Department of Laboratory Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan.
Department of Laboratory Medicine, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan.
Anal Chem. 2024 Mar 26;96(12):4835-4844. doi: 10.1021/acs.analchem.3c05019. Epub 2024 Mar 15.
The rapid proliferation of new psychoactive substances (NPS) poses significant challenges to conventional mass-spectrometry-based identification methods due to the absence of reference spectra for these emerging substances. This paper introduces PSMS, an AI-powered predictive system designed specifically to address the limitations of identifying the emergence of unidentified novel illicit drugs. PSMS builds a synthetic NPS database by enumerating feasible derivatives of known substances and uses deep learning to generate mass spectra and chemical fingerprints. When the mass spectrum of an analyte does not match any known reference, PSMS simultaneously examines the chemical fingerprint and mass spectrum against the putative NPS database using integrated metrics to deduce possible identities. Experimental results affirm the effectiveness of PSMS in identifying cathinone derivatives within real evidence specimens, signifying its potential for practical use in identifying emerging drugs of abuse for researchers and forensic experts.
新型精神活性物质（NPS）的迅速扩散对基于常规质谱的鉴定方法构成了重大挑战，因为这些新兴物质缺乏参考光谱。本文介绍了 PSMS，这是一个基于人工智能的预测系统，专门用于解决识别新出现的不明非法药物的局限性。PSMS 通过枚举已知物质的可行衍生物来构建合成 NPS 数据库，并使用深度学习生成质谱和化学指纹。当分析物的质谱与任何已知参考物质都不匹配时，PSMS 会同时使用综合指标来检查化学指纹和质谱与假定的 NPS 数据库之间的关系，以推断可能的身份。实验结果证实了 PSMS 在识别实际证据样本中的卡他酮衍生物方面的有效性，表明其在识别研究人员和法医学专家新兴滥用药物方面具有实际应用潜力。
