The reactivity parameters,Q and e,in the Q-e scheme reflect the reactivities of a monomer(or a radical)in free-radical copolymerizations.By applying multiple linear regression(MLR)analysis,the optimal quantitative str...The reactivity parameters,Q and e,in the Q-e scheme reflect the reactivities of a monomer(or a radical)in free-radical copolymerizations.By applying multiple linear regression(MLR)analysis,the optimal quantitative structure-activity relationship(QSAR)model for the reactivity parameter lnQ was developed based on five descriptors(NAF,NOF,EαLUMO,EβHOMO,and EβLUMO)and 69 monomers with the root mean square(rms)error of 0.61.The optimal MLR model of the parameter e obtained from five descriptors(TOcl,NpN,NSO,EαHOMO and DH)and 68 monomers produced rms error of 0.42.Compared with previous models,the two optimal MLR models in this paper show satisfactory statistical characteristics.The feasibility of combining 2D descriptors obtained from the monomers and 3D descriptors calculated from the radical structures(formed from monomers+H )to predict parameters Q and e has been demonstrated.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
A new molecular structural characterization（MSC）method called molecular vertexes correlative index（MVCI）was constructed in this paper.The index was used to describe the structures of 45 compounds and a quantitativ...A new molecular structural characterization（MSC）method called molecular vertexes correlative index（MVCI）was constructed in this paper.The index was used to describe the structures of 45 compounds and a quantitative structure-activity relationship（QSAR）model of toxicity（–lgEC50）was obtained through multiple linear regression（MLR）and stepwise multiple regression（SMR）.The correlation coefficient（R）of the model was 0.912,and the standard deviation（SD）of the model was 0.525.The estimation stability and prediction ability of the model were strictly analyzed by both internal and external validations.The Leave-One-Out（LOO）Cross-Validation（CV）correlation coefficient（RCV）was 0.816 and the standard deviation（SDCV）was 0.739,respectively.For the external validation,the correlation coefficient（Rtest）was 0.905 and the standard deviation（SDtest）was 0.520,respectively.The results showed that the index was superior in molecular structural representation.The stability and predictability of the model were good.展开更多
Quantitative structure-property relationship(QSPR)models were developed for prediction of photolysis half-life(t_(1/2))of polychlorinated biphenyls(PCBs)in water under ultraviolet(UV)radiation.Quantum chemical descrip...Quantitative structure-property relationship(QSPR)models were developed for prediction of photolysis half-life(t_(1/2))of polychlorinated biphenyls(PCBs)in water under ultraviolet(UV)radiation.Quantum chemical descriptors computed by the PM3 Hamiltonian software were used as independent variables.The cross-validated Q^(2)_(cum)value for the optimal QSPR model is 0.966,indicating good prediction capability for lg t_(1/2)values of PCBs in water.The QSPR results show that the largest negative atomic charge on a carbon atom(Q-C)and the standard heat of formation(ΔH_(f))have a dominant effect on t_(1/2)values of PCBs.Higher Q_(C)^(-)values or lowerΔHf values of the PCBs leads to higher lg t_(1/2)values.In addition,the lg t_(1/2)values of PCBs increase with the increase in the energy of the highest occupied molecular orbital values.Increasing the largest positive atomic charge on a chlorine atom and the most positive net atomic charge on a hydrogen atom in PCBs leads to the decrease of lg t_(1/2)values.展开更多
This work presents the development of molecular-based mathematical model for the prediction of CO_(2) solubility in deep eutectic solvents(DESs).First,a comprehensive database containing 1011 CO_(2) solubility data in...This work presents the development of molecular-based mathematical model for the prediction of CO_(2) solubility in deep eutectic solvents(DESs).First,a comprehensive database containing 1011 CO_(2) solubility data in various DESs at different temperatures and pressures is established,and the COSMO-RS-derived descriptors of involved hydrogen bond acceptors and hydrogen bond donors of DESs are calculated.Afterwards,the efficiency of the input variables,i.e.,temperature,pressure,COSMO-RS-derived descriptors of HBA and HBD as well as their molar ratio,is explored by a qualitative analysis of CO_(2) solubility in DESs using a simple multiple linear regression model.A machine learning method namely random forest is then employed to develop more accurate nonlinear quantitative structure-property relationship(QSPR)model.Combining the QSPR validation and comparisons with literature-reported models(i.e.,COSMO-RS model,traditional thermodynamic models and equations of state methods),the developed QSPR model with COSMO-RS-derived parameters as molecular descriptors is suggested to be able to give reliable predictions of CO_(2) solubility in DESs and could be used as a useful tool in selecting DESs for CO_(2) capture processes.展开更多
A new descriptor, called vector of topological and structural information for coded and noncoded amino acids (VTSA), was derived by principal component analysis (PCA) from a matrix of 66 topological and structural var...A new descriptor, called vector of topological and structural information for coded and noncoded amino acids (VTSA), was derived by principal component analysis (PCA) from a matrix of 66 topological and structural variables of 134 amino acids. The VTSA vector was then applied into two sets of peptide quantitative structure-activity relationships or quantitative sequence-activity modelings (QSARs/QSAMs). Molded by genetic partial least squares (GPLS), support vector machine (SVM), and immune neural network (INN), good results were obtained. For the datasets of 58 angiotensin converting enzyme inhibitors (ACEI) and 89 elastase substrate catalyzed kinetics (ESCK), the R 2, cross-validation R 2, and root mean square error of estimation (RMSEE) were as follows: ACEI, R cu 2 ?0.82, Q cu 2 ?0.77, E rmse?0.44 (GPLS+SVM); ESCK, R cu 2 ?0.84, Q cu 2 ?0.82, E rmse?0.20 (GPLS+INN), respectively.展开更多
基金supported by the National Natural Science Foundation of China(No.21472040)the Scientific Research Fund of Hunan Education Department(Nos.16A047 and 18A344)the Open Project Program of Hunan Provincial Key Laboratory of Environmental Catalysis & Waste Regeneration(Hunan Institute of Engineering)(2018KF11)
文摘The reactivity parameters,Q and e,in the Q-e scheme reflect the reactivities of a monomer(or a radical)in free-radical copolymerizations.By applying multiple linear regression(MLR)analysis,the optimal quantitative structure-activity relationship(QSAR)model for the reactivity parameter lnQ was developed based on five descriptors(NAF,NOF,EαLUMO,EβHOMO,and EβLUMO)and 69 monomers with the root mean square(rms)error of 0.61.The optimal MLR model of the parameter e obtained from five descriptors(TOcl,NpN,NSO,EαHOMO and DH)and 68 monomers produced rms error of 0.42.Compared with previous models,the two optimal MLR models in this paper show satisfactory statistical characteristics.The feasibility of combining 2D descriptors obtained from the monomers and 3D descriptors calculated from the radical structures(formed from monomers+H )to predict parameters Q and e has been demonstrated.
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 called molecular vertexes correlative index（MVCI）was constructed in this paper.The index was used to describe the structures of 45 compounds and a quantitative structure-activity relationship（QSAR）model of toxicity（–lgEC50）was obtained through multiple linear regression（MLR）and stepwise multiple regression（SMR）.The correlation coefficient（R）of the model was 0.912,and the standard deviation（SD）of the model was 0.525.The estimation stability and prediction ability of the model were strictly analyzed by both internal and external validations.The Leave-One-Out（LOO）Cross-Validation（CV）correlation coefficient（RCV）was 0.816 and the standard deviation（SDCV）was 0.739,respectively.For the external validation,the correlation coefficient（Rtest）was 0.905 and the standard deviation（SDtest）was 0.520,respectively.The results showed that the index was superior in molecular structural representation.The stability and predictability of the model were good.
基金The research was supported by the Open Foundation of the State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering(No.2009490511)the special fund of State Key Joint Laboratory of Environment Simulation and Pollution Control(No.10Y08ESPCN)the National High Technology Research and Development Program of China(No.2009AA05Z306).
文摘Quantitative structure-property relationship(QSPR)models were developed for prediction of photolysis half-life(t_(1/2))of polychlorinated biphenyls(PCBs)in water under ultraviolet(UV)radiation.Quantum chemical descriptors computed by the PM3 Hamiltonian software were used as independent variables.The cross-validated Q^(2)_(cum)value for the optimal QSPR model is 0.966,indicating good prediction capability for lg t_(1/2)values of PCBs in water.The QSPR results show that the largest negative atomic charge on a carbon atom(Q-C)and the standard heat of formation(ΔH_(f))have a dominant effect on t_(1/2)values of PCBs.Higher Q_(C)^(-)values or lowerΔHf values of the PCBs leads to higher lg t_(1/2)values.In addition,the lg t_(1/2)values of PCBs increase with the increase in the energy of the highest occupied molecular orbital values.Increasing the largest positive atomic charge on a chlorine atom and the most positive net atomic charge on a hydrogen atom in PCBs leads to the decrease of lg t_(1/2)values.
基金support from National Natural Science Foundation of China(21861132019,21776074)is greatly acknowledged.
文摘This work presents the development of molecular-based mathematical model for the prediction of CO_(2) solubility in deep eutectic solvents(DESs).First,a comprehensive database containing 1011 CO_(2) solubility data in various DESs at different temperatures and pressures is established,and the COSMO-RS-derived descriptors of involved hydrogen bond acceptors and hydrogen bond donors of DESs are calculated.Afterwards,the efficiency of the input variables,i.e.,temperature,pressure,COSMO-RS-derived descriptors of HBA and HBD as well as their molar ratio,is explored by a qualitative analysis of CO_(2) solubility in DESs using a simple multiple linear regression model.A machine learning method namely random forest is then employed to develop more accurate nonlinear quantitative structure-property relationship(QSPR)model.Combining the QSPR validation and comparisons with literature-reported models(i.e.,COSMO-RS model,traditional thermodynamic models and equations of state methods),the developed QSPR model with COSMO-RS-derived parameters as molecular descriptors is suggested to be able to give reliable predictions of CO_(2) solubility in DESs and could be used as a useful tool in selecting DESs for CO_(2) capture processes.
基金the Foundations of National High Technology (863) Programme (Grant No. 2006AA02Z312)State New Drug Project (Grant No. 1996ND1035A01)+4 种基金Fok- Yingtung Educational Foundation (Grant No. 980706)State Key Laboratory of Chemo/Biosensing and Chemometrics Foundation (Grant No. KLCB005-0012)Chongqing University Innovation Fund (Grant No. CUIF030506)Chongqing Mu-nicipality Applied Science Fund (Grant No. CASF01-3-6)Momentous Juche Innovation Fund for Tackle Key Problem Items (Grant No. MJIF 06-9-9)
文摘A new descriptor, called vector of topological and structural information for coded and noncoded amino acids (VTSA), was derived by principal component analysis (PCA) from a matrix of 66 topological and structural variables of 134 amino acids. The VTSA vector was then applied into two sets of peptide quantitative structure-activity relationships or quantitative sequence-activity modelings (QSARs/QSAMs). Molded by genetic partial least squares (GPLS), support vector machine (SVM), and immune neural network (INN), good results were obtained. For the datasets of 58 angiotensin converting enzyme inhibitors (ACEI) and 89 elastase substrate catalyzed kinetics (ESCK), the R 2, cross-validation R 2, and root mean square error of estimation (RMSEE) were as follows: ACEI, R cu 2 ?0.82, Q cu 2 ?0.77, E rmse?0.44 (GPLS+SVM); ESCK, R cu 2 ?0.84, Q cu 2 ?0.82, E rmse?0.20 (GPLS+INN), respectively.
