{"accessLevel": "public", "bureauCode": ["020:00"], "contactPoint": {"fn": "Richard Judson", "hasEmail": "mailto:judson.richard@epa.gov"}, "description": "This paper describes a model to take chemical structures and predict a property (the point of departure) for a new chemical. No new data were generated. The contents of this zip file contains metadata that you could use to make a model prediction. It does contain all of the code and a help file describing how to run the model. \n\nThis dataset is associated with the following publication:\nPradeep, P., K. Paul-Friedman, and R. Judson. Structure-based QSAR Models to Predict Repeat Dose Toxicity Points of Departure.   Computational Toxicology. Elsevier B.V., Amsterdam,  NETHERLANDS, 16(November 2020): 100139, (2020).", "distribution": [{"downloadURL": "https://pasteur.epa.gov/uploads/10.23719/1520778/Pradeep%20et%20al%20QSAR%20Models%20Supp%20Data%20Files.zip", "mediaType": "application/x-zip-compressed", "title": "Pradeep et al QSAR Models Supp Data Files.zip"}], "identifier": "https://doi.org/10.23719/1520778", "keyword": ["systemic toxicity", "point of departure", "qsar", "repeat dose toxicity"], "license": "https://pasteur.epa.gov/license/sciencehub-license-non-epa-generated.html", "modified": "2020-09-24", "programCode": ["020:000"], "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/10.1016/j.comtox.2020.100139"], "rights": null, "title": "Metadata Files for Structure-based QSAR models to predict repeat dose toxicity points of departure"}