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Data Aggregation, Curation and Modeling Approaches to Deliver Prediction Models to Support Computational Toxicology at the EPA (ACS Fall meeting)
Citation:
Williams, A., K. Mansouri, T. Martin, Christopher M. Grulke, John F. Wambaugh, R. Judson, A. Richard, G. Patlewicz, AND I. Shah. Data Aggregation, Curation and Modeling Approaches to Deliver Prediction Models to Support Computational Toxicology at the EPA (ACS Fall meeting). Presented at ACS Fall Meeting, Phildelphia, PA, August 21 - 25, 2016.
Impact/Purpose:
Presentation at the ACS PHYS session: Accelerating discovery: Citizen science, big data, and machine learning for physical chemistry
Description:
The U.S. Environmental Protection Agency (EPA) Computational Toxicology Program develops and utilizes QSAR modeling approaches across a broad range of applications. In terms of physical chemistry we have a particular interest in the prediction of basic physicochemical parameters such as logP, aqueous solubility, vapor pressure and other parameters to invoke in our exposure models or for the purpose of modeling environmental toxicity. We are also interested in the development of models related to environmental fate. As a result of our efforts we have assembled and curated data sets for various physicochemical properties and, utilizing modern machine-learning modeling approaches, have developed a number of high performing models that we are now delivering to the public. Our website, the iCSS Chemistry Dashboard, provides access to data predicted for over 700,000 chemical compounds. The original training data are available for review and the details of prediction for each endpoint include the domain of applicability as well as a measure of performance accuracy. This presentation will provide an overview of the existing aggregated data, our approaches to data curation and our progress towards an interactive environment for prediction of physicochemical and environmental fate parameters. The utilization of these parameters to support read-across approaches will also be discussed. This abstract does not reflect U.S. EPA policy.