PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A probabilistic approach to classifying metabolic stability
Anton Schwaighofer, Timon Schroeter, Sebastian Mika, Katja Hansen, Antonius ter Laak, Philip Lienau, Andreas Reichel, Nikolaus Heinrich and Klaus-Robert Müller
Journal of Chemical Information and Modelling Volume 48, Number 4, pp. 785-796, 2008. ISSN 1549-9596


Metabolic stability is an important property of drug molecules that shouldoptimallybe 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.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:5003
Deposited By:Timon Schröter
Deposited On:24 March 2009