Research Progress on Machine Learning Assisted Non-Targeted Screening Strategy for Identification of Fentanyl Analogs.

In recent years, the types and quantities of fentanyl analogs have increased rapidly. It has become a hotspot in the illicit drug control field of how to quickly identify novel fentanyl analogs and to shorten the blank regulatory period. At present, the identification methods of fentanyl analogs that have been developed mostly rely on reference materials to target fentanyl analogs or their metabolites with known chemical structures, but these methods face challenges when analyzing new compounds with unknown structures. In recent years, emerging machine learning technology can quickly and automatically extract valuable features from massive data, which provides inspiration for the non-targeted screening of fentanyl analogs. For example, the wide application of instruments like Raman spectroscopy, nuclear magnetic resonance spectroscopy, high resolution mass spectrometry, and other instruments can maximize the mining of the characteristic data related to fentanyl analogs in samples. Combining this data with an appropriate machine learning model, researchers may create a variety of high-performance non-targeted fentanyl identification methods. This paper reviews the recent research on the application of machine learning assisted non-targeted screening strategy for the identification of fentanyl analogs, and looks forward to the future development trend in this field.近年来，芬太尼类物质的种类和数量快速增长，如何对新型芬太尼类物质进行快速鉴别以缩短监管空窗期是当前禁毒工作的热点。目前已开发的芬太尼类物质识别鉴定方法多依赖标准物质，靶向分析特定已知化学结构的芬太尼类物质或其代谢物，而对于结构未知的新型化合物则束手无策。近年来兴起的机器学习技术能够快速地从海量数据中自动提取有价值的特征规律，为芬太尼类物质的非靶向筛查研究提供新的思路。例如拉曼光谱、核磁共振波谱、高分辨质谱等仪器的广泛应用能够最大程度地挖掘样品中与芬太尼类物质相关的特征数据，将这些数据辅以合适的机器学习模型，将创建多种高性能的非靶向芬太尼类物质识别鉴定方法。本文对近年来开发的机器学习辅助非靶向筛查策略用于芬太尼类物质识别鉴定的研究进行总结回顾，并展望该领域未来的发展趋势。.