Computer Models of Chemical Activity
Using structural data to generate activity predictions for new or poorly characterized chemicals can inform decisions about further testing needs.
|Open-source quantitative structure-property relationship tools||NICEATM and collaborators at EPA developed tools that use molecular structures to estimate the physicochemical features for a wide range of chemicals.||Zang et al. 2017. In silico prediction of physicochemical properties of environmental chemicals using molecular fingerprints and machine learning. J Chem Inf Modeling 57:36-49|
|Quantitative structure–activity relationship models to screen for potential skin sensitizers||Working with collaborators at the University of North Carolina-Chapel Hill, NICEATM scientists developed QSAR models of human data that can be combined with or used instead of animal data to screen for potential skin sensitizers.||Alves et al. 2016. QSAR models of human data can enrich or replace LLNA testing for human skin sensitization. Green Chemistry 18:6501-6515|
|Domain-specific QSAR models for phenolic compounds||NICEATM and UNC-CH collaborators also developed domain-specific QSAR models to predict specific activity and relative potency of phenolic compounds that act on the estrogen receptor pathway.||In progress|
|Open source tools for predicting estrogen and androgen receptor activity||NICEATM is creating open-source versions of published and unpublished QSAR models to predict chemical activity relevant to endocrine disruption. Predictions from these models will be available via NICEATM’s Integrated Chemical Environment resource.||In progress|
|QSAR models to predict critical parameters for in vitro to in vivo extrapolation||As part of an open-source workflow for IVIVE, NICEATM is developing QSAR models to predict Henry’s constant, dissociation constant, and tissue-specific partition coefficients.||In progress|