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Synergistic Combination of CASE Algorithms and DFT Chemical Shift Predictions: A Powerful Approach for Structure Elucidation, Verification, and Revision

Author:
Buevich, Alexei V., Elyashberg, Mikhail E.
Source:
Journal of natural products 2016 v.79 no.12 pp. 3105-3116
ISSN:
1520-6025
Subject:
algorithms, expert systems, nuclear magnetic resonance spectroscopy, organic compounds, prediction
Abstract:
Structure elucidation of complex natural products and new organic compounds remains a challenging problem. To support this endeavor, CASE (computer-assisted structure elucidation) expert systems were developed. These systems are capable of generating a set of all possible structures consistent with an ensemble of 2D NMR data followed by selection of the most probable structure on the basis of empirical NMR chemical shift prediction. However, in some cases, empirical chemical shift prediction is incapable of distinguishing the correct structure. Herein, we demonstrate for the first time that the combination of CASE and density functional theory (DFT) methods for NMR chemical shift prediction allows the determination of the correct structure even in difficult situations. An expert system, ACD/Structure Elucidator, was used for the CASE analysis. This approach has been tested on three challenging natural products: aquatolide, coniothyrione, and chiral epoxyroussoenone. This work has demonstrated that the proposed synergistic approach is an unbiased, reliable, and very efficient structure verification and de novo structure elucidation method that can be applied to difficult structural problems when other experimental methods would be difficult or impossible to use.
Agid:
5606413