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Polyfluoroalkyl Substances (PFASs) pose
a substantial threat as endocrine disruptors, and thus early identification of
those that may interact with steroid hormone receptors, such as the androgen
receptor (AR), is critical. In this study we screened 5,206 PFASs from the
CompTox database against the different binding sites on the AR using both molecular
docking and machine learning techniques. We developed support vector machine
models trained on Tox21 data to classify the active and inactive PFASs for AR
using different chemical fingerprints as features. The maximum accuracy was
95.01% and Matthew’s correlation coefficient (MCC) was 0.76 respectively, based
on MACCS fingerprints (MACCSFP). The combination of docking-based screening and
machine learning models identified 29 PFASs that have strong potential for
activity against the AR and should be considered priority chemicals for
biological toxicity testing.