Review on modelling aspects in reversed-phase liquid chromatographic quantitative structure-retention relationships.
In the literature an increasing interest in quantitative structure-retention relationships (QSRR) can be observed. After a short introduction on QSRR and other strategies proposed to deal with the starting point selection problem prior to method development in reversed-phase liquid chromatography, a number of interesting papers is reviewed, dealing with QSRR models for reversed-phase liquid chromatography. The main focus in this review paper is put on the different modelling methodologies applied and the molecular descriptors used in the QSRR approaches. Besides two semi-quantitative approaches (i.e. principal component analysis, and decision trees), these methodologies include artificial neural networks, partial least squares, uninformative variable elimination partial least squares, stochastic gradient boosting for tree-based models, random forests, genetic algorithms, multivariate adaptive regression splines, and two-step multivariate adaptive regression splines.
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FABI, Department of Analytical Chemistry and Pharmaceutical Technology, Pharmaceutical Institute, Vrije Universiteit Brussel, Laarbeeklaan 103, B-1090 Brussels, Belgium.
Vander Heyden
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D002853:Chromatography, Liquid / Q000379:methods*; D006801:Humans; D016014:Linear Models; D008956:Models, Chemical; D016571:Neural Networks, Computer; D021281:Quantitative Structure-Activity Relationship; D012984:Software