title: Modeling aqueous solubility.
abstract: This paper describes the development of an aqueous solubility model based on solubility data from the Syracuse database, calculated octanol-water partition coefficient, and 51 2D molecular descriptors. Two different statistical packages, SIMCA and Cubist, were used and the results were compared. The Cubist model, which comprises a collection of rules, each of which has an associated Multiple Linear Regression model (MLR), gave better overall results on a test set of 640 compounds with an overall squared correlation coefficient of 0.74 and an absolute average error of 0.68 log units. Both training and independent test sets had similar distributions of structures in terms of the different functionalities present-60% neutral, 14% acidic, 8% phenolic, 11% monobasic, 4% polybasic, and 3% zwitterionic molecules. Sets were designed by random selection, with 2688 (81%) and 640 (19%) molecules, respectively, forming the training and the test sets.
authors.affiliation: Computational Chemistry and Chemoinformatics, ArQule (UK) Limited, Science Park, Cambridge, UK. darko_Butina@hotmail.com
authors.forename: Darko
authors.initials: D
authors.lastname: Butina
authors.forename: Joelle M R
authors.initials: JM
authors.lastname: Gola
mesh_terms: D008956:Models, Chemical; D009930:Organic Chemicals / Q000737:chemistry*; D021281:Quantitative Structure-Activity Relationship; D012995:Solubility; D014867:Water / Q000737:chemistry*
chemical_list: D009930:Organic Chemicals; D014867:Water