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Structure-Based Virtual Screening of Perfluoroalkyl and Polyfluoroalkyl Substances (PFASs) as Endocrine Disruptors of Androgen Receptor Activity Using Molecular Docking and Machine Learning

submitted on 21.02.2020, 23:46 and posted on 24.02.2020, 13:09 by Azhagiya Singam Ettayapuram Ramaprasad, Phum Tachachartvanich, Denis Fourches, Anatoly Soshilov, Jennifer C.Y. Hsieh, Michele La merrill, Martyn T. Smith, Kathleen A. Durkin
Perfluoroalkyl and 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.


NIH S10OD023532

17-E0023, 17-E0024 Office of Environmental Health Hazard Assessment of the California Environmental Protection Agency

Hatch project 1002182United States Department of Agriculture National Institute of Food and Agriculture


Email Address of Submitting Author


University of California, Berkeley



ORCID For Submitting Author


Declaration of Conflict of Interest