Bioactivity Classification of 2,649 Per- and Polyfluoroalkyl Substances (PFASs) via Quantitative Structure–Activity Relationships and Molecular Docking to Health-Relevant Proteins

The structural diversity
of poly- and perfluoroalkyl
substances
(PFASs) poses challenges for class-based chemical hazard assessments.
Computational simulations of PFAS-protein binding provide a means
to improve screening-level evaluations by considering molecular interactions
with toxicologically relevant proteins. Many PFASs are known to interact
with carrier and channel proteins that influence bioaccumulation and
toxicity, yet experimental data remain limited. To address this gap,
we developed multicondition quantitative structure–activity
relationship (QSAR) models that incorporate PFAS-protein associations.
We simulated interactions of 4,397 PFASs with six health-relevant
proteins, generating molecular docking binding scores for 12 ligand-binding
sites. We then constructed seven bioactivity prediction models, integrating
44 additional chemical descriptors for each target. Training used
binary bioactivity classifications for 560 PFASs derived from public
toxicology and bioactivity databases. Using the best performing model
(random forest), we classified the bioactivity potential of 2,649
PFASs within the applicability domain. Strong binding of PFASs to
human serum albumin (fatty acid binding sites 3 and 4) and peroxisome
proliferator-activated receptor γ, along with molecular complexity
and the number of saturated hetero rings, were associated with PFAS
bioactivity. A structure–activity landscape index analysis
further suggested that nonfluorinated moietiesincluding bromine
substitution, benzimidazole, and dinitroaniline groupscontribute
to PFAS bioactivity.