Fuzzy Structure Generation:  A New Efficient Tool for Computer-Aided Structure Elucidation (CASE)

• Published in: Journal of chemical information and modeling 2007-05, Vol.47 (3), p.1053-1066
• Main Authors: Elyashberg, Mikhail E, Blinov, Kirill A, Molodtsov, Sergey G, Williams, Antony J, Martin, Gary E
• Format: Article
• Language: English
• Subjects: Chemistry; Fuzzy logic; Information systems
• ISSN: 1549-9596; 1549-960X
• DOI: 10.1021/ci600528g

Contemporary Computer-Aided Structure Elucidation (CASE) systems are heavily based on the utilization of 2D NMR spectra. The utilization of HMBC/GHMBC and COSY/GCOSY correlations generally assumes that these correlations result from 2-3 J CH and 2-3 J HH spin−spin couplings, respectively, and consequently these values are used as the default setting in these systems. Our previous studies , have shown that about half of the problems studied actually contain some correlations of 4−6 bonds, so-called “nonstandard” correlations. In such cases the initial 2D NMR data are contradictory, and the correct solution is therefore not directly attainable. Unfortunately nonstandard correlations and the number of intervening bonds usually cannot be identified experimentally. In this work we suggest a new approach that we term Fuzzy Structure Generation. This allows the solution of structural problems whose 2D NMR data contain an unknown number of nonstandard correlations having different and unknown lengths. Suggested methods for the application of Fuzzy Structure Generation are described, and their application is illustrated by a series of real-world examples. We conclude that Fuzzy Structure Generation is efficient, and there is no real alternative at present in terms of a universal practical method for the structure elucidation of organic molecules from 2D NMR data.