分子电性距离矢量用于多溴联苯醚分子结构表达及色谱保留时间预测

应用分子电性距离矢量(MEDV)对多溴联苯醚(PBDEs)的209种同系物进行结构表征.通过多元线性回归的方法,建立了PBDEs定量结构-色谱保留(QSRR)关系的6个变量和5个变量的两种模型.两种模型的建模计算值复相关系数R均为0.995;用留一法(LOO)进行了交互检验,其复相关系数(R2cv)分别为0.987和0...     

On the characterization of novel biologically active steroids: Selection of lipophilicity models of newly synthesized steroidal derivatives by classical and non-parametric ranking approaches

In this paper, the guidelines for the interpretation of the results of quantitative structure-retention relationship (QSRR) modeling, comparison and assessment of the established models, as well as the selection of the best and most consistent QSRR model were presented. Various linear and non-linear chemometric regression techniques were used to build QSRR models for chromatographic lipophilicity prediction of a series of triazole, tetrazole, toluenesulfonylhydrazide, nitrile, dinitrile and dione steroid derivatives. Linear regression (LR) and multiple linear regression (MLR) were used as linear techniques, while artificial neural networks (ANNs) were applied as non-linear modeling techniques. Generated models were statistically evaluated applying different approaches for model comparison and ranking. Two non-parametric methods (generalized pair correlation method – GPCM and sum of ranking differences – SRD) were used for model ranking and assessment of the best model for chromatographic lipophilicity prediction using experimentally obtained logk values and row average as a reference ranking. Both, GPCM and SRD, provided highly similar model choice regardless on a different background. These results are in agreement with the classical approach.     

多环芳香硫化合物的定量结构-气相色谱保留指数关系

应用分子电性距离矢量(MEDV)对114个多环芳香硫化合物(PASHs)进行结构表征,通过多元线性回归建立了PASHs的气相色谱保留指数与MEDV参数之间的定量结构-保留值关系模型;同时采用逐步回归分析进行变量筛选,继而以留一法交互检验对所得优化模型进行预测能力评价,所建立的模型的相关系数为0.9947,交互检验相关系数为0.9940,表明该优化模型具有良好的稳定性和预测能力。此外,通过将样本集按2:1分成校准集和测试集预测,统计分析结果显示所建的模型具有良好的相关性和稳定性。本文所建的定量结构-保留值关系(QSRR)模型为预测PASHs的气相色谱保留指数提供了一个便捷有效的新方法。     

An Efficient and Simple Approach to Predict Kovat's Indexes of Polychlorinated Naphthalenes in Gas Chromatography

Molecular structures of polychlorinated naphthalenes were numerically described with a simple but efficient encoding method. Correspondingly a set of structural parameters were obtained for these compounds and linearly correlated with their gas chromatography retention indexes. A quantitative structure‐retention relationship Model (M1) was developed by using multiple linear regression (MLR) with correlation coefficient R = 0.9880 between the numeric structural codes and the gas chromatography retention indexes of 62 polychlorinated naphthalenes. If the “leave‐one‐out” cross‐validation procedure was employed to construct QSPR model for all samples, the second model M2 with the correlation coefficient being R = 0.9839 was generated. The structural codes of polychlorinated naphthalenes were tested with MLR for estimation and prediction of the GC RI by models M1 and M2, and the results obtained were satisfactory.     

烯烃气相色谱保留指数的QSRR研究

通过对184个烯烃类化合物在不同固定相不同柱温下的617个样本的气相色谱保留指数值(RI)与其部分参数:拓扑指数(mQ)、偶极矩(DPL)、固定液极性值(CP)及柱温(T)建立定量-色谱保留相关(QSRR)模型.分别利用多元线性回归(MLR)、偏最小二乘回归(PLSR)、人工神经网络(ANN)建模,同时采用内部及外部双重验证的办法对所得模型稳定性能进行深入分析和检验,建模计算值、留一法(LOO)交互检验(CV)预测值和外部样本的复相关系数Rcum,QLOO和Rext分别为0.999 2,0.998 4和0.999 2(MLR);0.999 0,0.998 0和0.999 1(PLSR);0.999 4,0.998 7和0.999 2(ANN).结果表明:所建定量结构保留关系(QSRR)模型具有良好的稳定性和预测能力,较好地揭示了烯烃类化合物在不同固定相不同柱温上气相色谱保留指数的变化规律.     

A QSRR Study on the Chromatographic Retention Indices of Hydroxylated Polychlorinated Biphenyls

Hydroxylated Polychlorinated Biphenyls (HO-PCBs) are the metabolite of polychlorinated biphenyls and have drawn much attention because they have hazard on human health and ecosystems. Molecular connectivity index calculation has been performed for 19 HO-PCB compounds. A number of statistically based parameters have been extracted. Linear relationship between chromatographic retention index (RI) and the molecular connectivity index of 15 compounds in the training set has been established by multiple linear regression method. The other 4 HO-PCBs are used as the external test set. The result shows that the parameters can be well used to express the quantitative structure-retention relationship (QSRR) of HO-PCBs. Good stability and predictive ability have been demonstrated by leave-one-out cross-validation and the external test set.     

A quantitative structure–mobility relationship of organic acids using solvation parameters

A quantitative structure–mobility relationship (QSMR) is proposed to estimate the electrophoretic mobility of diverse sets of analyses in capillary zone electrophoresis using Abraham solvation parameters of analyses, such as the excess molar refraction, polarizability, hydrogen bond acidity, basicity, and molar volume. QSMR was developed for prediction the electrophoretic mobility of 231 organic acids using the solvation parameters calculated by Abraham. Multiple linear regression (MLR) as a linear model and artificial neural network (ANN) methods were used to evaluate the nonlinear behavior of the involved parameters. The prediction results are obtained by nonlinear model, ANN, seem to be superior over MLR and were in good agreement with experimental data. In the proposed ANN–QSMR model, the overall mean percentage deviation values were 5.6, 5.4, and 5.3% and the coefficients of determinations (R²) were 0.84, 0.84, and 0.84 for training, test, and verification set, respectively. To investigate the robustness of the model, cross-validation methods have been established, i.e., leave-one-out and leave-N-out (N?=?5 and 10) and model is showed good predictive ability against data variation in cross-validation process. This model is not only able to accurately predict the migration order of a diverse set of organic acids but also model finds that solvation parameters are responsible in separation mechanism.