Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität , Dahlmannstr. 2, D-53113 Bonn, Germany.
J Med Chem. 2017 May 11;60(9):3879-3886. doi: 10.1021/acs.jmedchem.7b00154. Epub 2017 Apr 25.
Undetected pan-assay interference compounds (PAINS) with false-positive activities in assays often propagate through medicinal chemistry programs and compromise their outcomes. Although a large number of PAINS have been classified, often on the basis of individual studies or chemical experience, little has been done so far to systematically assess their activity profiles. Herein we report a large-scale analysis of the behavior of PAINS in biological screening assays. More than 23 000 extensively tested compounds containing PAINS substructures were detected, and their hit rates were determined. Many consistently inactive compounds were identified. The hit frequency was low overall, with median values of two to five hits for PAINS tested in hundreds of assays. Only confined subsets of PAINS produced abundant hits. The same PAINS substructure was often found in consistently inactive and frequently active compounds, indicating that the structural context in which PAINS occur modulates their effects.
未被检测出的泛分析干扰化合物(PAINS)在检测中具有假阳性活性,这些化合物经常在药物化学项目中传播,并影响其结果。尽管已经对大量的 PAINS 进行了分类,通常是基于个别研究或化学经验,但迄今为止,很少有系统地评估其活性特征的工作。在此,我们报告了对 PAINS 在生物筛选检测中的行为的大规模分析。检测到超过 23000 种含有 PAINS 子结构的经过广泛测试的化合物,并确定了它们的命中率。许多一致无活性的化合物被鉴定出来。整体命中率较低,在数百个检测中,PAINS 的平均命中数为两到五个。只有有限的 PAINS 子集产生了大量的命中。经常在一致无活性和频繁有活性的化合物中发现相同的 PAINS 亚结构,这表明 PAINS 出现的结构背景调节了它们的作用。
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