Investigation of retention and chiral recognition mechanism using quantitative structure-enantioselectivity retention relationship in high performance liquid chromatography

The enantiomers of a series of fourteen O-ethyl O-(substituted) phenyl N-isopropyl-phosphoroamidothioates have been separated by high performance liquid chromatography (HPLC) on the Pirkle-type chiral stationary phase. Seven molecular descriptors were calculated and four significant descriptors were chosen to correlate against the experimental lnk' values in order to form the quantitative structure-enantioselectivity retention relationships (QSERRs). Through the QSERRs, the retention and enantioselectivity mechanism were examined.

Studies on a Novel Characteristic Atom-pair Holographic Code Applied to Quantitative Structure-chromatographic Retention Relationship of Organic Compounds

6 Atomic fragment types of organic compound have been defined, and the multilevel atom-pair frequency matrix has been constructed according to the occurrence number in pairs of atomic fragments with different bond lengths in the molecule. On the basis of them, a novel molecular coding technique： characteristic atom-pair holographic code （CAHC）, is obtained. To some extent, this method exhibits a large number of benefits at the same time. For example, it can calculate 2D molecular topological descriptor easily, operate without difficulty and possess definite physicochemical meaning of 3D molecular structural characterization methods, and may fetch the complicated information of molecule, etc. Therefore, it is appropriate for the study on quantitative structure-property/activity relationship （QSPR/QSAR） of medicines and biological molecules. We attempt in this paper to utilize the method of CAHC to the quantitative prediction of reversed-phase liquid chromatogram （RPLC） retention data of 33 purine derivatives and 24 steroids. The fitting multiple correlation coefficient R2, cross-validated multiple correlation coefficient Q2 and predicted ability Q^2 pred over test set＇s samples of obtained partial least-square （PLS） regression model are respectively 0.990, 0.893 and 0.977, 0.897, 0.941.

Quantitative Structure-retention Relationship Study of Volatile Components from Rosa Banksiae Ait

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.

Quantitative Structure-retention Relationship Study of Polychlorinated Dibenzothiophenes by Molecular Electronegativity Distance Vector(MEDV)

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.

Quantitative Structure-chromatographic Retention Relationship for Polychlorinated Dibenzothiophenes and Their Corresponding Sulfones

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.

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.

Structural Characterization of Volatile Components of Rosa Banksiae Ait for Estimation and Prediction of Their Linear Retention Indices and Retention Times

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.

Study on Retention in Liquid Chromatography

After joining the faculty at Toyohashi University of Technology (TUT) in 1978, the author has found two areas in separation sciences where microcolumn liquid chromatography (micro LC) can be beneficial One is the hyphenated techniques between many spectroscopic methods such as mass (MS), infrared (FT IR) and atomic emission (ICP), and micro LC The other one is rather difficult, but basic and theoretical approach which deals with retention mechanism in LC The latter project has especially been giving him a lot of scientific funs and honors in the last 20 years On the occasion of being awarded by The Society for Chromatographic Sciences the author would like to summarize his contributions to this topic which asks us "What Is Chromatographic Retention?

Structural Characterization and Retention Time Prediction for Components of Essential Oil of Meconopsis Integrifolia Flowers

A molecular structural characterization （MSC） method called reduced molecular electronegativity-distance vector （MEDVR） was used to describe the molecular structures of 55 components of meconopsis integrifolia flowers. By use of stepwise multiple regression （SMR） and partial least square （PLS） methods, a model with the correlation coefficient （R1） of 0.987 and the standard deviation （SD1） of 1.377 could be obtained. Then through multiple linear regression （MLR）, another model with the correlation coefficient （R2） of 0.989 and standard deviation （SD2） of 1.395 could be constructed. Furthermore, in virtue of variable screening by the stepwise multiple regression technique （SMR）, 8 vectors were selected to build up another model with its correlation coefficient （R3） and standard deviation （SD3） of 0.989 and 1.366, respectively. Then all the three models were evaluated by performing cross-validation with the leave-one-out （LOO） procedure, and the correlation coefficients （QCV） were 0.981, 0.976 and 0.979, respectively. The results show that the models constructed could provide estimation stability and favorable predictive ability.

Estimation and Prediction of Retention Time for a Variety of Volatile Organic Compounds

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.

Prediction of liquid chromatography retention factors for α-branched phenylsulfonyl acetates using quantum chemical descriptors

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.

Prediction of Gas Chromatographic Retention Indices of Organophosphates by DFT and VSMP Method

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.

价键原子指数用于酱香型习酒香气成分保留性质研究

酱香型习酒是一种风味极其独特的酱香型白酒,具有风味醇厚、幽雅细腻和回味悠长的香气,香气成分繁多而复杂.为探究酱香型白酒的主要挥发性物质的组成及香气风味成分的差异,研究建构酱香型习酒香气成分保留指数的定量结构-保留相关性模型,根据酱香型习酒香气成分分子中原子的空间与电性结构,基于拓扑理论,提出了一种新的结构参数-价键原子指数~mA.另计算了酱香型习酒香气成分的电拓扑状态指数(Em),优化筛选了指数中的~0A、E_(1)、E_(2)、E_(14),将4种结构参数作为神经网络三层结构的输入节点数,色谱保留指数作为三层结构的输出节点数,网络结构采用4-12-1方式.建立的预测香气成分保留指数模型的相关系数达到0.9965,计算得到的酱香型习酒香气成分保留指数RI预测值的平均相对误差为1.41%.结果表明,酱香型习酒挥发性香气成分的保留指数,与价键原子指数和电拓扑状态指数之间具有很好的非线性关系,非氢原子结构及—CH_(3)、>CH_(2)、—O—等基团,是影响酱香型习酒香气成分保留指数的主要因素.

药物的定量结构色谱保留关系研究

采用人工神经网络(ANN)建立了110种药物的结构与色谱保留之间的定量关系(QSRR)模型,并将其与偏最小二乘回归(PLSR)模型进行比对.110种药物的ANN模型自相容能力的复相关系数(R^(2))为0.9191,泛化能力的复相关系数(R^(2))为0.9365;而PLSR模型的复相关系数(R^(2))为0.8275和0.8514.结果表明,ANN模型的自相容能力、泛化能力优于PLSR模型.采用集内集和集外集对模型进行检验时,ANN模型的复相关系数(R^(2))为0.9105,也优于PLSR模型,且ANN预测精密度和准确度更高.

Three-dimensional holographic vector of atomic interaction field for quantitative structure-retention relationship of purine bases

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.

A novel molecular distance edge vector as applied to chemical modeling of quantitative structure-retention relationships:Various gas chromatographic retention behaviors of polychlorinated dibenzofurans on different polarity-varying stationary phases

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.

Predicting Chromatographic Retention Time of C10-Chlorinated Paraffins in Gas Chromatography-Mass Spectrometry Using Quantitative Structure Retention Relationship

Chlorinated paraffins（CPs） are potential persistent organic pollutants（POPs）, which threat the safety of environment and organisms. However, the analysis of CPs is a difficult task due to their complex composition containing thousands of congeners. In the present work, quantitative structure retention relationship（QSRR） of CPs was studied. A total of 470 molecular descriptors were generated, for describing the structures of 28 CPs and 12 descriptors relevant to retention time of the CPs were selected by stepwise regression. Then, QSRR models between retention time on the one hand and the selected descriptors on the other hand were established by multiple linear regres- sion（MLR）, partial least squares（PLS） and least square support vector regression（LS-SVR）. The result shows that PLS model is better than MLR and LS-SVR, obtaining a squared correlation coefficient（r2） of 0.9996 and a root mean squared error（RMSE） of 0.015. The PLS model was then used to predict the retention time of 49 C10-CPs. Three of them were investigated by gas chromatography coupled with mass spectrometry（GC-MS）. A well-defined correlation was found between the measured retention time and the predicted value.

Quantitative structure-retention relationship for polychlorinated dibenzofurans based on molecular interaction field analysis

A new quantitative structure-retention relationship (QSRR) model is developed for polychlorinated dibenzofurans (PCDFs) based on molecular interaction field (MIF) analysis. The MIF of all 135 PCDFs is calculated using DRY, C1= and C3 probe, characterizing the hydrophobic and steric interaction between PCDFs and different groups of stationary phase. Then QSRR model is constructed by multiblock partial least squares (MBPLS), and the significance of each block is evaluated by the block importance in the prediction (BIP) method. The model used for prediction is statistically significant, with calibration and cross-validation correlation coefficients 0.9990 and 0.9980 respectively, and relative error less than 1.0%. The results of MBPLS and BIP show that the steric properties have dominant influence on the retention behavior of PCDFs, and then the hydrophobic effects.

植物精油成分气相色谱保留指数的全息定量构效关系

气相色谱保留指数(RI)是色谱分析中的重要参数,但通过实验获取RI值的过程较为繁琐,需要建立一种简便、高效、准确的模型来预测RI值。本文搜集了60种植物精油成分的RI实验值,构建了精油成分化合物的结构性质与RI值之间的全息定量构效关系(HQSAR)模型。当碎片大小(fragment size)、碎片特征(fragment distinction)和全息长度(hologram length)模型参数分别设置为“1~4”、“C,Ch”和199时,可以建立最优HQSAR模型。利用外部测试集验证和留一交叉验证对模型进行检验,经外部测试集验证的预测均方根误差(RMSEP)、预测决定系数(Q_(F3)^(2))、一致性相关系数(CCC)和平均相对误差(MRE)分别为40.45、0.984、0.968和2.20%;经留一交叉验证的交叉验证均方根误差(RMSECV)和MRE分别为72.56和4.17%。此外,HQSAR模型的分子贡献图表明,芳香族化合物的烷基链在连接了羟基基团后,其RI值会增大;脂肪族化合物中存在的长链烷基也会导致RI值增大。研究结果表明,所建立的HQSAR模型能够用于预测植物精油成分的RI值,并为其他精油成分RI值的预测提供可靠依据。

QSRR Study on the Relationship between the Chromatographic Capacity Factor and Lipophilicity and Structure Parameters of Halogenated Thiophenols

The capacity factors （k＇） of fourteen types ofhalogenated thiophenols in different phases of methanol-water eluent were determined by reversed phased high-performance liquid chromatography （RP-HPLC） and the relationships between the logarithm of capacity factor lgK＇ and methanol ratio ψ were analyzed. A fair linear relationship is found between lgK＇ and ψ, and the correlation coefficients R2 of the constructed linear equations are all greater than 0.990. Relationship between the chromatographic data lgKw＇ when extrapolated to pure water and n-octanol/water partition coefficient lgKow obtained by the group contribution method has shown a good linear correlation with R2= 0.956. The structure parameters of fourteen halogenated thiophenols were calculated by using DFT, and the correlation equation of lgKw＇ and structure parameters was obtained by using SPSS, lgKw＇ = -0.409 ＋ 0.039a and R2 = 0.981, meaning that lgKw＇ is mainly determined by the polarizability α.