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新型 N-取代 2-亚烷基-4-氧代噻唑烷的定量构效关系。

Quantitative structure-retention relationship of new N-substituted 2-alkylidene-4-oxothiazolidines.

机构信息

Innovation Centre, Faculty of Chemistry Ltd, Belgrade, Serbia.

出版信息

J Sep Sci. 2011 Sep;34(18):2397-404. doi: 10.1002/jssc.201100266. Epub 2011 Jul 7.

DOI:10.1002/jssc.201100266
PMID:21735548
Abstract

A quantitative structure-retention relationship (QSRR) was built to investigate the relationship between the structural descriptors of 23 newly synthesized N-substituted 2-alkylidene-4-oxothiazolidines and their chromatographic retention. Retention was investigated by means of the reversed-phase thin-layer chromatography (RP-TLC) on the C(18) and CN stationary phases, using methanol, acetonitrile, and tetrahydrofuran as organic modifiers. Full geometry optimization based on the Austin Model 1 (AM1) semi-empirical molecular orbital method was carried out and a set of physicochemical molecular descriptors was calculated from the optimized structures. QSRR was built by means of multiple linear regression (MLR) and partial least squares regression (PLS). The best MLR and PLS models were chosen, on the basis of comparison of the statistical parameters (squared correlation coefficient (R(2)), cross-validated coefficient (R(cv)(2)), and Fischer significance value (F) were used for MLR and the square of the multiple correlation coefficients for the calibration objects (R(2)Y (cum)) and the square of the multiple correlation coefficients for the cross-validation segments (Q(2)Y (cum)) were used for PLS models). Statistically significant and physically meaningful QSRR provided better insight on understanding the retention behavior of the new series of compounds. Lipohilicity (expressed as log P) was included in all MLR and PLS models.

摘要

建立了一个定量结构-保留关系(QSRR),以研究 23 种新合成的 N-取代 2-亚烷基-4-氧代噻唑烷的结构描述符与它们的色谱保留之间的关系。保留通过反相薄层层析(RP-TLC)在 C(18)和 CN 固定相上进行,使用甲醇、乙腈和四氢呋喃作为有机修饰剂。基于 Austin Model 1(AM1)半经验分子轨道方法进行了全几何优化,并从优化结构中计算了一组物理化学分子描述符。通过多元线性回归(MLR)和偏最小二乘回归(PLS)建立了 QSRR。最佳的 MLR 和 PLS 模型是基于统计参数(平方相关系数(R(2))、交叉验证系数(R(cv)(2))和 Fischer 显著性值(F)用于 MLR,以及校准对象的多元相关系数的平方(R(2)Y(cum))和交叉验证段的多元相关系数的平方(Q(2)Y(cum))用于 PLS 模型)的比较来选择的。统计学上显著且具有物理意义的 QSRR 提供了更好的理解新化合物系列保留行为的洞察力。亲脂性(以 log P 表示)包含在所有 MLR 和 PLS 模型中。

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