tx9b00498_si_001.xlsx (647.53 kB)
Applicability Domains Enhance Application of PPARγ Agonist Classifiers Trained by Drug-like Compounds to Environmental Chemicals
dataset
posted on 2020-02-17, 05:33 authored by Zhongyu Wang, Jingwen Chen, Huixiao HongPeroxisome
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.