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计算机辅助药物代谢及毒性预测的工业应用

Industrial applications of in silico ADMET.

作者信息

Beck Bernd, Geppert Tim

机构信息

Department of Lead Identification and Optimization Support, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorferstrasse 65, 88397, Biberach an der Riss, Germany,

出版信息

J Mol Model. 2014 Jul;20(7):2322. doi: 10.1007/s00894-014-2322-5. Epub 2014 Jun 28.

Abstract

Quantitative structure activity relationship (QSAR) modeling has been in use for several decades now. One branch of it, in silico ADMET, became more and more important since the late 1990s as studies indicated that poor pharmacokinetics and toxicity were important causes of costly late-stage failures in drug development. In this paper we describe some of the available methods and best practice for the different stages of the in silico model building process. We also describe some more recent developments, like automated model building and the prediction probability. Finally we will discuss the use of in silico ADMET for "big data" and the importance and possible further development of interpretable models.

摘要

定量构效关系(QSAR)建模已应用数十年。自20世纪90年代末以来,其一个分支——计算机辅助的药物代谢及毒性预测(in silico ADMET)变得越来越重要,因为研究表明,药物代谢动力学不佳和毒性是药物开发后期成本高昂的失败的重要原因。在本文中,我们描述了计算机辅助模型构建过程不同阶段的一些可用方法和最佳实践。我们还描述了一些最新进展,如自动化模型构建和预测概率。最后,我们将讨论计算机辅助的药物代谢及毒性预测在“大数据”中的应用以及可解释模型的重要性和可能的进一步发展。

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