ArticleChirality2025
Molecular Docking Observations on Enantiomeric Retention Trends and Selection of Chiral Stationary Phase.
Article in Chirality, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
5 authors.
Funding
Abstract
This study explores molecular docking as a predictive tool for enantiomeric separations in supercritical fluid chromatography, focusing on the binding mechanisms of chiral stationary phases. The enantiomeric separation of five chiral molecules and thalidomide was systematically evaluated using six polysaccharide-based chiral stationary phases in supercritical fluid chromatography. The influence of chiral selector type (amylose vs. cellulose), chlorination, and mobile phase composition on enantioseparation was assessed. Maximum resolution values for compounds 1-5 ranged from 1.40 to 8.90, while thalidomide achieved a resolution of up to 11.45. Retention factors (k) varied between 2.85 and 12.38, depending on CSP interactions. To gain molecular-level insights into enantioselective recognition, docking simulations using AutoDock Vina were performed, predicting binding affinities between -7.85 and -6.40. These predictions were systematically compared with experimental results to assess docking's reliability in capturing key retention descriptors. The study further characterized the absolute configurations of semipreparatively isolated enantiomers through X-ray crystallography and optical rotation measurements, confirming stereochemistry and validating enantiomeric purity. By bridging computational modeling with experimental workflows, this study demonstrates the potential of molecular docking to support chromatographic method development, reducing reliance on trial-and-error optimization.
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