The prediction of carbon-13 NMR chemical shifts using ensembles of networks
Refereed conference paper presented and published in conference proceedings

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AbstractEnsembles of multi-layer network is set up to predict the carbon-13 nuclear magnetic resonance (C13 NMR) chemical shifts of a series of monosubstituted benzenes. The descriptors (inputs) used are twelve structural-based vectors that correspond to the calculated Huckel and Gasteiger electron densities of the mono-substituted aromatic systems and four graphical descriptors that correspond to the numbers of appearance of some specific structural features of the substitutents, The outputs are the C13 NMR chemical shifts of the ipso, ortho, meta, and para carbons. A.draining set of 38 data was used and, after training, the neural network was tested for its ability to predict the C13 NMR chemical shifts of 15 compounds not included in the Graining set. In this paper, we demonstrated that the performance of artificial neural networks in C13 NMR chemical shift prediction could be improved by (a) using both structural-based and graphical descriptors as input parameters. (b) pruning. (c) combining the prediction from a number of networks. Furthermore, pruning the connection weights can also enable us to select the appropriate input variables.
All Author(s) ListChan LW, Chow HF
Name of Conference2nd IEEE World Congress on Computational Intelligence (WCCI 98)
Start Date of Conference04/05/1998
End Date of Conference09/05/1998
Place of ConferenceANCHORAGE
Country/Region of ConferenceUnited States of America
Pages96 - 100
LanguagesEnglish-United Kingdom
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Engineering; Engineering, Biomedical; Engineering, Electrical & Electronic; Medical Informatics; Neurosciences; Neurosciences & Neurology

Last updated on 2021-27-09 at 06:01