A systematic approach for the generation and verification of structural hypotheses

• Published in: Magnetic resonance in chemistry Vol. 47; no. 5; pp. 371 - 389
• Main Authors: Elyashberg, Mikhail, Blinov, Kirill, Williams, Antony
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01.05.2009
• Subjects: 13C; 15N; expert system; GIAO; increments; neural nets; NMR
• ISSN: 0749-1581; 1097-458X; 1097-458X
• DOI: 10.1002/mrc.2397

During the process of molecular structure elucidation the selection of the most probable structural hypothesis may be based on chemical shift prediction. The prediction is carried out using either empirical or quantum‐mechanical (QM) methods. When QM methods are used, NMR prediction commonly utilizes the GIAO option of the DFT approximation. In this approach the structural hypotheses are expected to be investigated by scientist. In this article we hope to show that the most rational manner by which to create structural hypotheses is actually by the application of an expert system capable of deducing all potential structures consistent with the experimental spectral data and specifically using 2D NMR data. When an expert system is used the best structure(s) can be distinguished using chemical shift prediction, which is best performed either by an incremental or neural net algorithm. The time‐consuming QM calculations can then be applied, if necessary, to one or more of the ‘best’ structures to confirm the suggested solution. Copyright © 2009 John Wiley & Sons, Ltd. In this article we show that the most rational manner by which to create structural hypotheses is by the application of an expert system capable of deducing all potential structures consistent with the experimental spectral data. Empirical or quantum‐mechanical (QM) NMR prediction methods are compared. It is shown that when an expert system is used the best structure(s) can be distinguished using either incremental or neural net (NN)‐based NMR prediction algorithms.