Computational Models of Chemical Activity
Using structural data to generate activity predictions for new or poorly characterized chemicals can help researchers and regulators make decisions about further testing needs.
Project | Description | Publication |
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Open-source quantitative structure-property relationship tools | NICEATM and collaborators at EPA developed tools that use molecular structures to predict the physicochemical features for a wide range of substances. |
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QSAR models to screen for potential skin sensitizers | NICEATM and collaborators at the University of North Carolina-Chapel Hill (UNC-CH) developed QSAR models of human data that can either be combined with or used instead of animal data to screen for potential skin sensitizers. |
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Open-source tools for predicting estrogen and androgen receptor pathway activity | NICEATM is creating open-source versions of published computational models to predict activity relevant to endocrine disruption. Predictions from these models are available through NICEATM's Integrated Chemical Environment (ICE) resource. |
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QSAR models to predict critical parameters for in vitro to in vivo extrapolation (IVIVE) | To support an open-source workflow for IVIVE, NICEATM developed QSAR models to predict properties, such as human plasma fraction unbound and hepatic intrinsic clearance, that affect how substances behave in biological systems. | |
International collaborations to predict estrogenic, anti-androgenic, and acute systemic toxicity | NICEATM and EPA ran three global collaborations to leverage the expertise of the world-wide modeling community to predict estrogenic activity (CERAPP), anti-androgenic activity (CoMPARA) and acute oral systemic toxicity (CATMoS). Predictions from these models are available through ICE and will be available through the EPA Chemistry Dashboard. The consensus models from CERAPP, CoMPARA, and CATMoS were also added to the standalone OPERA application. |
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QSAR models to predict physicochemical properties, environmental fate and toxicological endpoints | OPERA was updated to include QSAR models to predict physicochemical properties such as lipid-aqueous dissociation coefficient (logD) and acid dissociation constant (pKa). |
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Statistical models for classification of eye irritants | NICEATM developed statistical models that could potentially be used to classify chemicals as eye corrosives, irritants, or non-irritants according to EPA and GHS hazard classification endpoints. Results suggest that these models are useful for screening substances for eye irritation potential. |
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Development of additional QSAR models |
NICEATM is developing QSAR models to predict the following:
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In progress |