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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.
Azhagiya Singam, Ettayapuram Ramaprasad; Tachachartvanich, Phum; Fourches, Denis; Soshilov, Anatoly; Hsieh, Jennifer C Y; La Merrill, Michele A; Smith, Martyn T; Durkin, Kathleen A.
Afiliación
  • Azhagiya Singam ER; Molecular Graphics and Computation Facility, College of Chemistry, University of California, Berkeley, CA, USA.
  • Tachachartvanich P; Department of Environmental Toxicology, University of California, Davis, CA, USA.
  • Fourches D; Department of Chemistry, North Carolina State University, Raleigh, NC, USA.
  • Soshilov A; Division of Scientific Programs, Pesticide and Environmental Toxicology Branch, Water Toxicology Section, Office of Environmental Health Hazard Assessment, California Environmental Protection Agency, USA.
  • Hsieh JCY; Division of Scientific Programs, Reproductive and Cancer Hazard Assessment Branch, Cancer Toxicology and Epidemiology Section, Office of Environmental Health Hazard Assessment, California Environmental Protection Agency, USA.
  • La Merrill MA; Department of Environmental Toxicology, University of California, Davis, CA, USA.
  • Smith MT; Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley, CA, USA. Electronic address: martynts@berkeley.edu.
  • Durkin KA; Molecular Graphics and Computation Facility, College of Chemistry, University of California, Berkeley, CA, USA. Electronic address: durkin@berkeley.edu.
Environ Res ; 190: 109920, 2020 11.
Article en En | MEDLINE | ID: mdl-32795691
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Disruptores Endocrinos / Fluorocarburos Tipo de estudio: Diagnostic_studies / Screening_studies Idioma: En Revista: Environ Res Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Disruptores Endocrinos / Fluorocarburos Tipo de estudio: Diagnostic_studies / Screening_studies Idioma: En Revista: Environ Res Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos
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