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).

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).

Data and supplemental files from COMPARA (A large-scale modeling project). COMPARA combined multiple models developed in collaboration with modelers and computational toxicology scientists from 25 international groups to predict androgen receptor activity of a common set of 55,450 chemical structures. Quantitative structure-activity relationship models and docking approaches were employed to build a total of 91 predictive models for binding, agonist, and antagonist. The consensus values for agonist and antagonist activity are being made public through the CompTox Chemicals Dashboard. This dataset is associated with the following publication: Mansouri, K., N. Kleinstreuer, R. Judson, A. Williams, I. Shah, and A. Richard. CoMPARA: Collaborative Modeling Project for Androgen Receptor Activity. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 128(2): 27002, (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).

Data on 1855 chemicals were generated during ToxCast Phases I and II and Tox21 screening using 11 AR-related in vitro assays to build a computational network model for AR pathway activity. This dataset is associated with the following publication: Kleinstreuer, N.C., P. Ceger, E. Watt, M. Martin, K. Houck, P. Browne, R. Thomas, W. Casey, D. Dix, D. Allen, S. Sakamuru, M. Xia, R. Huang, and R. Judson. (Chemical Research in Toxicology) Development and Validation of a Computational Model for Androgen Receptor Activity. CHEMICAL RESEARCH IN TOXICOLOGY. American Chemical Society, Washington, DC, USA, 30(4): 946-964, (2017).

Data on 1855 chemicals were generated during ToxCast Phases I and II and Tox21 screening using 11 AR-related in vitro assays to build a computational network model for AR pathway activity. This dataset is associated with the following publication: Kleinstreuer, N.C., P. Ceger, E. Watt, M. Martin, K. Houck, P. Browne, R. Thomas, W. Casey, D. Dix, D. Allen, S. Sakamuru, M. Xia, R. Huang, and R. Judson. (Chemical Research in Toxicology) Development and Validation of a Computational Model for Androgen Receptor Activity. CHEMICAL RESEARCH IN TOXICOLOGY. American Chemical Society, Washington, DC, USA, 30(4): 946-964, (2017).

Data are summarized in a two-dimensional data matrix that was developed for each substance for hazard characterization (Tables S1–S3). In the horizontal direction of the matrix, read-across of the target phenol to the source analogues was performed for the purpose of data-gap filling, whereas in the vertical direction, data from different streams (traditional and NAM) were compared and contrasted, to evaluate concordance of orthogonal approaches for evaluating potential estrogenicity. The greater the degree of agreement in orthogonal approaches for determining bioactivity, the greater the confidence one has in using the collective results of such NAMs in hazard characterization of the target phenol. This dataset is associated with the following publication: Webster, F., M. Gagne, G. Patlewicz, P. Pradeep, N. Trefiak, R. Judson, and T. Barton-Maclaren. Predicting estrogen receptor activation by a group of substituted phenols: An integrated approach to testing and assessment case study. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, USA, 106: 278-291, (2019).

Data are summarized in a two-dimensional data matrix that was developed for each substance for hazard characterization (Tables S1–S3). In the horizontal direction of the matrix, read-across of the target phenol to the source analogues was performed for the purpose of data-gap filling, whereas in the vertical direction, data from different streams (traditional and NAM) were compared and contrasted, to evaluate concordance of orthogonal approaches for evaluating potential estrogenicity. The greater the degree of agreement in orthogonal approaches for determining bioactivity, the greater the confidence one has in using the collective results of such NAMs in hazard characterization of the target phenol. This dataset is associated with the following publication: Webster, F., M. Gagne, G. Patlewicz, P. Pradeep, N. Trefiak, R. Judson, and T. Barton-Maclaren. Predicting estrogen receptor activation by a group of substituted phenols: An integrated approach to testing and assessment case study. REGULATORY TOXICOLOGY AND PHARMACOLOGY. Elsevier Science Ltd, New York, NY, USA, 106: 278-291, (2019).

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).