Applicability Domains Enhance Application of PPARγ Agonist Classifiers Trained by Drug-like Compounds to Environmental Chemicals

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.