Modeling Aqueous Solubility

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...

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Bibliographic Details
Published in:Journal of Chemical Information and Computer Sciences 2003-05, Vol.43 (3), p.837-841
Main Authors: Butina, Darko, Gola, Joelle M. R
Format: Article
Language:English
Subjects:
Models, Chemical
Organic Chemicals - chemistry
Quantitative Structure-Activity Relationship
Solubility
Water - chemistry
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Summary: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.
ISSN:0095-2338
1549-960X
DOI:10.1021/ci020279y