Advancing toxicity studies of per- and poly-fluoroalkyl substances (pfass) through machine learning: Models, mechanisms, and future directions.

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第一作者： Lingxuan,Meng

第一单位： Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China.

作者： Lingxuan,Meng ^[1] ; Beihai,Zhou ^[1] ; Haijun,Liu ^[2] ; Yuefang,Chen ^[3] ; Rongfang,Yuan ^[1] ; Zhongbing,Chen ^[4] ; Shuai,Luo ^[1] ; Huilun,Chen ^[5]

作者单位： Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China. ^[1] School of Resources and Environment, Anqing Normal University, Anqing, China. Electronic address: liuhj@aqnu.edu.cn. ^[2] Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China. Electronic address: chenyuefang@ustb.edu.cn. ^[3] Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamýcká 129, 16500 Praha-Suchdol, Czech Republic. Electronic address: chenz@fzp.czu.cz. ^[4] Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China. Electronic address: chenhuilun@ustb.edu.cn. ^[5]

关键词 Machine learning PFASs Toxicity evaluation Toxicity prediction

主题词氟碳化合物(Fluorocarbons);量化构效关系(Quantitative Structure-Activity Relationship);环境污染物(Environmental Pollutants);毒性试验(Toxicity Tests);人类(Humans);生态毒理学(Ecotoxicology)

DOI 10.1016/j.scitotenv.2024.174201

PMID 38936709

发布时间 2024-07-20

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