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Journal of Computer-Aided Molecular Design

, Volume 18, Issue 2, pp 75–87 | Cite as

Linear and nonlinear functions on modeling of aqueous solubility of organic compounds by two structure representation methods

  • Aixia Yan
  • Johann Gasteiger
  • Michael Krug
  • Soheila Anzali
Article

Abstract

Several quantitative models for the prediction of aqueous solubility of organic compounds were developed based on a diverse dataset with 2084 compounds by using multi-linear regression analysis and backpropagation neural networks. The compounds were described by two different structure representation methods: (1) with 18 topological descriptors; and (2) with 32 radial distribution function codes representing the 3D structure of a molecule and eight additional descriptors. The dataset was divided into a training and a test set based on Kohonen's self-organizing neural network. Good prediction results were obtained for backpropagation neural network models: with 18 topological descriptors, for the 936 compounds in the test set, a correlation coefficient of 0.92, and a standard deviation of 0.62 were achieved; with 3D descriptors, for the 866 compounds in the test set, a correlation coefficient of 0.90, and a standard deviation of 0.73 were achieved. The models were also tested by using another dataset, and the relationship of the two datasets was examined by Kohonen's self-organizing neural network.

Abbreviations: BPG – backpropagation; KNN – Kohonen's self-organizing neural network; MLRA – multilinear regression analysis; MMP – mean molecular polarizability; RDF – radial distribution function.

backpropagation Kohonen's self-organizing neural network multi-linear regression neural network radial distribution function solubility 

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Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Aixia Yan
    • 1
  • Johann Gasteiger
    • 1
    • 2
  • Michael Krug
    • 2
  • Soheila Anzali
    • 2
  1. 1.Computer-Chemie-Centrum and Institut für Organische ChemieUniversität Erlangen-NürnbergErlangenGermany
  2. 2.Merck KGaA, Global Technology BCIDarmstadtGermany

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