linear retention index （LRI） 10, 7 and 6 varieables were built up by combinating MEDV with the Ultra2 column GC retention time （tR） of 53 volatile components of Rosa Banksiae Air. The multiple correlation coefficients （R） of modeling calculation values of QSRR model were 0.906, 0.906, 0.949, 0.943 and 0.949, respectively. The cross-verification multiple correlation coefficients （RCV） were 0.903, 0.904, 0.867, 0.901 and 0.904, respectively. The results show that the models constructed could provide estimation stability and favorable predictive ability.
基金supported by the Industry Innovation Foundation of Shanxi Province(Grant No.2006031204)the Chongqing Applied Fundamental Science Foundation(Grant No.01-3-6).
文摘A newly developed descriptor, three- dimensional holographic vector of atomic interaction field (3D-HoVAIF), was used to describe the chemical structures of purine bases. After variable screening by stepwise multiple regression (SMR) technique, a partial least square (PLS) regression model was built with 3D-HoVAIF. The model was satisfactory com- paring to reference since correlation coefficients of molecular modeling ( Rc 2um), cross- validation ( Qc 2um) and standard deviation of estimation (SD) were 0.966, 0.860 and 0.112, respectively, showing that the model had favorable estimation and prediction capa- bilities. It was illustrated that information related to retention data of purine bases could preferably be expressed by 3D-HoVAIF with definite physico- chemical meanings and easy structural interpretation for purine bases. It was illustrated that 3D-HoVAIF was to preferably express retention data of purine bases and had definite physicochemical significance. So 3D-HoVAIF was a useful structural expression technique for quantitative structure activity (or prop- erty or retention) relationships (QSAR/QSPR/QSRR) study, such as structural characterization and chro- matographic retention prediction.
基金This work was supported by the Chunhui Project Fund of the Ministry of Education(Grant No.SCPF99-4-4+37)Fok Ying-Tung Educational Foundation(Grant No.FYTF98-7-6)+1 种基金Chongqing Applied Science Fund(Grant No,CASF01-3-6)Chongqing University ZYXT Innovation Fund(Grant No.CUIF03-5-6+04-10-10).
文摘Based on the identical group as a pseudo atom instead of a typical atom, a novel modified molecular dis-tance-edge (MDE) vector μ was developed in our laboratory to characterize chemical structure of polychlorinated diben-zofurans (PCDFs) congeners and/or isomers. Quantitative structure-retention relationships (QSRRs) between the new VMDE parameters and gas chromatographic (GC) retention behavior of PCDFs were then generated by multiple linear regression (MLR) method for non-polar, moderately polar, and polar stationary phases. Four excellent models with high correlation coefficients, R=0.984-0.995, were proposed for non-polar columns (DB-5, SE-54, OV-101). For the moder-ately polar columns (OV-1701), the correlation coefficient of the developed good model is only 0.958. For the polar col-umns (SP-2300), the QSRR model is poor with R=0.884. Then cross validation with leave-one out of procedure (CV) is performed in high correlation with the non-polar (Rcv=992-0.974) and weakly polar (Rcv=921) columns and in little cor-relation (Rcv=0.834) with the polar columns. These results show that the new μ vector is suitable for describing the re-tention behaviors of PCDFs on non-polar and moderately polar stationary phases and not for the various gas chroma-tographic retention behaviors of PCDFs on the different po-larity-varying stationary phases.
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TheNationalNaturalScienceFoundationofChina (No .2 9837180 )
文摘The logarithms of retention factors normalized to a hypothetical pure water eluent(log k w) were determined on a reversed phase high performance liquid chromatography(RP HPLC) column (Li Chrosorb RP 18 column) for 20 new α\|branched phenylsulfonyl acetates. The atomic charge method was applied to develop quantitative structure retention relationships(QSRRs). Among the available geometric and electronic descriptors, surface area (S), ovality (O), and the charge of carboxyl group(Q OC ) are significant. In the model, the contribution of surface area (S) is the greatest. The molecular mechanism of retention was demonstrated through the model. With the correlation coefficient ( r 2 adj , adjusted for degrees of freedom) of 0.964, the standard error of 0.164 and the F value of 170.39, the model has good predictive capacity.
基金Sponsored by the NSF of Guangxi Province (No. 2011XNSFA018059)Guangxi Key Laboratory Research Fund of Environmental Engineering and Protection Assessment (No. 0801Z026)+1 种基金Major Science of Water Pollution Control and Management (No. 2008ZX07317-02)the Guangxi Zhuang Autonomous Region Department of Education Research (No. 201010LX174) Funding
文摘Polychlorinated dibenzothiophenes（PCDTs） are a group of important persistent organic pollutants.In the present study,geometrical optimization and electrostatic potential calculations have been performed for all 135 PCDTs congeners at the B3LYP/6-31G＊ level of theory.By means of the VSMP（variable selection and modeling based on prediction） program,one optimal descriptor（molecular polarizability,α） was selected to develop a QSRR model for the prediction of gas chromatographic retention indices（GC-RI） of PCDTs.The estimated correlation coefficients（r2） and LOO-validated correlation coefficients（q2）,all more than 0.99,were built by multiple linear regression,which shows a good estimation ability and stability of the models.A prediction power for the external samples was validated by the model built from the training set with 17 polychlorinated dibenzothiophenes.
文摘将食用植物油中的脂肪酸转化为相应的脂肪酸甲酯,并采用立体结构参数Steric and Electronic Descriptors(SEDs)表征其分子结构,然后运用多元线性回归(MLR)方法,建立了预测食用植物油中脂肪酸(甲酯)的定量结构-色谱保留相关(QSRR)模型,同时采用内部及外部双重验证的方法对所建模型的稳定性能和预测能力进行了分析和验证。建模计算值、留一法(LOO)交互检验(CV)预测值和外部样本预测值的相关系数R、R LOO、Q2ext分别为0.9990、0.9970、0.9860。结果表明,SEDs参数能较好地表征食用植物油中的脂肪酸甲酯分子的结构信息,所建立的QSRR模型具有良好的稳定性和预测能力,为间接分析鉴定食用植物油中脂肪酸提供了一种方便有效的新途径。
