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Internet Electronic Journal of Molecular Design - IEJMD, ISSN 1538-6414, CODEN IEJMAT
| ABSTRACT - Internet Electron. J. Mol. Des. February 2002, Volume 1, Number 2, 80-93 |
Neural Network Modeling of Melting Temperatures for Sulfur-Containing Organic
Compounds
Julian Koziol
Internet Electron. J. Mol. Des. 2002, 1, 80-93
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Abstract:
Searching for a comprehensive numerical description of the
chemical structure and for methods that enable to develop
effective and credible QSPR (quantitative structureproperty
relationships) models represent significant challenges for the
contemporary theoretical chemistry. Among these methods
artificial neural networks (ANN) appears to be very promising in
obtaining models that convert structural features into different
properties of chemical compounds. Two different models
relating structural descriptors to melting temperatures of sulfur
containing organic compounds are developed using ANN. A new
set of molecular descriptors is evaluated to determine their
suitability for QSPR studies. Using two data sets containing 150
sulfides and 226 sulfones, ANN trained with the back
propagation and conjugated gradient algorithms are able to
predict the melting temperatures with good accuracy. The results
obtained show a good predictive ability for the ANN models,
giving R2cv equal to 0.880 and 0.794 for the sulfides and
sulfones, respectively. The QSPR studies described in this paper
provide strong evidence that the tested structural descriptors are
useful and effective for the ANN modeling of the melting
temperatures of sulfides and sulfones.
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