Developing QSAR Models with Defined Applicability Domains on PPARγ Binding Affinity Using Large Data Sets and Machine Learning Algorithms

Chemicals
may cause adverse effects on human health through binding
to peroxisome proliferator-activated receptor γ (PPARγ).
Hence, binding affinity is useful for evaluating chemicals with potential
endocrine-disrupting effects. Quantitative structure–activity
relationship (QSAR) regression models with defined applicability domains
(ADs) are important to enable efficient screening of chemicals with
PPARγ binding activity. However, lack of large data sets hindered
the development of QSAR models. In this study, based on PPARγ
binding affinity data sets curated from various sources, 30 QSAR models
were developed using molecular fingerprints, two-dimensional descriptors,
and five machine learning algorithms. Structure–activity landscapes
(SALs) of the training compounds were described by network-like similarity
graphs (NSGs). Based on the NSGs, local discontinuity scores were
calculated and found to be positively correlated with the cross-validation
absolute prediction errors of the models using the different training
sets, descriptors, and algorithms. Moreover, innovative ADs were defined
based on pairwise similarities between compounds and were found to
outperform some conventional ADs. The curated data sets and developed
regression models could be useful for evaluating PPARγ-involved
adverse effects of chemicals. The SAL analysis and the innovative
ADs could facilitate understanding of prediction results from QSAR
models.