In this work, the enantioseparation of 15 structurally similar chiral solutes is studied, and analysis of the retention factors is performed using a genetic algorithm and multiple linear regression analysis technique. The present quantitative structure-enantioselective retention relationship model generated for retention factors' data has confirmed the importance of a number of descriptors altering the retention behavior and enantioselectivity of the studied compounds. Thus, fragment-based descriptor PSA, which encodes polar surface area, has confirmed the crucial role of heteroatoms in the retention behavior exhibited by pyrroliddin-2-ones. The presence of E(LUMO) descriptor, which represents a quantum-chemical property, has indicated the role of charge transfer interactions between the chiral stationary phase and enantiomers to retention factors, showing that lowest unoccupied molecular orbital energy is significantly different between enantiomers. The developed model exhibits a very good performance characterized by following statistical parameters: r(2) = 0.93 for training set and r(2) = 0.99 for the validation set. The selected three-variable model displays high predictive ability, catching the main factors affecting the enantioselectivity of studied chiral compounds, and therefore can be used for prediction of retention factors of other chiral compounds of similar structure to improve the separation process and so on.
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