Fast determination of 13C NMR chemical shifts using artificial neural networks.

Meiler J, Meusinger R, Will M
J Chem Inf Comput Sci. 2000 40 (5): 1169-76

PMID: 11045810

Nine different artificial neural networks were trained with the spherically encoded chemical environments of more than 500000 carbon atoms to predict their 13C NMR chemical shifts. Based on these results the PC-program "C_shift" was developed which allows the calculation of the 13C NMR spectra of any proposed molecular structure consisting of the covalently bonded elements C, H, N, O, P, S and the halogens. Results were obtained with a mean deviation as low as 1.8 ppm; this accuracy is equivalent to a determination on the basis of a large database but, in a time as short as known from increment calculations, was demonstrated exemplary using the natural agent epothilone A. The artificial neural networks allow simultaneously a precise and fast prediction of a large number of 13C NMR spectra, as needed for high throughput NMR and screening of a substance or spectra libraries.

MeSH Terms (3)

Magnetic Resonance Spectroscopy Models, Molecular Neural Networks (Computer)

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