A Probabilistic Approach to Classifying Metabolic Stability
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- 作者：Anton Schwaighofer ; Timon Schroeter ; Sebastian Mika ; Katja Hansen ; Antonius ter Laak ; Philip Lienau ; Andreas Reichel ; Nikolaus Heinrich ; Klaus-Robert Mü ; ller
- 刊名：Journal of Chemical Information and Modeling
- 出版年：2008
- 出版时间：April 2008
- 年：2008
- 卷：48
- 期：4
- 页码：785 - 796
- 全文大小：322K
- 年卷期：v.48,no.4(April 2008)
- ISSN：1549-960X
文摘
Metabolic stability is an important property of drug molecules that shouldmg src="http://pubs.acs.org/images/entities/sbd_2.gif">optimallymg src="http://pubs.acs.org/images/entities/sbd_2.gif">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.