Prediction of carbon-13 nuclear magnetic resonance chemical shifts by artificial neural networks

Prediction of carbon-13 nuclear magnetic resonance chemical shifts by artificial neural networks

Empirical models relating atom-based structural descriptors to 13C NMR chemical shifts have been used to accurately simulate 13C NMR spectra for compounds whose shifts are unknown. An investigation of neural networks as a supplement to regression analysis in linking structural descriptors to chemica...

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Bibliographic Details
Published in:Analytical chemistry (Washington) Vol. 64; no. 10; pp. 1157 - 1164
Main Authors: Anker, Lawrence S, Jurs, Peter C
Format: Journal Article
Language:English
Published: Washington American Chemical Society 15-05-1992
Subjects:
Atoms & subatomic particles
Chemicals
NMR
Nuclear magnetic resonance
Online Access:Get full text
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Summary:Empirical models relating atom-based structural descriptors to 13C NMR chemical shifts have been used to accurately simulate 13C NMR spectra for compounds whose shifts are unknown. An investigation of neural networks as a supplement to regression analysis in linking structural descriptors to chemical shifts is presented.
Bibliography:ark:/67375/TPS-8QRLWVHP-X
istex:32A256CADB0B7D4994A337E5D0DE1416FFC988AC
ISSN:0003-2700
1520-6882
DOI:10.1021/ac00034a015