The theoretical linear solvation energy relationship(TLSER) approach was adopted to predict the aqueous solubility and n -octanol/water partition coefficient of three groups of environmentally important chemicals-poly...The theoretical linear solvation energy relationship(TLSER) approach was adopted to predict the aqueous solubility and n -octanol/water partition coefficient of three groups of environmentally important chemicals-polychlorinated biphenyls(PCBs), polychlorinated dibenzodioxins and dibenzofurans(PCDDs and PCDFs). For each compound, five quantum parameters were calculated using AM1 semiempirical molecular orbital methods and used as structure descriptors: average molecular polarizability(α), energy of the lowest unoccupied molecular orbit( E _ LUMO ), energy of the highest occupied molecular orbit( E _ HOMO ), the most positive charge on a hydrogen atom( q _+), and the most negative atomic partial charge( q _-) in the solute molecule. Then standard independent variables in TLSER equation was extracted and two series of quantitative equations between these quantum parameters and aqueous solubility and n -octanol/water partition coefficient were obtained by stepwise multiple linear regression(MLR) method. The developed equations have both quite high accuracy and explicit meanings. And the cross-validation test illustrated the good predictive power and stability of the established models. The results showed that TLSER could be used as a promising approach in the estimation of partition and solubility properties of macromolecular chemicals, such as persistent organic pollutants.展开更多
Development of energy-efficient lubricants is a way to reduce energy consumption for transportation,with the tendency to design molecules that are beneficial in reducing the viscosity of synthetic oils.Oligoether este...Development of energy-efficient lubricants is a way to reduce energy consumption for transportation,with the tendency to design molecules that are beneficial in reducing the viscosity of synthetic oils.Oligoether esters(OEEs),as a low-viscosity ester base oil,have characteristics such as simple synthesis and excellent lubrication effect;however,the application of OEEs in tribology field has rarely been investigated.The objective of the present study is to investigate the effect of structure on the lubricating performance of OEEs and to develop a predictive model for OEEs based on quantitative structure‒property relationship(QSPR)through a combination of experiment and statistical modeling.Results showed that glycol chains contribute positively to lubrication with the ether functional groups increasing the sites of adsorption.Compared to branched-chain OEEs,straight-chain OEEs exhibited reduced wear,which was mainly due to the thicker adsorption film formed by the straight-chain structure.Furthermore,carbon films were detected on lightly worn surfaces,indicating that OEEs underwent oxidation during the friction process.Based on the results of principal component analysis(PCA)and partial least squares(PLS),it could be found that the predictive models of viscosity‒temperature performance,thermal stability performance,coefficient of friction(COF),and wear volume(WV)performed well and robustly.Among them,COF and WV can be best predicted with an R^(2) of about 0.90.展开更多
Based on the quantum chemical descriptors,quantitative structure-property relationship（QSPR） models have been developed to estimate and predict the photodegradation rate constant（logK） of polycyclic aromatic hydro...Based on the quantum chemical descriptors,quantitative structure-property relationship（QSPR） models have been developed to estimate and predict the photodegradation rate constant（logK） of polycyclic aromatic hydrocarbons（PAHs） by use of linear method（multiple linear regression,MLR） and non-linear method（back propagation artificial neural network,BP-ANN）.A BP-ANN with 3-3-1 architecture was generated by using three quantum chemical descriptors appearing in the MLR model.The standard heat of formation（HOF）,the gap of frontier molecular orbital energies（ΔELH） and total energy（TE） were inputs and its output was logK.Leave-One-Out（LOO） Cross-Validated correlation coefficient（R^2CV） of the established MLR and BP-ANN models were 0.6383 and 0.7843,respectively.The nonlinear BP-ANN model has better predictive ability compared to the linear MLR model with the root mean square error（RMSE） for training and validation sets to be 0.1071,0.1514 and the squared correlation coefficient（R^2） of 0.9791,0.9897,respectively.In addition,some insights into the molecular structural features affecting the photodegradation of PAHs were also discussed.展开更多
The interaction of nanoparticles with proteins is extremely complex, important for understanding the biological properties of nanomaterials, but is very poorly understood. We have employed a combinatorial library of s...The interaction of nanoparticles with proteins is extremely complex, important for understanding the biological properties of nanomaterials, but is very poorly understood. We have employed a combinatorial library of surface modified gold nanoparticles to interrogate the relationships between the nanoparticle surface chemistry and the specific and nonspecific binding to a common, important, and representative enzyme, acetylcholinesterase （ACHE）. We also used Bayesian neural networks to generate robust quantitative structure-property relationship （QSPR） models relating the nanoparticle surface to the AChE binding that also provided significant understanding into the molecular basis for these interactions. The results illustrate the insights that result from a synergistic blending of experimental combinatorial synthesis and biological testing of nanoparticles with quantitative computational methods and molecular modeling.展开更多
Quantitative structure-property relationships（QSPRs） have been developed to predict the thermal stability for a set of 22 nitroaromatic compounds by means of the theoretical descriptors derived from electrostatic po...Quantitative structure-property relationships（QSPRs） have been developed to predict the thermal stability for a set of 22 nitroaromatic compounds by means of the theoretical descriptors derived from electrostatic potentials on molecular surface. Several techniques, including partial least squares regression（PLS）, least-squares support vector machine（LSSVM） and Gaussian process（GP） have been utilized to establish the relationships between the structural descriptor and the decomposition enthalpy. The nonlinear LSSVM and GP models have proven to own a better predictive ability than the linear PLS method. Moreover, owing to its ability to handle both linear- and nonlinear-hybrid relationship, GP gives a stronger fitting ability and a better predictive power than LSSVM, and therefore could be well applied to developing QSPR models for the thermal stability of nitroaromatic explosives.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
In this paper, according to the peak numbers of the nuclear magnetic resonance and the Randic embranchment degree (δ_i) of carbon atom i, the carbon atom’s environment valence g_i is defined as: g_i=(t_i+δ_i)/2.The...In this paper, according to the peak numbers of the nuclear magnetic resonance and the Randic embranchment degree (δ_i) of carbon atom i, the carbon atom’s environment valence g_i is defined as: g_i=(t_i+δ_i)/2.The g_i reflect the characteristic of each carbon atom, and as well as the conjunction detail of the carbon atom with other carbon atoms.So, the g_ i could distinguish better the chemical environment of each carbon atom in the molecule than δ_i.A connectivity index of environment valence ( mS) and its athwart index ( mS′) are proposed based on the adjacency matrix and the carbon atom’s environment valence g_i.Among them, the 0S and 0S′ include the characteristic and the connectivity of each carbon atom, the 1S and 1S′ reflect the second conjunction between carbon atoms.Based on 0S′ and N(the number of carbon atom), a new structural parameter——symmetry degree (N_ ec), is defined as: N_ ec=[(0S′_S/0S′_C)N] 2/3,and the N_ ec reflect the size of the molecule as well as the symmetry of the molecule.The N_ ec, 0S and R_n(the biggest ring’s edge numbers of cycloalkanes) of 474 saturated hydrocarbons (216 paraffins and 258 cycloalkanes) were calculated and correlated with their boiling points.The best regression equation was obtained as follow: ln(1056-T_b)=6.9480-0.1040N_ ec -0.0086890S-0.009614R_ n+0.01998R 0.5_n,n=474,R=0.9989,F=52627,S=5.63K.The model was checked up by the Jackknife’s method.It should have overall steadiness and could be used for predicting the boiling point of saturated hydrocarbons.展开更多
用量子化学MOPAC-AM1法计算21种多环芳烃(PAHs)的SEDs(steric and electronic descriptors)参数,然后用多元线性回归法建立预测多环芳烃的沸点(BP)和辛醇/水分配系数(logK_(ow))等的QSPR模型,预测BP的模型含3个变量[前线轨道能量差(E_(l...用量子化学MOPAC-AM1法计算21种多环芳烃(PAHs)的SEDs(steric and electronic descriptors)参数,然后用多元线性回归法建立预测多环芳烃的沸点(BP)和辛醇/水分配系数(logK_(ow))等的QSPR模型,预测BP的模型含3个变量[前线轨道能量差(E_(lumo)-E_(homo))、分子总电子能(EE)和分子总连接性(TCon)],预测logK_(ow)的模型含3个变量[偶极矩(D)、分子总能量(TE)和分子总连接性(TCon)]。所建2个模型的相关系数的平方(R^2)分别为0.997 6和0.9861,交叉验证系数(R_(LOO)~2)分别为0.9820和0.9575,说明模型均具有很好的预测能力和较强的稳健性,同时也证明SEDs参数适用于多环芳烃类化合物的QSPR研究。展开更多
基金TheNationalKeyBasicResearchFoundationofChina (No .G1 9990 4 571 1 )
文摘The theoretical linear solvation energy relationship(TLSER) approach was adopted to predict the aqueous solubility and n -octanol/water partition coefficient of three groups of environmentally important chemicals-polychlorinated biphenyls(PCBs), polychlorinated dibenzodioxins and dibenzofurans(PCDDs and PCDFs). For each compound, five quantum parameters were calculated using AM1 semiempirical molecular orbital methods and used as structure descriptors: average molecular polarizability(α), energy of the lowest unoccupied molecular orbit( E _ LUMO ), energy of the highest occupied molecular orbit( E _ HOMO ), the most positive charge on a hydrogen atom( q _+), and the most negative atomic partial charge( q _-) in the solute molecule. Then standard independent variables in TLSER equation was extracted and two series of quantitative equations between these quantum parameters and aqueous solubility and n -octanol/water partition coefficient were obtained by stepwise multiple linear regression(MLR) method. The developed equations have both quite high accuracy and explicit meanings. And the cross-validation test illustrated the good predictive power and stability of the established models. The results showed that TLSER could be used as a promising approach in the estimation of partition and solubility properties of macromolecular chemicals, such as persistent organic pollutants.
基金the National Natural Science Foundation of China(No.52175156)the Key Research and Development Projects of Shaanxi Province(No.2021GY-157)+1 种基金the Young Talent fund of University Association for Science and Technology in Shaanxi(No.20220615)the Special Fund for Basic Scientific Research of Central Colleges(Chang an University)with Nos.300102221512,300102221510,and 300102222502.
文摘Development of energy-efficient lubricants is a way to reduce energy consumption for transportation,with the tendency to design molecules that are beneficial in reducing the viscosity of synthetic oils.Oligoether esters(OEEs),as a low-viscosity ester base oil,have characteristics such as simple synthesis and excellent lubrication effect;however,the application of OEEs in tribology field has rarely been investigated.The objective of the present study is to investigate the effect of structure on the lubricating performance of OEEs and to develop a predictive model for OEEs based on quantitative structure‒property relationship(QSPR)through a combination of experiment and statistical modeling.Results showed that glycol chains contribute positively to lubrication with the ether functional groups increasing the sites of adsorption.Compared to branched-chain OEEs,straight-chain OEEs exhibited reduced wear,which was mainly due to the thicker adsorption film formed by the straight-chain structure.Furthermore,carbon films were detected on lightly worn surfaces,indicating that OEEs underwent oxidation during the friction process.Based on the results of principal component analysis(PCA)and partial least squares(PLS),it could be found that the predictive models of viscosity‒temperature performance,thermal stability performance,coefficient of friction(COF),and wear volume(WV)performed well and robustly.Among them,COF and WV can be best predicted with an R^(2) of about 0.90.
基金supported by the Natural Science Foundation of Fujian Province (D0710019)the Natural Science Foundation of Overseas Chinese Affairs Office of the State Council (06QZR09)
文摘Based on the quantum chemical descriptors,quantitative structure-property relationship（QSPR） models have been developed to estimate and predict the photodegradation rate constant（logK） of polycyclic aromatic hydrocarbons（PAHs） by use of linear method（multiple linear regression,MLR） and non-linear method（back propagation artificial neural network,BP-ANN）.A BP-ANN with 3-3-1 architecture was generated by using three quantum chemical descriptors appearing in the MLR model.The standard heat of formation（HOF）,the gap of frontier molecular orbital energies（ΔELH） and total energy（TE） were inputs and its output was logK.Leave-One-Out（LOO） Cross-Validated correlation coefficient（R^2CV） of the established MLR and BP-ANN models were 0.6383 and 0.7843,respectively.The nonlinear BP-ANN model has better predictive ability compared to the linear MLR model with the root mean square error（RMSE） for training and validation sets to be 0.1071,0.1514 and the squared correlation coefficient（R^2） of 0.9791,0.9897,respectively.In addition,some insights into the molecular structural features affecting the photodegradation of PAHs were also discussed.
文摘The interaction of nanoparticles with proteins is extremely complex, important for understanding the biological properties of nanomaterials, but is very poorly understood. We have employed a combinatorial library of surface modified gold nanoparticles to interrogate the relationships between the nanoparticle surface chemistry and the specific and nonspecific binding to a common, important, and representative enzyme, acetylcholinesterase （ACHE）. We also used Bayesian neural networks to generate robust quantitative structure-property relationship （QSPR） models relating the nanoparticle surface to the AChE binding that also provided significant understanding into the molecular basis for these interactions. The results illustrate the insights that result from a synergistic blending of experimental combinatorial synthesis and biological testing of nanoparticles with quantitative computational methods and molecular modeling.
基金Supported by the National Natural Science Foundation of China(No.20502022)
文摘Quantitative structure-property relationships（QSPRs） have been developed to predict the thermal stability for a set of 22 nitroaromatic compounds by means of the theoretical descriptors derived from electrostatic potentials on molecular surface. Several techniques, including partial least squares regression（PLS）, least-squares support vector machine（LSSVM） and Gaussian process（GP） have been utilized to establish the relationships between the structural descriptor and the decomposition enthalpy. The nonlinear LSSVM and GP models have proven to own a better predictive ability than the linear PLS method. Moreover, owing to its ability to handle both linear- and nonlinear-hybrid relationship, GP gives a stronger fitting ability and a better predictive power than LSSVM, and therefore could be well applied to developing QSPR models for the thermal stability of nitroaromatic explosives.
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%.
文摘In this paper, according to the peak numbers of the nuclear magnetic resonance and the Randic embranchment degree (δ_i) of carbon atom i, the carbon atom’s environment valence g_i is defined as: g_i=(t_i+δ_i)/2.The g_i reflect the characteristic of each carbon atom, and as well as the conjunction detail of the carbon atom with other carbon atoms.So, the g_ i could distinguish better the chemical environment of each carbon atom in the molecule than δ_i.A connectivity index of environment valence ( mS) and its athwart index ( mS′) are proposed based on the adjacency matrix and the carbon atom’s environment valence g_i.Among them, the 0S and 0S′ include the characteristic and the connectivity of each carbon atom, the 1S and 1S′ reflect the second conjunction between carbon atoms.Based on 0S′ and N(the number of carbon atom), a new structural parameter——symmetry degree (N_ ec), is defined as: N_ ec=[(0S′_S/0S′_C)N] 2/3,and the N_ ec reflect the size of the molecule as well as the symmetry of the molecule.The N_ ec, 0S and R_n(the biggest ring’s edge numbers of cycloalkanes) of 474 saturated hydrocarbons (216 paraffins and 258 cycloalkanes) were calculated and correlated with their boiling points.The best regression equation was obtained as follow: ln(1056-T_b)=6.9480-0.1040N_ ec -0.0086890S-0.009614R_ n+0.01998R 0.5_n,n=474,R=0.9989,F=52627,S=5.63K.The model was checked up by the Jackknife’s method.It should have overall steadiness and could be used for predicting the boiling point of saturated hydrocarbons.
文摘用量子化学MOPAC-AM1法计算21种多环芳烃(PAHs)的SEDs(steric and electronic descriptors)参数,然后用多元线性回归法建立预测多环芳烃的沸点(BP)和辛醇/水分配系数(logK_(ow))等的QSPR模型,预测BP的模型含3个变量[前线轨道能量差(E_(lumo)-E_(homo))、分子总电子能(EE)和分子总连接性(TCon)],预测logK_(ow)的模型含3个变量[偶极矩(D)、分子总能量(TE)和分子总连接性(TCon)]。所建2个模型的相关系数的平方(R^2)分别为0.997 6和0.9861,交叉验证系数(R_(LOO)~2)分别为0.9820和0.9575,说明模型均具有很好的预测能力和较强的稳健性,同时也证明SEDs参数适用于多环芳烃类化合物的QSPR研究。
