机器学习和多层分子网络辅助的查寻找芬太尼化合物
Changzhi Shi1,2, Wanli Li1, Yang Wang1,3
1Shanghai Institute for Doping Analyses, Shanghai University of Sport, Shanghai 200438, China.
Science advances
|September 5, 2025
概括
一个新的选平台Fentanyl-Hunter使用机器学习和质谱学准确识别芬太尼化合物. 该工具在各种样本中检测到芬太尼及其代谢物,揭示了全球广泛存在的情况.
科学领域:
- 法医化学
- 分析化学
- 计算化学
背景情况:
- 芬太尼及其类型对全球健康构成重大风险.
- 准确识别芬太尼化合物对于公共健康和安全至关重要.
- 现有的芬太尼检测方法的范围和准确性可能有限.
研究的目的:
- 推出芬太尼猎人,一种用于芬太尼识别的新选平台.
- 用机器学习和分子网络来提高芬太尼的检测.
- 在各种样本类型中评估芬太尼化合物的流行程度.
主要方法:
- 使用772个芬太尼光谱的机器学习分类器的开发.
- 用于分类的光谱结合特征工程的实施.
- 基于光谱相似性和质量距离构建多层分子网络.
- 在生物,环境和公共质谱数据集中应用芬太尼-亨特.
主要成果:
- 该分类模型达到0.868 ±0.02的高F1分数.
- 多层网络成功覆盖了超过87%的已知芬太尼化合物.
- 芬太尼-亨特在生物和环境样本中发现了芬太尼成员,包括来自四种衍生物的35种代谢物.
- 诺芬太尼被确定为废水中主要的芬太尼化合物.
- 在8个国家的250多个样本中,对605,000多个多发性硬化病例的回顾性分析显示了芬太尼,苏芬太尼,诺芬太尼或雷米芬太尼酸.
结论:
- 芬太尼猎人是一个有效的平台,用于准确识别和注释芬太尼化合物.
- 该平台展示了芬太尼及其代谢物在全球广泛存在的潜力.
- 这种工具可以帮助与滥用芬太尼相关的公共卫生监测和法医调查.
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