2004
DOI: 10.1002/mrc.1424
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Novel methods of automated structure elucidation based on 13C NMR spectroscopy

Abstract: Three new approaches for automated structure elucidations of organic molecules using NMR spectroscopic data were introduced recently. These approaches apply a neural network 13C NMR chemical shift prediction method to rank the results of structure generators by their agreement of the predicted and experimental chemical shifts. These three existing implementations are compared using realistic example molecules. The applicability and reliability of such approaches is addressed.

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Cited by 18 publications
(10 citation statements)
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“…They compared the performance of this neural network method to that of the commonly used approaches utilizing additivity rules, fragment-based database searches, and quantum chemical calculations. In their article, Meiler and Köck [251] compare these three existing implementations using real molecules (a thiourea derivative, tryptophan, and prianosin D acetate) as examples. The results were comparable with those obtained using a five-sphere HOSE code prediction relying on the SPECINFO database.…”
Section: Computer-assisted Analysismentioning
confidence: 99%
“…They compared the performance of this neural network method to that of the commonly used approaches utilizing additivity rules, fragment-based database searches, and quantum chemical calculations. In their article, Meiler and Köck [251] compare these three existing implementations using real molecules (a thiourea derivative, tryptophan, and prianosin D acetate) as examples. The results were comparable with those obtained using a five-sphere HOSE code prediction relying on the SPECINFO database.…”
Section: Computer-assisted Analysismentioning
confidence: 99%
“…For the small number of interesting constitutions a back-calculation on the carbon chemical shifts was made (ChemDraw v11), that were compared to the experimental values (see table 2). The last line in the table contains the sum of the absolute chemical shift differences for all carbons, exposing molecule 6 as the one that best fits the experimental data [24,27,28]. …”
Section: Resultsmentioning
confidence: 99%
“…Since the solutions will then have to be checked manually for their chemical feasibility and sense, Different efforts have been made to reduce the number solutions. Among others, ranking of the constitutional assignments by chemical shift deviation and/or substructural elements have been tested [25,26] integrated to C OCON runs. Unfortunately, the described software could not be made available for the online version of C OCON ( WEB C OCON at http://cocon.nmr.de), since it uses data protected by Intellectual Property.…”
Section: Resultsmentioning
confidence: 99%