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反应库预测日光下水生系统中环境有机污染物的直接光化学转化产物。

Reaction Library to Predict Direct Photochemical Transformation Products of Environmental Organic Contaminants in Sunlit Aquatic Systems.

机构信息

Oak Ridge Institute for Science and Education (ORISE), hosted at United States Environmental Protection Agency, Athens, Georgia 30605, United States.

Center for Environmental Measurement and Modeling, United States Environmental Protection Agency, Athens, Georgia 30605, United States.

出版信息

Environ Sci Technol. 2020 Jun 16;54(12):7271-7279. doi: 10.1021/acs.est.0c00484. Epub 2020 May 26.

Abstract

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.

摘要

基于 cheminformatics 的应用程序可用于预测环境污染物的转化途径,这对于快速优先考虑具有潜在毒性/持久性产物的污染物非常有用。直接光解可能是水生系统中吸收阳光的化合物的重要降解途径。在这项研究中,我们开发了第一个免费的直接光转化途径预测工具,该工具使用基于规则的反应库。系统地编译了研究各种污染物(如农药、药物和含能化合物)的期刊出版物,将 155 种反应方案编码到反应库中。该预测工具的执行结果针对来自编译期刊出版物的 390 种化合物进行了内部评估,并针对来自监管报告的 138 种化合物进行了外部评估。仅当计算第一个反应步骤形成的产物时,内部评估的召回率(灵敏度)和精度(选择性)分别为 0.62 和 0.35,外部评估的召回率和精度分别为 0.56 和 0.20。该预测工具可以帮助缩小化学品注册/评估中的数据差距,并为未来的实验研究提供信息。

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