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Computational Approaches to Support Identification of Chemicals in the Environment
Citation:
McEachran, A. AND A. Williams. Computational Approaches to Support Identification of Chemicals in the Environment. IN: Springer Nature, Springer Nature, New York, NY, 1-2, (2019).
Impact/Purpose:
To further increase the confidence in an identification beyond metadata, researchers use spectral “fragmentation patterns” (how a chemical structure breaks apart in a high energy collision) to match what was observed on an analytical instrument to what has previously been observed for that same structure. These data, when available, can boost the confidence in identifying chemicals and there are an increasing number of freely available spectral databases available online. The goal in our reported work was to fill a crucial gap by predicting and storing the fragmentation patterns of the entirety of the EPA’s DSSTox database to enable easy access to both the rich metadata and fragmentation patterns for broad, high-throughput use to boost confidence in chemical identifications. We hope that individuals, research groups, and analytical chemistry vendors will find the data of value, informative, and effective.
Description:
The number of chemicals detected in the environment continues to increase. These range from expected pollutants such as pesticides, pharmaceuticals (for example, opioids and cannabinoids) and metabolites and degradants. The rapid identification of small molecules in environmental monitoring studies generally utilizes high resolution mass spectrometry (HRMS) and non-targeted analysis (NTA) techniques. NTA analysis generally combines the acquisition of HRMS spectral signatures for hundreds to thousands of chemicals with informatics approaches that perform searches against databases containing “known” chemicals. While these large databases are useful for broad chemical searching, more focused databases are better-suited for identifying chemicals in the environment. At the US-EPA we have been building a more focused data collection to support our computational toxicology research for almost 20 years (the DSSTox Database which now contains over 875,000 substances (as of August 2019). The views expressed in this paper are those of the authors and do not necessarily reflect the views or policies of the U.S. EPA.