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Internet Electronic Journal of Molecular Design - IEJMD, ISSN 1538-6414, CODEN IEJMAT
| ABSTRACT - Internet Electron. J. Mol. Des. January 2003, Volume 2, Number 1, 33-49 |
Semiempirical Topological Index: A Novel Molecular Descriptor for
Quantitative Structure-Retention Relationship Studies
Berenice da Silva Junkes, Renata Dias de Mello Castanho Amboni, Rosendo Augusto Yunes, and Vilma Edite Fonseca Heinzen
Internet Electron. J. Mol. Des. 2003, 2, 33-49
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Abstract:
An important property that has been extensively studied in QSPR is
the chromatographic retention. Based on new considerations about the
chromatographic behavior and experimental data, our group has
developed a new topological index designed semi-empirical
topological index, IET. The main goal of the present paper is to
generalize the semi-empirical topological index, verifying the
predictive-ability of the chromatographic retention for a diverse set of
organic compounds (alkanes, alkenes, esters, ketones, aldehydes, and
alcohols) and to obtain a general QSRR model. QSRR may be used as
an important complementary tool for the elucidation of the molecular
structure or for the prediction of the chromatographic retention. This
index is based on the hypothesis that the chromatographic retention is
due to the interaction of each atom of the molecule with the stationary
phase, and consequently the value of the index is reduced by steric
effects from its neighbors. Considering that the complexity involved in
the solute-stationary phase interactions cannot be estimated only by
theoretical considerations, values were attributed to the atoms of the
molecules from the experimental chromatographic retention and
theoretical deductions. The simple linear regression between the
chromatographic retention and the index proposed, for all 548 organic
compounds, is extremely satisfactory (correlation coefficient, r =
1.0000, standard deviation, SD = 7.01, and leave-one-out cross-
validation correlation coefficient, r2CV = 0.999).
The predictive quality
of the QSRR was tested for an external prediction set of 182
compounds randomly chosen from 548 compounds (r = 1.0000 and
SD = 7.65).
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