Scientific Publications by FDA Staff

Chem Res Toxicol 2020 Jun 15;33(6):1382-8

Applicability domains enhance application of PPAR gamma agonist classifiers trained by drug-like compounds to environmental chemicals.

Wang ZY, Chen JW, Hong HX

Peroxisome proliferator activator receptor gamma (PPAR gamma) agonist activity of chemicals is an indicator of concerned health conditions such as fatty liver and obesity. In silico screening PPAR gamma 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 PPAR gamma-targeted drugs, a large amount of PPAR gamma 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 PPAR gamma 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 PPAR gamma agonists.