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化学文库设计与高通量筛选命中验证中的计算毒理学方法

Computational Toxicology Methods in Chemical Library Design and High-Throughput Screening Hit Validation.

作者信息

Hevener Kirk E

机构信息

Department of Pharmaceutical Sciences, University of Tennessee Health Science Center, Memphis, TN, USA.

出版信息

Methods Mol Biol. 2018;1800:275-285. doi: 10.1007/978-1-4939-7899-1_13.

Abstract

The discovery of molecular toxicity in a clinical drug candidate can have a significant impact on both the cost and timeline of the drug discovery process. Early identification of potentially toxic compounds during screening library preparation or, alternatively, during the hit validation process, is critical to ensure that valuable time and resources are not spent pursuing compounds that may possess a high propensity for human toxicity. This chapter focuses on the application of computational molecular filters, applied either prescreening or postscreening, to identify and remove known reactive and/or potentially toxic compounds from consideration in drug discovery campaigns.

摘要

临床候选药物中分子毒性的发现可能会对药物研发过程的成本和时间线产生重大影响。在筛选文库制备过程中,或者在活性验证过程中尽早识别潜在的有毒化合物,对于确保不将宝贵的时间和资源浪费在可能具有高人体毒性倾向的化合物上至关重要。本章重点介绍计算分子过滤器的应用,该过滤器可在预筛选或后筛选时使用,以识别和排除已知的反应性和/或潜在有毒化合物,使其不被纳入药物研发项目的考虑范围。

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本文引用的文献

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Deep learning for computational chemistry.用于计算化学的深度学习
J Comput Chem. 2017 Jun 15;38(16):1291-1307. doi: 10.1002/jcc.24764. Epub 2017 Mar 8.
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Deep Learning in Drug Discovery.药物研发中的深度学习
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QSAR Methods.定量构效关系方法
Methods Mol Biol. 2016;1425:1-20. doi: 10.1007/978-1-4939-3609-0_1.
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ZINC 15--Ligand Discovery for Everyone.锌15——面向大众的配体发现平台。
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