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Molecules 2016, 21(10), 1297;

Mining Chromatographic Enantioseparation Data Using Matched Molecular Pair Analysis

Department of Structural Chemistry, Merck Research Laboratories, Rahway, NJ 07065, USA
Aix Marseille Université, Centrale Marseille, CNRS, iSm2, 13397 Marseille CEDEX 20, France
Department of Chemistry, University of Illinois, Urbana, IL 61801, USA
Department of Process Research & Development, Merck Research Laboratories, Rahway, NJ 07055, USA
Authors to whom correspondence should be addressed.
Academic Editor: Yoshio Okamoto
Received: 13 July 2016 / Revised: 14 September 2016 / Accepted: 16 September 2016 / Published: 29 September 2016
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We apply matched molecular pair (MMP) analysis to data from ChirBase, which contains literature reports of chromatographic enantioseparations. For the 19 chiral stationary phases we examined, we were able to identify 289 sets of pairs where there is a statistically significant and consistent difference in enantioseparation due to a small chemical change. In many cases these changes highlight enantioselectivity differences between pairs or small families of closely related molecules that have for many years been used to probe the mechanisms of chromatographic chiral recognition; for example, the comparison of N-H vs. N-Me analytes to determine the criticality of an N-H hydrogen bond in chiral molecular recognition. In other cases, statistically significant MMPs surfaced by the analysis are less familiar or somewhat puzzling, sparking a need to generate and test hypotheses to more fully understand. Consequently, mining of appropriate datasets using MMP analysis provides an important new approach for studying and understanding the process of chromatographic enantioseparation. View Full-Text
Keywords: matched molecular pairs; chiral chromatography; chiral recognition matched molecular pairs; chiral chromatography; chiral recognition

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Sheridan, R.P.; Piras, P.; Sherer, E.C.; Roussel, C.; Pirkle, W.H.; Welch, C.J. Mining Chromatographic Enantioseparation Data Using Matched Molecular Pair Analysis. Molecules 2016, 21, 1297.

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