The screening of a number of chiral stationary phases (CSPs) with different modifiers in supercritical fluid chromatography to find a chromatographic method for separation of enantiomers can be time-consuming. Computational methods for data analysis were utilized to establish a hierarchical screening strategy, using a dataset of 110 drug-like chiral compounds with diverse structures tested on 15 CSPs with two different modifiers. This dataset was analyzed using a combinatorial algorithm, principal component analysis (PCA), and a correlation matrix. The primary goal was to find a set of eight columns resolving a large number of compounds, but also having complementary enantioselective properties. In addition to the hereby defined hierarchical experimental strategy, quantitative structure enantioselective models (QSERs) were evaluated. The diverse chemical space and relatively limited size of the training set reduced the accuracy of the QSERs. However, including separation factors from other CSPs increased the accuracies of the QSERs substantially. Hence, such combined models can support the experimental strategy in prioritizing the CSPs of the second screening phase, when a compound is not separated by the primary set of columns.
Keywords: chiral stationary phase; enantiomer separation; machine learning; principal component analysis; quantitative structure enantioselective models; supercritical fluid chromatography.
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