Development, validation and integration of in silico models to identify androgen active chemicals
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).
Complete Metadata
| accessLevel | public |
|---|---|
| bureauCode |
[
"020:00"
]
|
| contactPoint |
{
"fn": "Richard Judson",
"hasEmail": "mailto:judson.richard@epa.gov"
}
|
| description | 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). |
| distribution |
[
{
"title": "https://cran.r-project.org/web/packages/tcpl/index.html",
"accessURL": "https://cran.r-project.org/web/packages/tcpl/index.html"
},
{
"title": "https://www.fda.gov/science-research/bioinformatics-tools/endocrine-disruptor-knowledge-base",
"accessURL": "https://www.fda.gov/science-research/bioinformatics-tools/endocrine-disruptor-knowledge-base"
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]
|
| identifier | https://doi.org/10.23719/1503185 |
| keyword |
[
"ACToR",
"Androgen receptor",
"Support vector machines",
"articial neural networks",
"decision tree",
"endocrine disrupting chemicals",
"high-throughput screening",
"in silico"
]
|
| license | https://pasteur.epa.gov/license/sciencehub-license.html |
| modified | 2019-05-06 |
| 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/10.1016/j.chemosphere.2018.12.131"
]
|
| rights |
null
|
| title | Development, validation and integration of in silico models to identify androgen active chemicals |
