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Determining the Predictive Limit of QSAR Models
Citation:
Kolmar, S. Determining the Predictive Limit of QSAR Models. Sciome WIP Webinar, Virtual, Virtual, July 14, 2021. https://doi.org/10.23645/epacomptox.14983854
Impact/Purpose:
QSAR models provide an automated method for the estimation of all types of chemical safety relevant endpoints for data poor chemicals. To provide robust QSAR models to inform chemical evaluation, a set of best practices for modeling and dataset collection will be defined. These procedures will then be applied for endpoints with known value to the Agency in its chemical safety efforts including the prediction of toxicities, bioactivities, and environmental fate and physicochemical properties to support exposure modeling. Where appropriate, the predictive performance of models will be compared with current models being used by the program offices to ensure accuracy and fit for purpose. Finally, research into the interplay between dataset attributes (e.g., size, noisiness, curation level, source disparities) and model quality (predictive power) will be completed to better estimate the uncertainty of our predictions and to provide guidance in improving our QSAR modeling strategies in the future.
Description:
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URLs/Downloads:
DOI: Determining the Predictive Limit of QSAR Models
SK_062421_COP.PDF (PDF, NA pp, 4808.746 KB, about PDF)