Journal of Computer-Aided Molecular Design

, Volume 15, Issue 8, pp 741–752 | Cite as

Simultaneous prediction of aqueous solubility and octanol/water partition coefficient based on descriptors derived from molecular structure

  • David J. Livingstone
  • Martyn G. Ford
  • Jarmo J. Huuskonen
  • David W. Salt


It has been shown that water solubility and octanol/water partition coefficient for a large diverse set of compounds can be predicted simultaneously using molecular descriptors derived solely from a two dimensional representation of molecular structure. These properties have been modelled using multiple linear regression, artificial neural networks and a statistical method known as canonical correlation analysis. The neural networks give slightly better models both in terms of fitting and prediction presumably due to the fact that they include non-linear terms. The statistical methods, on the other hand, provide information concerning the explanation of variance and allow easy interrogation of the models. Models were fitted using a training set of 552 compounds, a validation set and test set each containing 68 molecules and two separate literature test sets for solubility and partition.

canonical correlation electrotopological descriptors log P log S neural networks regression analysis 


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Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • David J. Livingstone
    • 1
  • Martyn G. Ford
    • 2
  • Jarmo J. Huuskonen
    • 3
  • David W. Salt
    • 4
  1. 1.ChemQuestSandown, Isle of WightUK
  2. 2.Centre for Molecular DesignUniversity of PortsmouthPortsmouth, HantsUK
  3. 3.Division of Pharmaceutical Chemistry, Department of PharmacyUniversity of HelsinkiFIN-00014
  4. 4.School of Computer Science & MathematicsUniversity of PortsmouthPortsmouth, HantsUK

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