{"accessLevel": "public", "bureauCode": ["020:00"], "contactPoint": {"fn": "Ann Richard", "hasEmail": "mailto:richard.ann@epa.gov"}, "description": "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. \n\nThis dataset is associated with the following publication:\nMansouri , 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).", "distribution": [{"accessURL": "https://gaftp.epa.gov/COMPTOX/Sustainable_Chemistry_Data/CERAPP_QSAR_Models/", "title": "https://gaftp.epa.gov/COMPTOX/Sustainable_Chemistry_Data/CERAPP_QSAR_Models/"}], "identifier": "A-6t1n-312", "keyword": ["qsar", "endocrine disruption", "estrogen receptor", "ToxCast", "DSSTox", "Chemistry Dashboard", "Read Across"], "license": "https://pasteur.epa.gov/license/sciencehub-license.html", "modified": "2016-07-01", "programCode": ["020:095"], "publisher": {"name": "U.S. EPA Office of Research and Development (ORD)", "subOrganizationOf": {"name": "U.S. Environmental Protection Agency", "subOrganizationOf": {"name": "U.S. Government"}}}, "references": ["https://doi.org/10.1289/ehp.1510267"], "rights": null, "title": "CERAPP: Collaborative Estrogen Receptor Activity Prediction Project"}