Lies and Liabilities: Computational Assessment of High-Throughput Screening Hits to Identify Artifact Compounds.
Lies and Liabilities: Computational Assessment of High-Throughput Screening Hits to Identify Artifact Compounds.
复制标题
谎言和责任：高通量筛选命中的计算评估以识别人工化合物。
DOI：
10.1021/acs.jmedchem.3c00482
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发表时间：
2023
影响因子：
7.3
通讯作者：
Tropsha,Alexander
中科院分区：
文献类型：
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作者：
Alves,ViniciusM;Yasgar,Adam;Wellnitz,James;Rai,Ganesha;Rath,Marielle;Braga,RodolphoC;Capuzzi,StephenJ;Simeonov,Anton;Muratov,EugeneN;Zakharov,AlexeyV;Tropsha,Alexander
来自化学文库高通量筛选(HTS)的命中通常是假阳性，因为它们干扰了检测技术。作为回应，我们生成了最大的公开可用的化学责任库，并开发了“责任预测器”，这是一个免费的网络工具，用于预测HTS制品。更具体地说，我们生成、整理和集成了HTS的硫醇反应性、氧化还原活性和荧光素酶(萤火虫和纳米)活性的数据集，并开发和验证了定量结构-干扰关系(QSIR)模型来预测这些滋扰行为。由此得到的模型显示，每次检测256种外部化合物时，外部平衡准确率为58%-78%。在此开发和验证的QSIR模型比流行的疼痛过滤器更可靠地识别实验命中的滋扰化合物。这两个模型和精选的数据集都是在https://liability.mml.unc.edu/.上公开提供的“负债预测器”中实现的“风险预报器”可用作化学库设计的一部分，或用于对HTS命中进行分类。
Hits from high-throughput screening (HTS) of chemical libraries are often false positives due to their interference with assay detection technology. In response, we generated the largest publicly available library of chemical liabilities and developed “Liability Predictor,” a free web tool to predict HTS artifacts. More specifically, we generated, curated, and integrated HTS data sets for thiol reactivity, redox activity, and luciferase (firefly and nano) activity and developed and validated quantitative structure–interference relationship (QSIR) models to predict these nuisance behaviors. The resulting models showed 58–78% external balanced accuracy for 256 external compounds per assay. QSIR models developed and validated herein identify nuisance compounds among experimental hits more reliably than do popular PAINS filters. Both the models and the curated data sets were implemented in “Liability Predictor,” publicly available at https://liability.mml.unc.edu/. “Liability Predictor” may be used as part of chemical library design or for triaging HTS hits.