{
  "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": [
    "point of departure",
    "qsar",
    "repeat dose toxicity",
    "systemic 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"
}