Use of quantitative structure–enantioselective retention relationship for the liquid chromatography chiral separation prediction of the series of pyrrolidin‐2‐one compounds

Abstract

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.

State-of-the-art and recent developments of immobilized polysaccharide-based chiral stationary phases for enantioseparations by high-performance liquid chromatography (2013–2017)

“…Quantum chemical calculations of a set of chiral pyrrolidin-2-one derivatives were used to predict structureenantioselective retention relationships for amylose tris- (3,5-dimethylphenylcarbamate) as chiral stationary phase in the normal phase mode [134]. Although direct solute-selector interactions were not modeled, the polar surface area of the solutes as well as a significant role of charge-transfer interactions between the…”

Modelling the enantioresolution capability of cellulose tris(3,5-dichlorophenylcarbamate) stationary phase in reversed phase conditions for neutral and basic chiral compounds

“…Different QSPR studies have been reported for modelling data in enantioselective chromatography using polysaccharides-based stationary phases, some of them are presented/reviewed in the paper by Del Rio [11]. In these QSPR models, enantioresolution-related information (retention or selectivity values) is correlated with different molecular properties of compounds through linear free energy relationships (LFERs) studies [13][14][15][16], linear solvation energy relationships (LSERs) [17], and 3D-QSPR properties employing comparative molecular field analysis (CoMFA) [18].…”

“…Multiple linear regression (MLR) [15][16][17], artificial neural networks [18], and genetic algorithm [16,18] are used as chemometric techniques.…”

Global chemical reactivity parameters for several chiral beta-blockers from Density Functional Theory Viewpoint

“…Stereochemical features of a molecule can have an influence on its stability and reactivity to other chiral molecules. In 2012, five quantum chemical descriptors were applied to pyrrolidin-2-one compounds: HLG (gap between E HOMO and E LUMO ), hardness (η), softness (σ) and electronegativity (χ), total energy (E total ), for the prediction of retention factors [7]. Based on Koopmans’ theorem, the chemical potential χ m and chemical hardness η can be calculated as: η= +0.5(E LUMO − E HOMO ) and χ m = −0.5(E HOMO + E LUMO ).…”