Quantitative Structure-Property Relationship on Prediction of Surface Tension of Nonionic Surfactants

A quantitative structure-property relationship (QSPR) study has been made for the prediction of the surface tension of nonionic surfactants in aqueous solution. The regressed model includes a topological descriptor, the Kier & Hall index of zero order (KH0) of the hydrophobic segment of surfactant and a quantum chemical one, the heat of formation (fHD) of surfactant molecules. The established general QSPR between the surface tension and the descriptors produces a correlation coefficient of multiple determination, 2r=0.9877, for 30 studied nonionic surfactants.

A Quantitative Structure Property Relationship for Prediction of Flash Point of Alkanes Using Molecular Connectivity Indices

Many structure-property/activity studies use graph theoretical indices, which are based on the topological properties of a molecule viewed as a graph. Since topological indices can be derived directly from the molecular structure without any experimental effort, they provide a simple and straightforward method for property prediction. In this work the flash point of alkanes was modeled by a set of molecular connectivity indices （Х）, modified molecular connectivity indices （ ^mХ^v ） and valance molecular connectivity indices （ ^mХ^v ）, with ^mХ^v calculated using the hydrogen perturbation. A stepwise Multiple Linear Regression （MLR） method was used to select the best indices. The predicted flash points are in good agreement with the experimental data, with the average absolute deviation 4.3 K.

Quantitative Correlation of Chromatographic Retention and Acute Toxicity for Alkyl(1-phenylsulfonyl) Cycloalkane Carboxylates and Their Structural Parameters by DFT

Twenty eight alkyl(1-phenylsulfonyl) cycloalkane carboxylates were computed at the B3LYP/6-31G* level. Based on linear solvation energy theory, two quantitative correlation equations of the molecular structures of alkyl(1-phenylsulfonyl) cycloalkane carboxylate com- pounds to their chromatographic retention (capacity factor lgKW) and the toxicity for photo- bacterium phosphoreum (–lgEC50) were developed by using the molecular structural parameters as theoretical descriptors (r2 = 0.9501, 0.9488). The two quantitative correlation equations were consequently cross validated by leave-one-out (LOO) validation method with q2 of 0.9113 and 0.9281, respectively. The result showed that the two equations achieved in this work by B3LYP/6-31G* are both more advantageous than those from AM1, and can be used to predict the lgKW and –lgEC50 of congeneric organics.

Quantitative Models for the Structure and Photodegradation of Polycyclic Aromatic Hydrocarbons

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.

Quantitative structure－activity study on the reductive dehalogenation potency of the halogenated aromatics

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Molecular Structural Characterization and Quantitative Prediction of Reduced Ion Mobility Constants for Diversified Organic Compounds

Based on two-dimensional topological structures, a novel molecular electronegativity interaction vector with hybridization （MEHIV） was developed to describe atomic hybridization state in different molecular environments. Five quantitative models by MEHIV characterization and multiple linear regression modeling were successfully established to predict reduced ion mobility constants （Ko） of alkanes, aromatic hydrocarbons, fatty alcohols, fatty aldehydes and ketones and carboxylic esters. The correlation coefficients Roy by leave-one-out cross-validation are 0.792, 0.787, 0,949, 0.972 and 0.981, respectively, and the standard deviations SDcv are 0.067, 0.086, 0.064, 0.043 and 0.042, respectively. These results suggested that MEHIV is an excellent topological index descriptor with many advantages such as straightforward physicochemical meaning, high characterization competence, convenient expansibility and easy manipulation.

邻苯二甲酸酯类化合物正辛醇-水分配系数的QSPR研究

采用PM3算法计算邻苯二甲酸酯类化合物(PAEs)的量子化学参数,应用偏最小二乘(PLS)算法建立了PAEs的正辛醇-水分配系数(KOW)的定量结构-性质相关(QSPR)模型.模型的结果表明,用量子化学参数建立的QSPR模型相关性显著,具有较好的稳健性和预测能力,因此,利用该模型可对其他PAEs分子的lgKOW值进行初步预测.模型的结果表明,影响lgKOW的主要量子化学参数是分子总能量TE、相对分子质量Mr、平均分子极化率α和分子生成热ΔHf.lgKOW随着Mr和α的增大而增大,随着TE和ΔHf的增大而减小.

基于遗传-支持向量机和遗传-径向基神经网络的有机物正辛醇-水分配系数QSPR研究

基于遗传算法(GA)的因子筛选和支持向量机(SVM)的非线性回归,提出了1种改进的有机物定量结构-性质相关(QSPR)建模方法——遗传-支持向量机(GA-SVM),并将其用于38种食品工业常用有机物正辛醇-水分配系数(Kow)的QSPR建模.结果显示,QSPR模型选取了分子量、Hansen极性、沸点、含氧率和含氢率5种参数;模型的预测值与实测值间的误差平方和(SSE)、均方差(RMSE)和决定系数(R2)分别为0.048、0.036和0.999,表明模型具有较强的预测能力;同时,交叉验证的结果(SSE=0.295,RMSE=0.089,R2=0.995)也表明,模型具有良好的稳健性,因此,GA-SVM算法适用于对有机物正辛醇-水分配系数的QSPR建模.此外,将基于GA-SVM的QSPR模型分别与基于遗传-径向基神经网络(GA-RBFNN)和基于线性算法的模型进行了比较,结果表明,应用GA-SVM建立的QSPR模型无论从稳健性还是预测能力上都优于应用其它2种算法建立的模型,因此,GA-SVM算法比GA-RBFNN和线性算法更适合于对有机物正辛醇-水分配系数进行QSPR建模.

基于DFT的二噁英脂-水分配系数QSPR研究

基于密度泛函理论,利用多氯二苯并对二噁英(PCDDs)和多氯二苯并呋喃(PCDFs)几何构型全优化而得的量子化学和延伸计算参数:前线轨道能、熵、无迹四极矩、分子绝对硬度、电负性和亲电指数等,对两类化合物在(虹鳉)脂-水间的平衡分配性质进行了定量化模型研究,并由多元线性回归分析(MLR)建立了二次函数型的定量结构-性质相关关系(QSPR):lgKbw=5.343-0.001(S-125.480)2-0.355(ω-3.239)2+0.006(Qzz-2.950)2-22.728(η-2.365)2.结果表明,模型具有较高的拟合优度与稳健性,其决定系数和交叉验证相关系数分别为0.943和0.908,且模型的内外部预测性能理想,总体质量明显优于SOFA(solubility parameter for fate analysis)计算结果,可用于未知脂-水分配性质的预测;PCDD/Fs脂-水平衡分配(系数)主要与分子体积以及由电荷分布决定的芳烃分子间相互作用有关,也受潜在的生物转化和分子反应性的影响.

O/I和量化参数值对脂肪醇-lgSw,lgKow的QSPR研究

应用Chem3D8.0软件计算了与脂肪醇的lgSw,lgKow相关的O/I和结构参数,对51种脂肪醇的-lgSw,lgKow做了定量结构-性质关系(QSPR)的研究.给出了新的模型方程,它能表征定量结构关系.计算了脂肪醇的-lgSw,lgKow值,并与理论值进行了比较,证实了新模型的计算值与实验值比较接近.模型中自变量不存在相关关系,残差呈正态分布.

Estimation of photolysis half-lives of dyes in a continuous-flow system with the aid of quantitative structure-property relationship

In this paper the photolysis half-lives of the model dyes in water solutions and under ultraviolet （UV） radiation were determined by using a continuous-flow spectrophotometric method. A quantitative structure- property relationship （QSPR） study was carried out using 21 descriptors based on different chemometric tools including stepwise multiple linear regression （MLR） and partial least squares （PLS） for the prediction of the photolysis half-life （t1/2） of dyes. For the selection of test set compounds, a K-means clustering technique was used to classify the entire data set, so that all clusters were properly represented in both training and test sets. The QSPR results obtained with these models show that in MLR-derived model, photolysis half-lives of dyes depended strongly on energy of the highest occupied molecular orbital （EHoMO）, largest electron density of an atom in the molecule （ED^＋） and lipophilicity （logP）. While in the model derived from PLS, besides aforementioned EHOMO and ED^＋ descriptors, the molecular surface area （Sm）, molecular weight （M-W）, electronegativity （X）, energy of the second highest occupied molecular orbital （EHoMO- 1） and dipole moment （μ） had dominant effects on logt1/2 values of dyes. These were applicable for all classes of studied dyes （including monoazo, disazo, oxazine, sulfo- nephthaleins and derivatives of fluorescein）. The results were also assessed for their consistency with findings from other similar studies.

Prediction on Critical Micelle Concentration of Nonionic Surfactants in Aqueous Solution:Quantitative Structure-Property Relationship Approach

In order to predict the critical micelle concentration (cmc) of nonionic surfactants in aqueous solution,a quantitative structure-property relationship (QSPR) was found for 77 nonionic surfactants belonging to eight series. The best-regressed model contained four quantum-chemical descriptors,the heat of formation (ΔH),the molecular dipole moment (D),the energy of the lowest unoccupied molecular orbital (E_ LUMO ) and the energy of the highest occupied molecular orbital (E_ HOMO ) of the surfactant molecule; two constitutional descriptors,the molecular weight of surfactant (M) and the number of oxygen and nitrogen atoms (n_ ON ) of the hydrophilic fragment of surfactant molecule; and one topological descriptor,the Kier & Hall index of zero order (KH0) of the hydrophobic fragment of the surfactant. The established general QSPR between lg(cmc) and the descriptors produced a relevant coefficient of multiple determination:R 2=0.986. When cross terms were considered,the corresponding best model contained five descriptors E_ LUMO ,D,KH0,M and a cross term n_ ON ·KH0,which also produced the same coefficient as the seven-parameter model.

苯硫酚衍生物的溶解性质的定量结构-性质相关(QSPR)研究

在高效液相色谱(HPLC)测定方法的基础上,采用摇瓶法,准确测得25℃时16个取代苯硫酚类化合物的水溶解度(-lgSw)和19个正辛醇/水分配系数(lgKow)数据.采用密度泛函理论(DFT)中的B3LYP方法,在6-311G**基组上全优化计算得到24个取代苯硫酚的结构参数和热力学参数,采用SPSS12.0 for windows程序,分别求得-lgSw和lgKow值与这些参数之间的相关方程,相关系数R2分别为0.958和0.946.用交叉验证法对方程的稳定性进行了验证(q2分别为0.925和0.930),并用t检验和变异膨胀因子(Variance Inflation Factors,VIF)对两个方程中各变量的显著性和自相关性进行了检验.结果表明:所建方程具有较强的预测能力,并用其预测了8个没有实验数据的取代苯硫酚的-lgSw值和5个lgKow值.

基于TLSER和QSPR关系预测PAHs和PCBs的LDPE膜-空气分配系数

有机污染物在被动采样材料与环境介质之间的平衡分配系数(K_P),是测定环境中有机污染物浓度的重要参数,但K_P值大部分都需要经过繁琐的实验测定获取,无法逐个测定数量繁多的污染物,因此需开发一种预测K_P值的方法。为此,搜集整理了一些多环芳香烃(PAHs)和多氯联苯(PCBs)的低密度聚乙烯(LDPE)-空气分配系数(K_PA)的实测值,基于理论线性溶解能(TLSER)和定量结构性质关系(QSPR),利用逐步多元线性回归(MLR)分别构建了预测K_PA值的模型。模型的决定系数R2adj分别为0.927和0.956,交叉验证系数Q2LOO分别为0.915和0.946,外部系数Q2ext分别为0.913和0.960。结果表明,2种模型具有良好的拟合优度、稳健性和预测能力,并解释了模型的机理。所构建的2种模型均可用来预测应用域内有机污染物的LDPE膜-空气分配系数。

Heterogeneous Fenton degradation of azodyes catalyzed by modified polyacrylonitrile fiber Fe complexes: QSPR (quantitative structure peorperty relationship) study

The amidoximated polyacrylonitrile （PAN） fiber Fe complexeswere prepared and used as the heterogeneous Fenton catalysts for thedegradation of28 anionicwater soluble azodyes inwater under visible irradiation. The multiple linear regression （MLR） methodwas employed todevelop the quantitative structure property relationship （QSPR） model equations for thedecoloration and mineralization of azodyes. Moreover, the predictive ability of the QSPR model equationswas assessed using Leave-one-out （LOO） and cross-validation （CV） methods. Additionally, the effect of Fe content of catalyst and the sodium chloride inwater on QSPR model equationswere also investigated. The results indicated that the heterogeneous photo-Fentondegradation of the azodyeswithdifferent structureswas conducted in the presence of the amidoximated PAN fiber Fe complex. The QSPR model equations for thedyedecoloration and mineralizationwere successfullydeveloped using MLR technique. MW/S （molecularweightdivided by the number of sulphonate groups） and N N=N （the number of azo linkage） are considered as the most importantdetermining factor for thedyedegradation and mineralization, and there is a significant negative correlation between MW/S or N N=N anddegradation percentage or total organic carbon （TOC） removal. Moreover, LOO and CV analysis suggested that the obtained QSPR model equations have the better prediction ability. The variation in Fe content of catalyst and the addition of sodium chloridedid not alter the nature of the QSPR model equations.

咪唑类离子液体捕集CO_2性能的定量结构-性质相关性(QSPR)研究

CO_2是主要的温室气体,大量CO_2的存在严重影响着人类的生存环境和生态平衡,而咪唑型离子液体具有独特的气体溶解性,在CO_2的捕集分离中有很好的应用前景。基于定量结构-性质相关性(QSPR)原理,研究了咪唑类离子液体捕集CO_2的性能与其结构参数之间的内在定量关系。应用遗传算法获得与捕集量最为密切相关的一组描述符作为输入参数,随后,分别采用多元线性回归算法及支持向量机结合粒子群优化算法建立了咪唑类离子液体捕集CO_2的性能与其描述符之间的线性和非线性模型。多元线性回归算法得出训练集和测试集的复相关系数分别为0.765和0.814,支持向量机算法得出训练集和测试集的复相关系数分别为0.987和0.933。对预测模型进行了评价验证以及稳定性分析,结果表明,2种模型具有良好的稳定性能和预测能力。

Experimental and QSPR Studies on n-Octanol/water Partition Coefficient (lgK_(ow)) of Substituted Aniline

The n-octanol/water partition coefficients （lgKow） of 18 substituted anilines were determined at 25 ℃ by shake-flask method. The geometrical optimization of substituted anilines has been performed at B3LYP/6-311G^＊＊ level with Gaussian98 program, and the molecular surface areas of substituted anilines were calculated using ChemOffice 2004 program. The calculated structural parameters of substituted anilines were used as theoretical descriptors and the two-parameter （molecular surface area （MA） and the energy of the highest occupied molecular orbital （EaoMo）） quantitative structure-property relationship （QSPR） model of lgKow for substituted aniline with molecular structural parameters was developed by multi-linear regression method. The regression coefficient square （r^2） is 0.990 and the standard deviation SE 0.109. The model was validated by variance inflation factors （VIF） and t-test, and the results show that there exists small self-correlation between variables of the model with perfect stability. The model gives results in good qualitative agreement with experimental data. At last, the model was applied to predict lgKow values of five substituted anilines whose lgKow values have not been determined experimentally.

QSPR of n-Octanol/water Partition Coefficient (lgK_(ow)) for Alkyl(1-phenylsulfonyl) Cycloalkane-carboxylates

Quantum chemistry parameters of 28 alkyl（1-phenylsulfonyl） cycloalkane-carboxy-lates were computed at the 6-31G＊ level in fully optimal manner using B3LYP method of density functional theory （DFT）. With GQSARF2.0 program, the correlation equations that can predict n-octanol/water partition coefficient （lgKow） were developed using the structural and thermodynamic parameters of 28 alkyl（1-phenylsulfonyl） cycloalkane-carboxylates with experimental data of lgKow as theoretical descriptors; the correlation coefficient （R^2） was 0.9452 and the cross-validation squared correlation coefficient （Rcv^2） 0.9312. Furthermore, a four-variable model from MEDV was obtained, of which R2 = 0.9497 and Rov^2 =0.9388. The models were validated by variance inflation factor （VIF） and t-test. Cross-validation indicates that the correlation and predicting ability of the model based on both DFT method and MEDV are more advantageous than those obtained from semi-empirical AM1 method.

QSPR Study on the Boiling Points of Some Oxygenand Sulfur-containing Organic Compounds

Structural and thermodynamic parameters of 56 oxygen-containing and 56 sulfur- containing organic compounds were computed at the B3LPY/6-311G＊＊ level using density functional theory （DFT） method. Furthermore,the dependent equations between the experimental data of boiling points （Tb） and theoretical parameters were proposed with SPSS12.0 for windows software,whose correlation coefficients R2 are 0.933 and 0.945. These dependent equations were validated by cross-validation method （q2 are 0.923 and 0.929,respectively）. VIF （variance inflation factors） and t-value methods were also used to verify the significance and self-correlationship of each variable. Results indicate that our dependent equation exhibits good prediction ability,and molecular polarizability （α） is the main factor affecting the Tb of oxygen- and sulfur-containing organic compounds. To our interest,obvious dependence could also be found among the oxygen- and sulfur-containing organic compounds＇ experimental data of boiling points （Tb） with R^2 of 0.857.

基于ANN-QSPR算法的新型纯碳水化合物燃料性质预测方法

开发新型纯碳水化合物燃料作为新能源时,必须预测和筛选纯碳水化合物的物理性质,从而找到可能合适的化学物质,然而由实验来逐一确定大量分子的物理性质既耗时又昂贵。研究发现,运用人工智能网络—定量构效关系(ANN-QSPR)算法来建立纯碳水化合物物理性质的计算模型可以起到事半功倍的效果。基于DIPPR 801数据库中的纯组分性质和DragonX软件包计算了相应碳水化合物的分子描述符,所构建的模型结合了定量构效关系(QSPR)和两层前馈人工智能网络(ANN)。由此建立了多个全面而可靠的模型来预测新型纯碳水化合物燃料的各种物理性质,包括正常沸点、闪点、燃烧焓、蒸发焓、液体密度、表面张力、液体的黏度和熔点等。为了提高模型中数据集之间的一致性,还引入了主成分分析法(PCA),以进一步消除分子描述符值的维数。另外,通过共识建模进行交叉验证,减少了不确定性的影响,提高了模型的预测精度。