QSRR-based estimation of the retention time of opiate and sedative drugs by comprehensive two-dimensional gas chromatography

Recent applications of chemometrics in one- and two-dimensional chromatography

The proliferation of increasingly more sophisticated analytical separation systems, often incorporating increasingly more powerful detection techniques, such as high-resolution mass spectrometry, causes an urgent need for highly efficient data-analysis and optimization strategies. This is especially true for comprehensive two-dimensional chromatography applied to the separation of very complex samples. In this contribution, the requirement for chemometric tools is explained and the latest developments in approaches for (pre-)processing and analyzing data arising from one- and two-dimensional chromatography systems are reviewed. The final part of this review focuses on the application of chemometrics for method development and optimization.

A quantitative structure-activity relationship study of anti-HIV activity of substituted HEPT using nonlinear models

We performed studies on extended series of 79 HEPT ligands (1-[(2-hydroxyethoxy)methyl]-6-(phenylthio)thymine), inhibitors of HIV reverse-transcriptase with anti-HIV biological activity, using quantitative structure–activity relationship (QSAR) methods that imply analysis of correlations and representation of models. A suitable set of molecular descriptors was calculated, and the genetic algorithm was employed to select those descriptors which resulted in the best-fit models. The kernel partial least square and Levenberg–Marquardt artificial neural network were utilized to construct the nonlinear QSAR models. The proposed methods will be of great significance in this research, and would be expected to apply to other similar research fields.

Quantitative structure-retention relationship for retention behavior of organic pollutants in textile wastewaters and landfill leachate in LC-APCI-MS

INTRODUCTION

A quantitative structure-retention relation (QSRR) study was conducted on the retention times of organic pollutants in textile wastewaters and landfill leachate which was obtained by liquid chromatography-reversed phase-atmospheric pressure chemical ionization-mass spectrometry.

METHODS

The genetic algorithm was used as descriptor selection and model development method. Modeling of the relationship between selected molecular descriptors and retention time was achieved by linear (partial least square) and nonlinear (Levenberg-Marquardt artificial neural network, L-M ANN) methods. Linear and nonlinear models provide good results whereas more accurate results were obtained by the L-M ANN model.

CONCLUSION

This is the first research on the QSRR of the organic pollutants in textile wastewaters and landfill leachate against the retention time.