Structure Elucidation from 2D NMR Spectra Using the StrucEluc Expert System:  Detection and Removal of Contradictions in the Data

The elucidation of chemical structures from 2D NMR data commonly utilizes a combination of COSY, HMQC/HSQC, and HMBC data. Generally COSY connectivities are assumed to mostly describe the separation of protons that are separated by 1 skeletal bond (3 J HH), while HMBC connectivities represent proton...

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Veröffentlicht in:Journal of Chemical Information and Computer Sciences 2004-09, Vol.44 (5), p.1737-1751
Hauptverfasser: Molodtsov, Sergey G, Elyashberg, Mikhail E, Blinov, Kirill A, Williams, Antony J, Martirosian, Eduard E, Martin, Gary E, Lefebvre, Brent
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Sprache:eng
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Zusammenfassung:The elucidation of chemical structures from 2D NMR data commonly utilizes a combination of COSY, HMQC/HSQC, and HMBC data. Generally COSY connectivities are assumed to mostly describe the separation of protons that are separated by 1 skeletal bond (3 J HH), while HMBC connectivities represent protons separated from carbon atoms by 1 to 2 skeletal bonds (2 J CH and 3 J CH). Obviously COSY and HMBC connectivities of lengths greater than those described have been detected. Though experimental techniques have recently been described to aid in the identification of the nature of the couplings the detection of whether a coupling is 2-bond or greater still remains a challenge in most laboratories. In the StrucEluc software system the common lengths of the connectivities, 1-bond for COSY and 1- or 2-bond for HMBC, derived from 2D NMR data are set as the default. Therefore, in the presence of any extended connectivities contradictions can appear in the 2D NMR data. In this article, algorithmic methods for the detection and removal of contradictions in 2D NMR data that have been developed in support of StrucEluc are described. The methods are based on the analysis of molecular connectivity diagrams, MCDs. These methods have been implemented in the StrucEluc system and tested by solving 50 structural problems with 2D NMR spectral data containing contradictions. The presence of contradictions was detected by the algorithm in 90% of the cases, and the contradictions were automatically removed in ∼50% of the problems. A method of “fuzzy” structure generation in the presence of contradictions has been suggested and successfully tested in this work. This work will demonstrate examples of the application of developed methods to a number of structural problems.
ISSN:0095-2338
1549-9596
1549-960X
DOI:10.1021/ci049956+