Abstract：
:The dissolution of a chemical into water is a process fundamental to both chemistry and biology. The persistence of a chemical within the environment and the effects of a chemical within the body are dependent primarily upon aqueous solubility. With the well-documented limitations hindering the accurate experimental determination of aqueous solubility, the utilization of predictive methods have been widely investigated and employed. The setting of a solubility challenge by this journal proved an excellent opportunity to explore several different modeling methods, utilizing a supplied dataset of high-quality aqueous solubility measurements. Four contrasting approaches (simple linear regression, artificial neural networks, category formation, and available in silico models) were utilized within our laboratory and the quality of these predictions was assessed. These were chosen to span the multitude of modeling methods now in use, while also allowing for the evaluation of existing commercial solubility models. The conclusions of this study were surprising, in that a simple linear regression approach proved to be superior over more complex modeling methods. Possible explanations for this observation are discussed and also recommendations are made for future solubility prediction.
journal_name
J Chem Inf Modeljournal_title
Journal of chemical information and modelingauthors
Hewitt M,Cronin MT,Enoch SJ,Madden JC,Roberts DW,Dearden JCdoi
10.1021/ci900286ssubject
Has Abstractpub_date
2009-11-01 00:00:00pages
2572-87issue
11eissn
1549-9596issn
1549-960Xjournal_volume
49pub_type
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