Estimation
of 13C-NMR chemical shift values using
neural network technology
Vladimir Purtuc, Veronika Schütz,
Susanne Felsinger und
Wolfgang Robien
Neural networks represent an attractive tool for the prediction of physicochemical
properties. We have focused our interest in the development of a general
network allowing the prediction of C13-NMR chemical shift values for all
classes of organic compounds.
The development of a neural network consists of several steps:
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Development of an appropriate model to describe the property under investigation
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Selection of molecular descriptors
-
Selection of the network topology
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Optimization of the network parameters and the network topology using a
training set of data
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Testing of the network against another data set in order to get quality
parameters
The data used during training and evaluation of the network are selected
from the CSEARCH-NMR database holding some 230,000 carbon NMR spectra with
a total number of 2,700,000 assigned chemical shift values. The severe
restriction during the training, even when using the Alpha-Cluster, is
based on the fact that the networks are comparably large depending on the
number of molecular descriptiors selected, leading to a large number of
weights to be optimized. Therefore a typical training set consists of only
400,000 examples selected on a random basis. The optimization of such a
large network is an extremely time- and memory-consuming task, but the
resulting parameter file has only a size of roughly 0.5MB holding the condensed
information extracted from 230,000 carbon NMR spectra allowing a very fast
and precise prediction of C-13 chemical shift values even at the PC-level.
The large advantage of our network design is the utilization of stereochemical
information which further improves the quality of the prediction. The evaluation
of stereochemical interactions is based on a technology with no need for
3-dimensional coordinates.
For a detailed description how to utilize this neural network for spectrum
prediction using WEB-technology click
here