Firefly luciferase is an enzyme that has found ubiquitous use in biological assays in high-throughput screening (HTS) campaigns. The inhibition of luciferase in such assays could lead to a false positive result. This issue has been known for a long time, and there have been significant efforts to identify luciferase inhibitors in order to enhance recognition of false positives in screening assays. However, although a large amount of publicly accessible luciferase counterscreen data is available, to date little effort has been devoted to building a chemoinformatic model that can identify such molecules in a given data set. In this study we developed models to identify these molecules using various methods, such as molecular docking, SMARTS screening, pharmacophores, and machine learning methods. Among the structure-based methods, the pharmacophore-based method showed promising results, with a balanced accuracy of 74.2%. However, machine-learning approaches using associative neural networks outperformed all of the other methods explored, producing a final model with a balanced accuracy of 89.7%. The high predictive accuracy of this model is expected to be useful for advising which compounds are potential luciferase inhibitors present in luciferase HTS assays. The models developed in this work are freely available at the OCHEM platform at http://ochem.eu.

中文翻译：

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荧光素酶顾问：在荧光素酶HTS分析中标记假阳性命中的高精度模型

萤火虫荧光素酶是一种在高通量筛选（HTS）活动中的生物学分析中普遍使用的酶。在这种测定中荧光素酶的抑制可能导致假阳性结果。这个问题早已为人所知，为了识别荧光素酶抑制剂，人们进行了巨大的努力，以增强对筛选试验中假阳性的认识。然而，尽管可获得大量公众可获得的萤光素酶反筛选数据，但迄今为止，很少有人致力于建立可在给定数据集中识别此类分子的化学信息学模型。在这项研究中，我们开发了使用各种方法识别这些分子的模型，例如分子对接，SMARTS筛选，药效团和机器学习方法。在基于结构的方法中，基于药效团的方法显示出令人鼓舞的结果，平衡精度为74.2％。但是，使用关联神经网络的机器学习方法优于所有其他探索方法，从而产生了最终模型，其平衡精度为89.7％。预期该模型的高预测准确性可用于建议哪些化合物是萤光素酶HTS分析中存在的潜在萤光素酶抑制剂。可在OCHEM平台（http://ochem.eu）上免费获得此工作中开发的模型。预期该模型的高预测准确性可用于建议哪些化合物是萤光素酶HTS分析中存在的潜在萤光素酶抑制剂。可在OCHEM平台（http://ochem.eu）上免费获得此工作中开发的模型。预期该模型的高预测准确性可用于建议哪些化合物是萤光素酶HTS分析中存在的潜在萤光素酶抑制剂。可在OCHEM平台（http://ochem.eu）上免费获得此工作中开发的模型。