Elucidating Key Characteristics of PFAS Binding to Human Peroxisome Proliferator-Activated Receptor Alpha: An Explainable Machine Learning Approach

Per-
and polyfluoroalkyl substances (PFAS) are widely employed
anthropogenic fluorinated chemicals known to disrupt hepatic lipid
metabolism by binding to human peroxisome proliferator-activated receptor
alpha (PPARα). Therefore, screening for PFAS that bind to PPARα
is of critical importance. Machine learning approaches are promising
techniques for rapid screening of PFAS. However, traditional machine
learning approaches lack interpretability, posing challenges in investigating
the relationship between molecular descriptors and PPARα binding.
In this study, we aimed to develop a novel, explainable machine learning
approach to rapidly screen for PFAS that bind to PPARα. We calculated
the PPARα–PFAS binding score and 206 molecular descriptors
for PFAS. Through systematic and objective selection of important
molecular descriptors, we developed a machine learning model with
good predictive performance using only three descriptors. The molecular
size (b_single) and electrostatic properties (BCUT_PEOE_3 and PEOE_VSA_PPOS) are important
for PPARα-PFAS binding. Alternative PFAS are considered safer
than their legacy predecessors. However, we found that alternative
PFAS with many carbon atoms and ether groups exhibited a higher affinity
for PPARα. Therefore, confirming the toxicity of these alternative
PFAS compounds with such characteristics through biological experiments
is important.