The U.S. Environmental Protection Agency (EPA) periodically releases in vitro data across a variety of targets, including the estrogen receptor (ER). In 2015, the EPA used these data to construct mathematical models of ER agonist and antagonist pathways to prioritize chemicals for endocrine disruption testing. However, mathematical models require in vitro data prior to predicting estrogenic activity, but machine learning methods are capable of prospective prediction from the molecular structure alone. The current study describes the generation and evaluation of Bayesian machine learning models grouped by the EPA’s ER agonist pathway model using multiple data types with proprietary software, Assay Central. External predictions with three test sets of in vitro and in vivo reference chemicals with agonist activity classifications were compared to previous mathematical model publications. Training data sets were subjected to additional machine learning algorithms and compared with rank normalized scores of internal five-fold cross-validation statistics. External predictions were found to be comparable or superior to previous studies published by the EPA. When assessing six additional algorithms for the training data sets, Assay Central performed similarly at a reduced computational cost. This study demonstrates that machine learning can prioritize chemicals for future in vitro and in vivo testing of ER agonism.

中文翻译：

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雌激素受体生物活性和内分泌干扰预测的机器学习模型。

美国环境保护署 (EPA) 定期发布各种目标的体外数据，包括雌激素受体 (ER)。2015 年，EPA 使用这些数据构建了 ER 激动剂和拮抗剂途径的数学模型，以优先考虑用于内分泌干扰测试的化学品。然而，数学模型在预测雌激素活性之前需要体外数据，但机器学习方法能够仅从分子结构进行前瞻性预测。当前的研究描述了贝叶斯机器学习模型的生成和评估，这些模型由 EPA 的 ER 激动剂通路模型使用专有软件 Assay Central 使用多种数据类型进行分组。三个体外测试集的外部预测将具有激动剂活性分类的体内参考化学品与以前的数学模型出版物进行比较。训练数据集经过额外的机器学习算法，并与内部五重交叉验证统计的等级归一化分数进行比较。外部预测被发现与 EPA 先前发表的研究相当或更好。在评估训练数据集的六种额外算法时，Assay Central 的表现类似，但计算成本较低。这项研究表明，机器学习可以优先考虑化学物质，以便将来对 ER 激动作用进行体外和体内测试。