The impact of LR-HSQMBC very long-range heteronuclear correlation data on computer-assisted structure elucidation.

The impact of LR-HSQMBC very long-range (n)JCH heteronuclear shift correlation data as a supplement to HMBC data as input for the computer-assisted structure elucidation program, Structure Elucidator(®), is assessed for the first time. The severely proton-deficient xanthone antibiotic cervinomycin A2 and the alkaloid staurosporine were employed as a model compounds.

Applying computer-assisted structure elucidation algorithms for the purpose of structure validation: revisiting the NMR assignments of hexacyclinol.

Computer-assisted structure elucidation (CASE) using a combination of 1D and 2D NMR data has been available for a number of years. These algorithms can be considered as "logic machines" capable of deriving all plausible structures from a set of structural constraints or "axioms", defined by the spectroscopic data and associated chemical information or prior knowledge. CASE programs allow the spectroscopist not only to determine structures from spectroscopic data but also to study the dependence of the proposed structure on changes to the set of axioms. In this article, we describe the application of the ACD/Structure Elucidator expert system to help resolve the conflict between two different hypothetical hexacyclinol structures derived by different researchers from the NMR spectra of this complex natural product. It has been shown that the combination of algorithms for both structure elucidation and structure validation delivered by the expert system enables the identification of the most probable structure as well as the associated chemical shift assignments.

Performance validation of neural network based (13)c NMR prediction using a publicly available data source.

The validation of the performance of a neural network based 13C NMR prediction algorithm using a test set available from an open source publicly available database, NMRShiftDB, is described. The validation was performed using a version of the database containing ca. 214,000 chemical shifts as well as for two subsets of the database to compare performance when overlap with the training set is taken into account. The first subset contained ca. 93,000 chemical shifts that were absent from the ACD\CNMR DB, the "excluded shift set" used for training of the neural network and the ACD\CNMR prediction algorithm, while the second contained ca. 121,000 shifts that were present in the ACD\CNMR DB training set, the "included shift set". This work has shown that the mean error between experimental and predicted shifts for the entire database is 1.59 ppm, while the mean deviation for the subset with included shifts is 1.47 and 1.74 ppm for excluded shifts. Since similar work has been reported online for another algorithm we compared the results with the errors determined using Robien's CNMR Neural Network Predictor using the entire NMRShiftDB for program validation.

An expert system for automated structure elucidation utilizing 1H-1H, 13C-1H and 15N-1H 2D NMR correlations.

A software program for the automated structure elucidation of complex organic molecules using an expert system and utilizing 2D homo- and heteronuclear correlation 1H, 13C and 15N NMR spectroscopy is described. The methodology is illustrated on the basis of the automated structure determination of strychnine and some other examples.