From High Resolution Tandem Mass Spectrometry to Pollutant Toxicity AI-Based Prediction: A Case Study of 7 Endocrine Disruptors Endpoints

作者：Xin Zhang, Xiaoxiao Han, Tongtong Xiang, Yanna Liu, Wenxiao Pan, Qiao Xue, Xian Liu, Jianjie Fu, Aiqian Zhang, Guangbo Qu, Guibin Jiang · 发表于：Environmental Science & Technology · 年份：2025 · DOI：10.1021/acs.est.4c11417 · 被引用次数：13 · 研究领域：Pesticide Residue Analysis and Safety、Computational Drug Discovery Methods、Machine Learning and Data Classification

Based on high-resolution mass spectrometry (HRMS), nontarget analysis (NTA) can rapidly identify and characterize numerous hazardous substances in complex environmental samples. However, the intricate identification process often results in the underutilization of many mass spectrometry features. Even when chemical structures are identified, their toxicological effects and health outcomes may remain unknown. To address these challenges, this study introduces MSFragTox, a novel approach that leverages the rich fragmentation spectra inherent in high resolution tandem mass spectrometry (MS/MS) to directly predict toxicity. This method integrates MS/MS data with high-throughput screening (HTS) assays, focusing on seven endocrine disruption-related endpoints from Tox21, and uses MS-derived fingerprints: substructure fragmentation probability vectors to construct toxicity predictions using machine learning algorithms. The best model demonstrated robust performance with an average area under the receiver operating characteristic curve (AUROC) of 0.845 on the test set, outperforming models based on traditional molecular fingerprints and descriptors. Additionally, a web client (http://ms.envwind.site:8500) is provided for users to screen toxicity based on chemical MS/MS data. Furthermore, in-depth analyses of commonalities and differences in substructures reveal the mechanisms underlying across toxicity endpoints. Using MSFragTox, we validated the potential endocrine-disrupting effect...