CBMAFF-Net:一种基于NMR的智能非目标查方法,用于新型精神活性物质
Xiaoshan Zheng1, Boyi Tang2,3, Peng Xu3,4
1School of Science, China Pharmaceutical University, Nanjing 210009, China.
Analytical chemistry
|November 13, 2024
概括
通过使用核磁共振 (NMR) 数据,CBMAFF-Net在复杂的中准确识别新的精神活性物质 (NPS). 这种智能选方法为NPS监管和早期预警系统提供了重大进步.
科学领域:
- 法医化学 法医化学
- 分析化学 分析化学
- 计算化学计算化学
背景情况:
- 新的精神活性物质 (NPS) 迅速发展,挑战了传统的识别方法.
- 发作中复杂的NPS组合阻碍了有效的调节和早期预警.
- 基于数据库的搜索与新型和混合NPS样本作斗争.
研究的目的:
- 开发一种智能选方法,用于快速分类未知的没收物质.
- 解决传统方法在识别复杂的NPS发作的局限性.
- 利用核磁共振 (NMR) 数据进行增强的NPS检测.
主要方法:
- 拟议的CBMAFF-Net (CNN BiLSTM多步注意力特征融合网络).
- 使用卷积神经网络 (CNN) 和双向长短期记忆 (BiLSTM) 来提取特征.
- 利用了对13C NMR和1H NMR数据加权特征融合的注意力机制.
主要成果:
- 在模拟数据上,CBMAFF-Net实现了99.8%的准确性和99.8%的F1得分.
- 该模型在42个实际的NPS扣押案例中显示了97.6%的准确性.
- 显著优于传统的基于数据库的相似性搜索算法.
结论:
- 对于快速选和分类NPSs,CBMAFF-Net显示出相当大的希望.
- 该方法提高了识别复杂NPS组合物的能力.
- 提供了一个强大的工具,用于对NPS进行法医分析和监管控制.
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