Structure-based virtual screening of perfluoroalkyl and polyfluoroalkyl substances (PFASs) as endocrine disruptors of androgen receptor activity using molecular docking and machine learning

Ettayapuram Ramaprasad Azhagiya Singam, Phum Tachachartvanich, Denis Fourches, Anatoly Soshilov, Jennifer C.Y. Hsieh, Michele A. La Merrill, Martyn T. Smith, Kathleen A. Durkin

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article number109920
JournalEnvironmental Research
Volume190
DOIs
StatePublished - Nov 2020

Keywords

  • Modeling
  • Steroid hormones
  • Tox21

ASJC Scopus subject areas

  • Biochemistry
  • Environmental Science(all)

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