The paper has data generated by NIH and the EPA coauthors provided input into the preparation of the manuscript. This dataset is not publicly accessible because: Data was not collected in EPA labs or paid for by EPA. It can be accessed through the following means: Data generated by NIH. Format: N/A. This dataset is associated with the following publication: Lynch, C., S. Sakamuru, R. Huang, D.A. Stavea, L. Varticovski, G.L. Hagar, R.S. Judson, K.A. Houck, N.C. Kleinstreuer, W. Casey, R.S. Paules, A. Simeonov, and M. Xia. (Toxicology) Identifying Environmental Chemicals as Agonists of the Androgen Receptor by Applying a Quantitative High-throughput Screening Platform. TOXICOLOGY. Elsevier Science Ltd, New York, NY, USA, 385: 48-58, (2017).

Screening certain environmental chemicals for their ability to interact with endocrine targets, including the androgen receptor (AR), is an important global concern. We previously developed a model using a battery of eleven in vitro AR assays to predict in vivo AR activity. Here we describe a revised mathematical modelling approach that also incorporates data from newly available assays and demonstrate that subsets of assays can provide close to the same level of predictivity. These subset models are evaluated against the full model using 1820 chemicals, as well as in vitro and in vivo reference chemicals from the literature. This dataset is associated with the following publication: Judson, R., K. Houck, K. Friedman, J. Brown, P. Browne, P. Johnston, D. Close, K. Mansouri, and N. Kleinstreuer. Selecting a Minimal set of Androgen Receptor Assays for Screening Chemicals. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, USA, 117(November 2020): 104764, (2020).

Screening certain environmental chemicals for their ability to interact with endocrine targets, including the androgen receptor (AR), is an important global concern. We previously developed a model using a battery of eleven in vitro AR assays to predict in vivo AR activity. Here we describe a revised mathematical modelling approach that also incorporates data from newly available assays and demonstrate that subsets of assays can provide close to the same level of predictivity. These subset models are evaluated against the full model using 1820 chemicals, as well as in vitro and in vivo reference chemicals from the literature. This dataset is associated with the following publication: Judson, R., K. Houck, K. Friedman, J. Brown, P. Browne, P. Johnston, D. Close, K. Mansouri, and N. Kleinstreuer. Selecting a Minimal set of Androgen Receptor Assays for Screening Chemicals. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, USA, 117(November 2020): 104764, (2020).

Chemical structures, RMSD values, docking scores, additional tables and figures, and methodological details (PDF) Additional information concerning the starting data set, EPA-ARDB.csv (CSV) Additional information concerning V1, V1_SI.csv (CSV) Additional information concerning V2, V2_SI.csv (CSV) Additional information concerning V3, V3_SI.csv (CSV). This dataset is associated with the following publication: Trisciuzzi, D., D. Alberga, K. Mansouri, R. Judson, E. Novellino, G.F. Mangiatordi, and O. Nicolotti. Predictive structure-based toxicology approaches to assess the androgenic potential of chemicals. Journal of Chemical Information and Modeling. American Chemical Society, Washington, DC, USA, 57(11): 2874-2884, (2017).

Chemical structures, RMSD values, docking scores, additional tables and figures, and methodological details (PDF) Additional information concerning the starting data set, EPA-ARDB.csv (CSV) Additional information concerning V1, V1_SI.csv (CSV) Additional information concerning V2, V2_SI.csv (CSV) Additional information concerning V3, V3_SI.csv (CSV). This dataset is associated with the following publication: Trisciuzzi, D., D. Alberga, K. Mansouri, R. Judson, E. Novellino, G.F. Mangiatordi, and O. Nicolotti. Predictive structure-based toxicology approaches to assess the androgenic potential of chemicals. Journal of Chemical Information and Modeling. American Chemical Society, Washington, DC, USA, 57(11): 2874-2884, (2017).

A diverse data set of 1667 chemicals with AR experimental activity were provided by the U.S. EPA from the oxicity Forecaster (ToxCast) program which generates data using in vitro high-throughput screening (HTS) assays measuring activity of chemicals at multiple points along the androgen receptor (AR) activity pathway. The Endocrine Disruptor Knowledgebase (EDKB) androgen receptor (AR) binding data set (Fang et al., 2003) was downloaded from the FDA website and was produced expressly as a training set designed for developing predictive models. The data is based on a validated assay using recombinant AR. The dataset contains 146 AR binders and 56 non-AR binders. These training set chemicals were selected for both chemical structure diversity and range of activity, both of which are essential to develop robust QSAR and other models (Perkins, 2003). This dataset is associated with the following publication: Manganelli, S., A. Roncaglioni, K. Mansouri, R. Judson, E. Benfenati, A. Manganaro, and P. Ruiz. Development, validation and integration of in silico models to identify androgen active chemicals. CHEMOSPHERE. Elsevier Science Ltd, New York, NY, USA, 220: 204-215, (2019).

It is literature data. This dataset is not publicly accessible because: The data was not generated by EPA. It can be accessed through the following means: Link will be available when it is published in peer reviewed journal. Format: The review paper only included published literature data. This dataset is associated with the following publication: Eichler, C., Y. Xu, J. Cao, C. Weschler, T. Salthammer, G. Morrison, Y. Zhang, C. Mandin, W. Wei, P. Blondeau, D. Poppendieck, E. Cohen-Hubal, X. Liu, C. Delmaar, A.J. Koivisto, O. Jolliet, H. Shin, M. Diamond, C. Bi, and J. Little. Assessing Human Exposure to Chemicals in Materials, Products and Articles:A Modular Mechanistic Framework. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, NA, (2020).

It is literature data. This dataset is not publicly accessible because: The data was not generated by EPA. It can be accessed through the following means: Link will be available when it is published in peer reviewed journal. Format: The review paper only included published literature data. This dataset is associated with the following publication: Eichler, C., Y. Xu, J. Cao, C. Weschler, T. Salthammer, G. Morrison, Y. Zhang, C. Mandin, W. Wei, P. Blondeau, D. Poppendieck, E. Cohen-Hubal, X. Liu, C. Delmaar, A.J. Koivisto, O. Jolliet, H. Shin, M. Diamond, C. Bi, and J. Little. Assessing Human Exposure to Chemicals in Materials, Products and Articles:A Modular Mechanistic Framework. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, NA, (2020).

This is a manuscript developed by a group at University of Berlin and Danish Technical University, using previously published data from EPA. The only EPA interaction has been providing advice on how to correctly use the data. This dataset is not publicly accessible because: The data were not collected by EPA and are hosted external to the agency. It can be accessed through the following means: Contact the corresponding author Yasmine Emara at the Department of Environmental Technology, Technical University Berlin, 10623 Berlin, Germany. Email: y.emara@tu-berlin.de. Format: Not available. This dataset is associated with the following publication: Emara, Y., P. Fantke, R. Judson, X. Chang, P. Pradeep, A. Lehmann, M. Siegert, and M. Finkbeiner. Integrating endocrine-related health effects into comparative human toxicity characterization. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 762: 143874, (2021).

This is a manuscript developed by a group at University of Berlin and Danish Technical University, using previously published data from EPA. The only EPA interaction has been providing advice on how to correctly use the data. This dataset is not publicly accessible because: The data were not collected by EPA and are hosted external to the agency. It can be accessed through the following means: Contact the corresponding author Yasmine Emara at the Department of Environmental Technology, Technical University Berlin, 10623 Berlin, Germany. Email: y.emara@tu-berlin.de. Format: Not available. This dataset is associated with the following publication: Emara, Y., P. Fantke, R. Judson, X. Chang, P. Pradeep, A. Lehmann, M. Siegert, and M. Finkbeiner. Integrating endocrine-related health effects into comparative human toxicity characterization. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 762: 143874, (2021).