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 data set contains the figures and tables from the publication in addition to the means, standard errors of the mean and the sample sizes used in each group for every experiment. the data set also contains a description of the genes, their function and acronyms on the QPCR arrays used in the study. Finally, the dataset includes the histopathology reports on the uterine changes induced by the different chemicals and the criteria used by the pathologist to classify the estrogenic effects of the chemicals. This dataset is associated with the following publication: Conley, J., B. Hannas, V. Wilson, E. Gray, and J. Furr. A demonstration of the uncertainty in predicting the estrogenic activity of individual chemicals and mixtures from an in vitro estrogen receptor transcriptional activation assay (T47D-KBluc) to the in vivo uterotrophic assay using oral exposure. TOXICOLOGICAL SCIENCES. Society of Toxicology, 382-395, (2016).

the data set contains the figures and tables from the publication in addition to the means, standard errors of the mean and the sample sizes used in each group for every experiment. the data set also contains a description of the genes, their function and acronyms on the QPCR arrays used in the study. Finally, the dataset includes the histopathology reports on the uterine changes induced by the different chemicals and the criteria used by the pathologist to classify the estrogenic effects of the chemicals. This dataset is associated with the following publication: Conley, J., B. Hannas, V. Wilson, E. Gray, and J. Furr. A demonstration of the uncertainty in predicting the estrogenic activity of individual chemicals and mixtures from an in vitro estrogen receptor transcriptional activation assay (T47D-KBluc) to the in vivo uterotrophic assay using oral exposure. TOXICOLOGICAL SCIENCES. Society of Toxicology, 382-395, (2016).

Microarray experiments examined in the study. This dataset is associated with the following publication: Rooney, J., N. Ryan, J. Liu, R. Houtman, R. van Beuningen, J. Hsieh, G. Chang, S. Chen, and J. Corton. A Gene Expression Biomarker Identifies Chemical Modulators of Estrogen Receptor α in an MCF-7 Microarray Compendium. CHEMICAL RESEARCH IN TOXICOLOGY. American Chemical Society, Washington, DC, USA, 34(2): 313-329, (2021).

GEOSite dataset for article 'In vitro transcriptomic analyses reveal pathway perturbations, estrogenic activities, and potencies of data-poor BPA alternative chemicals '. This dataset is associated with the following publication: Matteo, G., K. Leingartner, A. Rowan-Carroll, M. Meier, A. Williams, M. Beal, M. Gagne, R. Farmahin, S. Wickramasuriya, A.J. Reardon, T. Barton-Maclaren, J. Corton, C. Yauk, and E. Atlas. In vitro transcriptomic analyses reveal pathway perturbations, estrogenic activities, and potencies of data-poor BPA alternative chemicals. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 191(2): 266-275, (2023).

Data from a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) demonstrating using predictive computational models on high-throughput screening data to screen thousands of chemicals against the estrogen receptor. This dataset is associated with the following publication: Mansouri , K., A. Abdelaziz, A. Rybacka, A. Roncaglioni, A. Tropsha, A. Varnek, A. Zakharov, A. Worth, A. Richard , C. Grulke , D. Trisciuzzi, D. Fourches, D. Horvath, E. Benfenati , E. Muratov, E.B. Wedebye, F. Grisoni, G.F. Mangiatordi, G.M. Incisivo, H. Hong, H.W. Ng, I.V. Tetko, I. Balabin, J. Kancherla , J. Shen, J. Burton, M. Nicklaus, M. Cassotti, N.G. Nikolov, O. Nicolotti, P.L. Andersson, Q. Zang, R. Politi, R.D. Beger , R. Todeschini, R. Huang, S. Farag, S.A. Rosenberg, S. Slavov, X. Hu, and R. Judson. (Environmental Health Perspectives) CERAPP: Collaborative Estrogen Receptor Activity Prediction Project. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 1-49, (2016).

This is an original dataset generated at the U.S. EPA. Data was analyzed with the ToxCast Pipeline and deposited for release in invitroDB accessible via the U.S. EPA CompTox Chemicals Dashboard. Dataset is a zip file containing two Excel spreadsheets titled AIME-ERTA_384_Tables_All_Submission_v2 and README_AIME-ERTA_384_Manuscript_Sup_Data_v2. This dataset is associated with the following publication: Hopperstad, K., D. DeGroot, T. Zurlinden, C. Brinkman, R. Thomas, and C. Deisenroth. Chemical Screening in an Estrogen Receptor Transactivation Assay with Metabolic Competence. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 187(1): 112-126, (2022).

Read-across is an important data gap filling technique used within category and analog approaches for regulatory hazard identification and risk assessment. Although much technical guidance is available that describes how to develop category/analog approaches, practical principles to evaluate and substantiate analog validity (suitability) are still lacking. This case study uses hindered phenols as an example chemical class to determine: (1) the capability of three structure fingerprint/descriptor methods (PubChem, ToxPrints and MoSS MCSS) to identify analogs for read-across to predict Estrogen Receptor (ER) binding activity and, (2) the utility of data confidence measures, physicochemical properties, and chemical R-group properties as filters to improve ER binding predictions. The training dataset comprised 462 hindered phenols and 257 non- hindered phenols. For each chemical of interest (target), source analogs were identified from two datasets (hindered and non-hindered phenols) that had been characterized by a fingerprint/descriptor method and by two cut-offs: (1) minimum similarity distance (range: 0.1 - 0.9) and, (2) N closest analogs (range: 1 - 10). Analogs were then filtered using: (1) physicochemical properties of the phenol (termed global filtering) and, (2) physicochemical properties of the R-groups neighboring the active hydroxyl group (termed local filtering). A read-across prediction was made for each target chemical on the basis of a majority vote of the N closest analogs. The results demonstrate that: (1) concordance in ER activity increases with structural similarity, regardless of the structure fingerprint/descriptor method, (2) increased data confidence significantly improves read-across predictions, and (3) filtering analogs using global and local properties can help identify more suitable analogs. This case study illustrates that the quality of the underlying experimental data and use of endpoint relevant chemical descriptors to evaluate source analogs are critical to achieving robust read-across predictions. This dataset is associated with the following publication: Pradeep, P., K. Mansouri, G. Patlewicz, and R. Judson. (Computational Toxicology) A systematic evaluation of analogs and automated read-across prediction of estrogenicity: A case study using hindered phenols. Computational Toxicology. Elsevier B.V., Amsterdam, NETHERLANDS, 4: 22-30, (2017).

In vitro and in vivo data for the estrogen receptor. The in vivo data is for binding, agonism, and antagonism. The in vivo data is from mouse uterotropic assay data. The following columns are provided in each data set: molecular id, SMILES structure, class (1=active, 0 = inactive), and set (T=training, P=prediction set). This dataset is associated with the following publication: Martin , T. Prediction of in vitro and in vivo oestrogen receptor activity using hierarchical clustering. SAR AND QSAR IN ENVIRONMENTAL RESEARCH. Taylor & Francis, Inc., Philadelphia, PA, USA, 27(1): 17-30, (2016).

In vitro and in vivo data for the estrogen receptor. The in vivo data is for binding, agonism, and antagonism. The in vivo data is from mouse uterotropic assay data. The following columns are provided in each data set: molecular id, SMILES structure, class (1=active, 0 = inactive), and set (T=training, P=prediction set). This dataset is associated with the following publication: Martin , T. Prediction of in vitro and in vivo oestrogen receptor activity using hierarchical clustering. SAR AND QSAR IN ENVIRONMENTAL RESEARCH. Taylor & Francis, Inc., Philadelphia, PA, USA, 27(1): 17-30, (2016).