A probabilistic approach to classifying metabolic stability

Anton Schwaighofer, Timon Schroeter, Sebastian Mika, Katja Hansen, Antonius Ter Laak, Philip Lienau, Andreas Reichel, Nikolaus Heinrich, Klaus Robert Müller

Research output: Contribution to journalArticlepeer-review

34 Citations (Scopus)


Metabolie 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.

Original languageEnglish
Pages (from-to)785-796
Number of pages12
JournalJournal of Chemical Information and Modeling
Issue number4
Publication statusPublished - 2008 Apr

ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)
  • Computer Science Applications
  • Library and Information Sciences


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