A Probabilistic approach to classifying metabolic stability
A Probabilistic approach to classifying metabolic stability
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DOI：
10.1021/ci700142c
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发表时间：
2008-04-01
影响因子：
5.6
通讯作者：
Mueller, Klaus-Robert
中科院分区：
文献类型：
--
作者：
Schwaighofer, Anton;Schroeter, Timon;Mueller, Klaus-Robert
代谢稳定性是药物分子的一个重要特性，在药物设计过程中应该尽早考虑到这一点。随着许多中通量或高通量分析在早期药物发现中实施，这种特性的预测工具可能具有很高的价值。然而，代谢稳定性本身就难以预测，并且没有商业工具可用于此目的。在这项工作中，我们提出了一种机器学习方法来预测代谢稳定性，该方法是针对拜耳先灵制药药物开发过程中的化合物量身定制的。对于四种不同的体外测定，我们开发了贝叶斯分类模型来预测化合物代谢稳定的概率。所选择的方法隐含地考虑了“适用领域”。开发的模型在拜耳先灵制药最近的项目数据中得到验证，表明预测高度准确，适用范围估计正确。此外，我们在一组公开可用的数据上评估建模方法。
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.