Su An, Cheng Yingying, Zhang Chengwei, Yang Yun-Fang, She Yuan-Bin, Rajan Krishna
State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, Key Laboratory of Green Chemistry-Synthesis Technology of Zhejiang Province, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China; Key Laboratory of Pharmaceutical Engineering of Zhejiang Province, Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, PR China.
State Key Laboratory Breeding Base of Green Chemistry-Synthesis Technology, Key Laboratory of Green Chemistry-Synthesis Technology of Zhejiang Province, College of Chemical Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, China; Key Laboratory of Pharmaceutical Engineering of Zhejiang Province, Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, Zhejiang University of Technology, Hangzhou, Zhejiang 310014, PR China.
Sci Total Environ. 2024 Apr 15;921:171229. doi: 10.1016/j.scitotenv.2024.171229. Epub 2024 Feb 23.
Since structural analyses and toxicity assessments have not been able to keep up with the discovery of unknown per- and polyfluoroalkyl substances (PFAS), there is an urgent need for effective categorization and grouping of PFAS. In this study, we presented PFAS-Atlas, an artificial intelligence-based platform containing a rule-based automatic classification system and a machine learning-based grouping model. Compared with previously developed classification software, the platform's classification system follows the latest Organization for Economic Co-operation and Development (OECD) definition of PFAS and reduces the number of uncategorized PFAS. In addition, the platform incorporates deep unsupervised learning models to visualize the chemical space of PFAS by clustering similar structures and linking related classes. Through real-world use cases, we demonstrate that PFAS-Atlas can rapidly screen for relationships between chemical structure and persistence, bioaccumulation, or toxicity data for PFAS. The platform can also guide the planning of the PFAS testing strategy by showing which PFAS classes urgently require further attention. Ultimately, the release of PFAS-Atlas will benefit both the PFAS research and regulation communities.
由于结构分析和毒性评估未能跟上新型全氟和多氟烷基物质（PFAS）的发现速度，因此迫切需要对PFAS进行有效的分类和分组。在本研究中，我们展示了PFAS-Atlas，这是一个基于人工智能的平台，包含一个基于规则的自动分类系统和一个基于机器学习的分组模型。与先前开发的分类软件相比，该平台的分类系统遵循经济合作与发展组织（OECD）对PFAS的最新定义，并减少了未分类PFAS的数量。此外，该平台采用深度无监督学习模型，通过对相似结构进行聚类并链接相关类别来可视化PFAS的化学空间。通过实际应用案例，我们证明PFAS-Atlas可以快速筛选PFAS的化学结构与持久性、生物累积性或毒性数据之间的关系。该平台还可以通过显示哪些PFAS类别迫切需要进一步关注来指导PFAS测试策略的规划。最终，PFAS-Atlas的发布将使PFAS研究和监管领域都受益。
