I am a week behind on submitting a Chapter for publication in the Second Edition of the Encyclopedia of Spectroscopy. Too much work…not enough time. In the meantime I’ve co-authored a couple of publications with friends from ACD/Labs…one of these addresses an issue discussed on the ChemSpider Blog…a longstanding wish to compare empirical and quantum-mechanical NMR prediction approaches.
A Systematic Approach for the Generation and Verification of Structural Hypotheses.
During the process of molecular structure elucidation, selection of the most probable structural hypothesis may be based on chemical shift prediction. The prediction is carried out either by 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 the 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 using either incremental or neural net algorithm. The time-consuming quantum-mechanical calculations can then be applied, if necessary, to one or more of the “best” structures to confirm the suggested solution.
The Application of Empirical Methods of NMR Chemical Shift Prediction to Determine Relative Stereochemistry.
The reliable determination of stereostructures contained within chemical structures usually requires utilization of NMR data, chemical derivatization, molecular modeling, quantum-mechanical calculations and, if available, X-ray analysis. In this article we show that the number of stereoisomers which need to be thoroughly verified can be significantly reduced by the application of NMR chemical shift calculation to the full stereoisomer set of possibilities using a fragmental approach based on HOSE codes. The usefulness of suggested method is illustrated using experimental data published for artarborol.