title: Simultaneous prediction of aqueous solubility and octanol/water partition coefficient based on descriptors derived from molecular structure.
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.
authors.affiliation: ChemQuest, Sundown, Isle of Wight, UK. davel@chmqst.demon.co.uk
authors.forename: D J
authors.initials: DJ
authors.lastname: Livingstone
authors.forename: M G
authors.initials: MG
authors.lastname: Ford
authors.forename: J J
authors.initials: JJ
authors.lastname: Huuskonen
authors.forename: D W
authors.initials: DW
authors.lastname: Salt
mesh_terms: D020003:1-Octanol; D015195:Drug Design; D016014:Linear Models; D008956:Models, Chemical; D015394:Molecular Structure; D016571:Neural Networks, Computer; D010575:Pesticides / Q000737:chemistry; D012995:Solubility; D014867:Water
chemical_list: D010575:Pesticides; D014867:Water; D020003:1-Octanol