Peroxisome proliferator activator receptor gamma (PPARγ) agonist activity of chemicals is an indicator of concerned health conditions such as fatty liver and obesity. In silico screening PPARγ agonists based on quantitative structure–activity relationship (QSAR) models could serve as an efficient and pragmatic strategy. Owing to the broad research interests in discovery of PPARγ-targeted drugs, a large amount of PPARγ agonist activity data has been produced in the field of medicinal chemistry, facilitating development of robust QSAR models. In this study, random forest classifiers were developed based on the binary-category data transformed from the heterogeneous PPARγ agonist activity data of drug-like compounds. Coupling with applicability domains, capability of the established classifiers for predicting environmental chemicals was evaluated using two external data sets. Our results demonstrated that applicability domains could enhance application of the developed classifiers to predict environmental PPARγ agonists.

中文翻译：

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适用性域增强了由类药物化合物训练的PPARγ激动剂分类器在环境化学品中的应用。

化学物质的过氧化物酶体增殖物激活剂受体γ（PPARγ）激动剂活性是有关健康状况（如脂肪肝和肥胖症）的指标。电脑基于定量结构-活性关系（QSAR）模型筛选PPARγ激动剂可以作为一种有效而务实的策略。由于对PPARγ靶向药物的发现有广泛的研究兴趣，因此在药物化学领域已经产生了大量PPARγ激动剂活性数据，从而促进了健壮的QSAR模型的开发。在这项研究中，基于从类药物化合物的异质PPARγ激动剂活性数据转换而来的二元分类数据开发了随机森林分类器。结合适用领域，使用两个外部数据集评估了建立的分类器预测环境化学品的能力。我们的结果表明，适用性域可以增强开发的分类器在预测环境PPARγ激动剂中的应用。