Abstract：
:Metabolic stability is an important property of drug molecules that should-optimally-be taken into account early on in the drug design process. Along with numerous medium- or high-throughput assays being implemented in early drug discovery, a prediction tool for this property could be of high value. However, metabolic stability is inherently difficult to predict, and no commercial tools are available for this purpose. In this work, we present a machine learning approach to predicting metabolic stability that is tailored to compounds from the drug development process at Bayer Schering Pharma. For four different in vitro assays, we develop Bayesian classification models to predict the probability of a compound being metabolically stable. The chosen approach implicitly takes the "domain of applicability" into account. The developed models were validated on recent project data at Bayer Schering Pharma, showing that the predictions are highly accurate and the domain of applicability is estimated correctly. Furthermore, we evaluate the modeling method on a set of publicly available data.
journal_name
J Chem Inf Modeljournal_title
Journal of chemical information and modelingauthors
Schwaighofer A,Schroeter T,Mika S,Hansen K,Ter Laak A,Lienau P,Reichel A,Heinrich N,Müller KRdoi
10.1021/ci700142csubject
Has Abstractpub_date
2008-04-01 00:00:00pages
785-96issue
4eissn
1549-9596issn
1549-960Xjournal_volume
48pub_type
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