QSRR:  Quantitative Structure-(Chromatographic) Retention Relationships

Kaliszan, Roman

doi:10.1021/cr068412z

Cited by 437 publications

(280 citation statements)

References 344 publications

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“…The linear solvation energy relationship (LSER) theory describes the retention on the basis of selective solute-solvent interactions [3][4][5]. The linear retention model by Abraham and coworkers [6,7] expresses the retention as the sum of specific interactions between the solutes (independent variables) and the solvents (regression coefficients).…”

Section: Introductionmentioning

confidence: 99%

Application of solvation model to prediction of the solute retention in liquid chromatography over a wide range of mobile-phase compositions

Jirkal

Machovcová

Ševčı́k³

2016

Acta Chromatographica

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Summary.Possibilities have been studied of using a solvation model to predict the retention behavior of solutes in liquid chromatography mobile phases of water-acetonitrile and water-methanol with the organic solvent content varying from 1 to 100 vol%. Twenty-one test solutes, both aliphatic and aromatic compounds, have been selected on the basis of two-level factorial designs. Using the multiple linear regression analysis, regression coefficients, which are characteristics of the stationary and mobile-phase system, were calculated for different mobile phases in the solvation model. Regression coefficients have been used for the prediction of the retention behavior. Unbiased results have been obtained by using two sets, one training and the other the testing set. The predicted retention has been compared with the experimental data. The methanol-water system provided good results at low and medium methanol concentrations; the retention prediction was unsatisfactory for mobile phases containing more than 90% of methanol. The acetonitrile-water system yielded similar results, but the retention prediction ceased to be at acetonitrile concentrations greater than 80%. The retention has primarily been determined by cohesive and acid-base interactions. The dependences of the regression coefficients on the mobile-phase composition were similar for the acetonitrilewater and methanol-water systems.

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Section: Introductionmentioning

confidence: 99%

Application of solvation model to prediction of the solute retention in liquid chromatography over a wide range of mobile-phase compositions

Jirkal

Machovcová

Ševčı́k³

2016

Acta Chromatographica

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“…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.…”

Section: Introductionmentioning

confidence: 99%

“…To obtain reliable QSRR models, appropriate input data is necessary and a stringent statistical analysis must be carried out. 14 In 2007, Put et al 7 published a review on reversed-phase liquid chromatography, mainly focusing on different modeling methodologies applied and molecular descriptors used in QSRRs. Although much success has been achieved in the retention prediction of drug molecules and peptides by QSRR models, there are few applications in quantitative chromatographic retention predictions of oligonucleotides.…”

Section: Introductionmentioning

confidence: 99%

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

Liang

Chen

et al. 2011

J Chinese Chemical Soc

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We develop a new combination strategy to predict retention times of oligonucleotides in ion-pair reversed-phase high performance liquid chromatography. The key step of the strategy is to use score of generalized base properties (SGBP) combined with auto cross covariance (ACC) to resolve the feature representation of nucleic acids. This representation characterize physicochemical, quantumchemical, topological, spatial structural features, etc., and their neighboring effect between bases at a certain distance apart in a sequence. The next step is to use the variables selected by genetic algorithm (GA) to construct prediction models of retention times of oligonucleotides based on support vector machine (SVM). Accordingly, GA-SVM models give different prediction performance using different input descriptors resulting from different step lengths in the ACC transformation, indicating the neighboring effect between bases should not be neglected in the features related to the chromatographic retention of oligonucleotides. As a whole, the GA-SVM predictors obtained from more than 20 training samples can produce satisfying performance in predicting the chromatographic retention of oligonucleotides at a range of temperatures (30°C, 40°C, 50°C, 60°C and 80°C), respectively. The present approach based on the SGBP-ACC-GA-SVM combination shows great application prospect in the field of separation and analysis science, bioinformatics and proteomics.

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“…The main aim of the study was to obtain new adsorbents and determine the qualitative and quantitative character of adsorbentadsorbate interactions for a group of test compounds representing linear and branched aliphatic hydrocarbons. Another aim was to characterise quantitatively the effect of selected structural elements of the adsorbates on their retention, which was realised by the calculation methods based on Quantitative Structure Retention Relationship (QSRR) [6].…”

Section: Introductionmentioning

confidence: 99%

Quantitative relationship and application of 3‐benzylketoimine metal dichlorides in the analysis of volatile hydrocarbons

Wawrzyniak¹

2009

J of Separation Science

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Quantitative relationship and application of 3-benzylketoimine metal dichlorides in the analysis of volatile hydrocarbonsThe paper concerns the analysis of volatile hydrocarbons on newly designed and produced capillary columns. According to the type of coating used, the columns are of the PLOT type, and according to the adsorbate-adsorbent type interactions they belong to the category of complexation GC. The adsorbent used was silica, whose surface was modified with bonded 2-(3-triethoxysilylpropylimino)-3-(benzyl)-pentanone-4 to change the adsorption characteristics. The presence of the ketoimine group of strongly electron-accepting properties permitted the binding of transition metals. The metal ions are capable of interacting selectively with nucleophilic compounds including olefins. The transition metal ions used for sorbent modification were copper(II) chlorides and nickel(II) chlorides. The supported adsorbent was immobilised by bonding the modified silica to the column walls by a film of poly(dimethylsiloxane). Due to this strategy, the coating was very stable, no sorbent loss from the column was observed and the column could be washed with solvent. Besides exemplary separations, the paper quotes retention parameters of the group of compounds analysed and presents a discussion of the specific interactions between the adsorbate and adsorbent.

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QSRR: Quantitative Structure-(Chromatographic) Retention Relationships

Cited by 437 publications

References 344 publications

Application of solvation model to prediction of the solute retention in liquid chromatography over a wide range of mobile-phase compositions

Application of solvation model to prediction of the solute retention in liquid chromatography over a wide range of mobile-phase compositions

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

Quantitative relationship and application of 3‐benzylketoimine metal dichlorides in the analysis of volatile hydrocarbons

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