False‐positive assay readouts caused by badly behaving compounds—frequent hitters, pan‐assay interference compounds (PAINS), aggregators, and others—continue to pose a major challenge to experimental screening. There are only a few in silico methods that allow the prediction of such problematic compounds. We report the development of Hit Dexter, two extremely randomized trees classifiers for the prediction of compounds likely to trigger positive assay readouts either by true promiscuity or by assay interference. The models were trained on a well‐prepared dataset extracted from the PubChem Bioassay database, consisting of approximately 311 000 compounds tested for activity on at least 50 proteins. Hit Dexter reached MCC and AUC values of up to 0.67 and 0.96 on an independent test set, respectively. The models are expected to be of high value, in particular to medicinal chemists and biochemists who can use Hit Dexter to identify compounds for which extra caution should be exercised with positive assay readouts. Hit Dexter is available as a free web service at http://hitdexter.zbh. uni‐hamburg.de.

中文翻译：

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Hit Dexter：用于预测频繁击球者的机器学习模型

由于行为不佳的化合物（频繁的击打剂，泛测定干扰化合物（PAINS），聚集器等）导致的假阳性测定结果继续对实验筛选构成重大挑战。只有很少的计算机方法可以预测此类有问题的化合物。我们报告了Hit Dexter的发展，Hit Dexter是两个非常随机的树分类器，用于预测可能通过真实滥交或通过分析干扰触发阳性分析读数的化合物。在从PubChem Bioassay数据库中提取的充分准备的数据集上对模型进行了训练，该数据集由大约311,000种化合物测试了对至少50种蛋白质的活性。在独立测试集上，Hit Dexter的MCC和AUC值分别达到0.67和0.96。这些模型有望具有很高的价值，特别是对于可以使用Hit Dexter来识别化合物的药物化学家和生物化学家，应该对这些化合物加倍注意，并应采用阳性测定读数。Hit Dexter可从http：//hitdexter.zbh免费获得网络服务。uni-hamburg.de。