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A systematic approach for the generation and verification of structural hypotheses
Author(s) -
Elyashberg Mikhail,
Blinov Kirill,
Williams Antony
Publication year - 2009
Publication title -
magnetic resonance in chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.483
H-Index - 72
eISSN - 1097-458X
pISSN - 0749-1581
DOI - 10.1002/mrc.2397
Subject(s) - chemistry , process (computing) , quantum chemical , experimental data , artificial neural network , selection (genetic algorithm) , artificial intelligence , biological system , algorithm , computational chemistry , machine learning , computer science , molecule , statistics , mathematics , organic chemistry , operating system , biology
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.