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

• Published in: Journal of computer-aided molecular design Vol. 15; no. 8; p. 741
• Main Authors: Livingstone, D J, Ford, M G, Huuskonen, J J, Salt, D W
• Format: Journal Article
• Language: English
• Published: Netherlands Springer Nature B.V 01.08.2001
• Subjects: 1-Octanol; Drug Design; Linear Models; Models, Chemical; Molecular Structure; Neural networks; Neural Networks (Computer); Pesticides - chemistry; Solubility; Studies; Water
• ISSN: 0920-654X; 1573-4951
• DOI: 10.1023/A:1012284411691

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.