Prediction of estrogen receptor binding for 58,000 chemicals using an integrated system of a tree-based model with structural alerts.
2002; National Institute of Environmental Health Sciences; Volume: 110; Issue: 1 Linguagem: Inglês
10.1289/ehp.0211029
ISSN1552-9924
AutoresHuixiao Hong, Weida Tong, Hong Fang, Leming Shi, Qian Xie, Jie Wu, Roger Perkins, John D. Walker, William S. Branham, Daniel M. Sheehan,
Tópico(s)Effects and risks of endocrine disrupting chemicals
ResumoResearch ArticleOpen AccessPrediction of estrogen receptor binding for 58,000 chemicals using an integrated system of a tree-based model with structural alerts. Huixiao Hong, Weida Tong, Hong Fang, Leming Shi, Qian Xie, Jie Wu, Roger Perkins, John D Walker, William Branham, and Daniel M Sheehan Huixiao Hong R.O.W. Sciences, Inc., Jefferson, Arkansas 72079, USA. , Weida Tong R.O.W. Sciences, Inc., Jefferson, Arkansas 72079, USA. , Hong Fang R.O.W. Sciences, Inc., Jefferson, Arkansas 72079, USA. , Leming Shi R.O.W. Sciences, Inc., Jefferson, Arkansas 72079, USA. , Qian Xie R.O.W. Sciences, Inc., Jefferson, Arkansas 72079, USA. , Jie Wu R.O.W. Sciences, Inc., Jefferson, Arkansas 72079, USA. , Roger Perkins R.O.W. Sciences, Inc., Jefferson, Arkansas 72079, USA. , John D Walker R.O.W. Sciences, Inc., Jefferson, Arkansas 72079, USA. , William Branham R.O.W. Sciences, Inc., Jefferson, Arkansas 72079, USA. , and Daniel M Sheehan R.O.W. Sciences, Inc., Jefferson, Arkansas 72079, USA. Published:1 January 2002https://doi.org/10.1289/ehp.0211029Cited by:80AboutSectionsPDF ToolsDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InRedditEmail AbstractA number of environmental chemicals, by mimicking natural hormones, can disrupt endocrine function in experimental animals, wildlife, and humans. These chemicals, called "endocrine-disrupting chemicals" (EDCs), are such a scientific and public concern that screening and testing 58,000 chemicals for EDC activities is now statutorily mandated. Computational chemistry tools are important to biologists because they identify chemicals most important for in vitro and in vivo studies. Here we used a computational approach with integration of two rejection filters, a tree-based model, and three structural alerts to predict and prioritize estrogen receptor (ER) ligands. The models were developed using data for 232 structurally diverse chemicals (training set) with a 10(6) range of relative binding affinities (RBAs); we then validated the models by predicting ER RBAs for 463 chemicals that had ER activity data (testing set). The integrated model gave a lower false negative rate than any single component for both training and testing sets. When the integrated model was applied to approximately 58,000 potential EDCs, 80% (approximately 46,000 chemicals) were predicted to have negligible potential (log RBA < -4.5, with log RBA = 2.0 for estradiol) to bind ER. The ability to process large numbers of chemicals to predict inactivity for ER binding and to categorically prioritize the remainder provides one biologic measure to prioritize chemicals for entry into more expensive assays (most chemicals have no biologic data of any kind). The general approach for predicting ER binding reported here may be applied to other receptors and/or reversible binding mechanisms involved in endocrine disruption.FiguresReferencesRelatedDetailsCited By Thakkar S, Perkins R, Hong H and Tong W (2018) Computational Toxicology ☆ Comprehensive Toxicology, 10.1016/B978-0-12-801238-3.64317-9, (327-350), . Todeschini R, Consonni V, Ballabio D and Grisoni F (2018) Mapping of Activity through Dichotomic Scores (MADS): A new chemoinformatic approach to detect activity-rich structural regions, Journal of Chemometrics, 10.1002/cem.2994, 32:4, (e2994), Online publication date: 1-Apr-2018. Hong H, Zhu J, Chen M, Gong P, Zhang C and Tong W (2018) Quantitative Structure–Activity Relationship Models for Predicting Risk of Drug-Induced Liver Injury in Humans Drug-Induced Liver Toxicity, 10.1007/978-1-4939-7677-5_5, (77-100), . 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