The exposure data landscape for manufactured chemicals.

By P. Egeghy, Richard S. Judson, S. Gangwal, S. Mosher, Doris J Smith, J. Vail, Elaine A. Cohen Hubal. 2012

199 citations recorded by the source corpus.

Subfields: Health, Toxicology and Mutagenesis, Physical and Theoretical Chemistry

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Health, Toxicology and Mutagenesis

1. Richard S. Judson
2. Ann M. Richard

In Physical and Theoretical Chemistry

1. Richard S. Judson
2. Christopher M. Grulke
3. Kamel Mansouri
4. Antony J. Williams

Citations in the corpus

• OPERA models for predicting physicochemical properties and environmental fate endpoints (2018, background): “However, because tens of thousands of chemicals require screening [3, 7, 15, 18, 25], faster and more costeffective in silico methods such as quantitative structure– activity/property relationships (QSAR/QSPR) modeling approaches [13, 16, 18, 26–28] are needed to prioritize chemicals for testing.”
• The Next Generation Blueprint of Computational Toxicology at the U.S. Environmental Protection Agency. (2019, background): “The data landscape for chemical exposures is even more sparse than that for chemical toxicity (Egeghy et al., 2012), yet is equally critical for establishing risk.”