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Prediction of pKa values for aliphatic carboxylic acids and alcohols with empirical atomic charge descriptors.

Authors
  • Zhang, Jinhua
  • Kleinöder, Thomas
  • Gasteiger, Johann
Type
Published Article
Journal
Journal of chemical information and modeling
Publication Date
Jan 01, 2006
Volume
46
Issue
6
Pages
2256–2266
Identifiers
PMID: 17125168
Source
Medline
License
Unknown

Abstract

Two quantitative pKa prediction models for aliphatic carboxylic acids and for alcohols were developed by multiple linear-regression (MLR) analysis with empirical atomic descriptors. The acid and alcohol molecules were described by a set of five and four atomic descriptors, respectively. For the pKa model of 1122 aliphatic carboxylic acids, the squared correlation coefficient is 0.813 with a standard error of prediction of 0.423; for the pKa model of 288 alcohols, the squared correlation coefficient is 0.817 with a standard error of prediction of 0.755, respectively. The good predictive abilities of the models obtained were indicated by both cross-validation and by external validation. An atomic descriptor was developed to model the inductive effect of the neighboring atoms for a central atom in a molecule. The ability of the descriptor to measure the inductive effect of substituent groups was demonstrated by a good correlation of this descriptor with Taft sigma* constants in aliphatic carboxylic acids. It provides a new approach to estimate Taft sigma* constants directly from molecular structures. An algorithm using Kohonen neural networks for splitting a data set into a training set and a test set is also presented.

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