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.

全氟和多氟烷基物质（PFAS）是广泛使用的、已知能够通过与人类过氧化物酶体增殖激活受体α（PPARα）结合而干扰肝脂质代谢的人为氟化化学物质。因此，筛选能够与PPARα结合的PFAS具有重要的意义。机器学习方法在快速筛选PFAS方面展现出巨大的潜力。然而，传统的机器学习方法缺乏可解释性，这在探究分子描述符与PPARα结合关系方面构成了挑战。在本研究中，我们旨在开发一种新颖且可解释的机器学习方法，以快速筛选与PPARα结合的PFAS。我们对PFAS计算了PPARα-PFAS结合评分和206个分子描述符。通过系统性和客观地筛选重要的分子描述符，我们仅使用三个描述符就开发出了一个具有良好预测性能的机器学习模型。分子大小（b_single）、静电性质（BCUT_PEOE_3和PEOE_VSA_PPOS）对于PPARα-PFAS的结合至关重要。替代型PFAS被认为比其传统的先辈更为安全。然而，我们发现含有众多碳原子和醚基团的替代型PFAS表现出对PPARα更高的亲和力。因此，通过生物学实验确认具有此类特性的替代型PFAS化合物的毒性至关重要。