小说精神活性物质:用人工智能杀死龙
David S Wishart1,2,3,4, Prashanthi Kovur1, Yamilé López-Hernández1,5
1Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada.
Therapeutic drug monitoring
|December 19, 2025
概括
人工智能 (AI) 通过预测结构和光谱来帮助识别新型精神活性物质 (NPS),改善法医毒理学. 这项技术加快了识别过程,增强了对新出现的NPS威胁的公共卫生反应.
科学领域:
- 法医毒理学 法医毒理学
- 计算化学的计算化学
- 药物监管监管机构 药物监管机构 药物监管机构
背景情况:
- 新型精神活性物质 (NPS) 对法医,医疗保健和监管系统构成挑战.
- 由于缺乏参考标准,传统的分析方法难以识别新合成的NPS.
- 人工智能 (AI) 和机器学习为NPS识别和表征提供了潜在的解决方案.
研究的目的:
- 审查人工智能和机器学习在应对NPS识别挑战中的应用.
- 探索基于AI的方法,集成到NPS分析的法医工作流程中.
- 检查与法医毒理学相关的新兴AI技术.
主要方法:
- 审查当前的法医工作流程和AI集成,包括深度学习,化学语言模型和光谱预测工具.
- 强调DarkNPS框架用于生成可信的NPS结构和in silico MS/MS光谱.
- 检查新兴的人工智能技术,如变压器,图形神经网络和多式联络框架.
主要成果:
- 人工智能系统通过使结构生成和光谱预测能够在没有物理标准的情况下显著减少NPS识别的时间和资源.
- 暗NPS模型预测了超过890万个可信的NPS结构,预测了未来90%以上的NPS.
- 人工智能在体中生成的MS/MS光谱库与实验光谱具有很高的相似性,可以在75%-90%的病例中进行识别.
结论:
- 人工智能与分析化学的整合提高了NPS识别的速度,范围,精度和实用性.
- 人工智能代表了对新出现的NPS威胁的法医毒理学和化学监测的有希望的进步.
- 人工智能驱动的识别有助于准确诊断,及时治疗和改善公共卫生反应.
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