Quantitative structure–(chromatographic) retention relationships

Abstract

Since the pioneering works of Kaliszan (R. Kaliszan, Quantitative Structure-Chromatographic Retention Relationships, Wiley, New York, 1987; and R. Kaliszan, Structure and Retention in Chromatography. A Chemometric Approach, Harwood Academic, Amsterdam, 1997) no comprehensive summary is available in the field. Present review covers the period of 1996-August 2006. The sources are grouped according to the special properties of kinds of chromatography: Quantitative structure-retention relationship in gas chromatography, in planar chromatography, in column liquid chromatography, in micellar liquid chromatography, affinity chromatography and quantitative structure enantioselective retention relationships. General tendencies, misleading practice and conclusions, validation of the models, suggestions for future works are summarized for each sub-field. Some straightforward applications are emphasized but standard ones. The sources and the model compounds, descriptors, predicted retention data, modeling methods and indicators of their performance, validation of models, and stationary phases are collected in the tables. Some important conclusions are: Not all physicochemical descriptors correlate with the retention data strongly; the heat of formation is not related to the chromatographic retention. It is not appropriate to give the errors of Kovats indices in percentages. The apparently low values (1-3%) can disorient the reviewers and readers. Contemporary mean interlaboratory reproducibility of Kovats indices are about 5-10 i.u. for standard non polar phases and 10-25 i.u. for standard polar phases. The predictive performance of QSRR models deteriorates as the polarity of GC stationary phase increases. The correlation coefficient alone is not a particularly good indicator for the model performance. Residuals are more useful than plots of measured and calculated values. There is no need to give the retention data in a form of an equation if the numbers of compounds are small. The domain of model applicability of models should be given in all cases.

Critical evaluation of a simple retention time predictor based on LogKow as a complementary tool in the identification of emerging contaminants in water

“…There are several in silico, Quantitative Structure-Retention Relationship (QSRR) methods available for the prediction of LC retention times, used in a variety of research [13,14]. The principal aim of QSRR is to predict retention data from the molecular structure, using descriptors such as molecular mass, polar surface area, Log P, molar polarisability and molar volume.…”

A New Combination Strategy as Applied in Predicting Chromatographic Retention Times of Oligonucleotides at a Range of Temperatures from 30 °C to 80 °C

“…Nowadays, high performance liquid chromatography has become one of the most efficient methods in DNA purification after synthesis as well as DNA quantification; however, the complexity from a proper choice of a suitable starting point for method development has bottlenecked its ever increasing usage. Although trial-and-error or screening approaches 6,7 are used, they are labor-intensive, and thus time-consuming and expensive.…”

“…[8][9][10] Quantitative structure-retention relationship (QSRR) models provide a probable solution for studying molecular chromatographic retention behavior. The principal aim of QSSR, on the premise that physicochemical properties of molecules are related to their structures, is to derive models for describing the chromatographic retention on a given chromatographic system and thereby predicting the retention of new sol-utes; 6 still the models can be used to elucidate potential retention mechanism, optimize the separation of complex mixtures or prepare experimental designs without futile experiments. Another goal of QSRR, reported in the field of proteomics, is to increase the number of correct identifications of peptides.…”

“…Another goal of QSRR, reported in the field of proteomics, is to increase the number of correct identifications of peptides. [11][12][13] Recently, two papers written by Héberger 6 and Kaliszan 14 contributed comprehensive reviews on QSRR. To obtain reliable QSRR models, appropriate input data is necessary and a stringent statistical analysis must be carried out.…”

“…6,14 Generally, descriptors encoding molecular structures are classified as physicochemical, quantumchemical, topological descriptors, etc. 6 The advantage of physicochemical descriptors is that they are generally strongly related to the retention; however, they are often not available or with relatively large errors only.…”