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

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Abstract

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.

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Livingstone, D.J., Ford, M.G., Huuskonen, J.J. et al. Simultaneous prediction of aqueous solubility and octanol/water partition coefficient based on descriptors derived from molecular structure. J Comput Aided Mol Des 15, 741–752 (2001). https://doi.org/10.1023/A:1012284411691

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