title: A probabilistic approach to classifying metabolic stability.
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.
authors.affiliation: Fraunhofer FIRST, Kekuléstrasse 7, 12489 Berlin, Germany. anton@first.fraunhofer.de
authors.forename: Anton
authors.initials: A
authors.lastname: Schwaighofer
authors.forename: Timon
authors.initials: T
authors.lastname: Schroeter
authors.forename: Sebastian
authors.initials: S
authors.lastname: Mika
authors.forename: Katja
authors.initials: K
authors.lastname: Hansen
authors.forename: Antonius
authors.initials: A
authors.lastname: Ter Laak
authors.forename: Philip
authors.initials: P
authors.lastname: Lienau
authors.forename: Andreas
authors.initials: A
authors.lastname: Reichel
authors.forename: Nikolaus
authors.initials: N
authors.lastname: Heinrich
authors.forename: Klaus-Robert
authors.initials: KR
authors.lastname: Müller
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D015195:Drug Design; D011336:Probability