Citation:

Yuan, C., C. Stevens, AND E. Weber. Reaction Library to Predict Direct Photochemical Transformation Products of Environmental Organic Contaminants in Sunlit Aquatic Systems. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, 54(12):7271–7279, (2020). https://doi.org/10.1021/acs.est.0c00484

Impact/Purpose:

Knowledge about environmental transformation products of organic contaminants is important for chemical risk assessment performed by regulatory agencies, research scientists, and chemical manufacturers. Facing the challenge of an increasing number of synthetic organic compounds released to the environment with limited degradation data, a number of cheminformatics-based tools have been created to predict transformation products to address the knowledge gap. Therefore, the goal of this study is to build and evaluate the first freely available in silico predictive tool for direct photolysis of organic contaminants in sunlit aquatic systems. This tool will be made available through the CTS website developed by the United States Environmental Protection Agency (U.S. EPA).

Description:

Cheminformatics-based applications to predict transformation pathways of environmental contaminants are useful to quickly prioritize contaminants with potentially toxic/persistent products. Direct photolysis can be an important degradation pathway for sunlight-absorbing compounds in aquatic systems. In this study, we developed the first freely available direct phototransformation pathway predictive tool, which uses a rule-based reaction library. Journal publications studying diverse contaminants (such as pesticides, pharmaceuticals, and energetic compounds) were systematically compiled to encode 155 reaction schemes into the reaction library. The execution result of this predictive tool was internally evaluated against 390 compounds from the compiled journal publications and externally evaluated against 138 compounds from the regulatory reports. The recall (sensitivity) and precision (selectivity) were 0.62 and 0.35, respectively, for internal evaluation, and 0.56 and 0.20, respectively, for external evaluation, when only the products formed from the first reaction step were counted. This predictive tool could help to narrow the data gaps in chemical registration/evaluation and inform future experimental studies.

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