从质量到类别:胺类的分类MS/MS光谱通过图形神经网络
Nir Cohen1, Shai Kendler2,3, Lilach Yishai-Aviram1
1Department of Analytical Chemistry, IIBR, Ness Ziona 7410001, Israel.
Journal of chemical information and modeling
|October 31, 2025
概括
一个新的图形神经网络 (GNN) 模型有效地识别了安非他胺衍生物,一种新型精神活性物质 (NPS),使用质谱学碎片化模式. 这种方法有助于在参考库不完整时检测新药.
科学领域:
- 法医化学 法医化学
- 计算化学的计算化学
- 分析化学 分析化学
背景情况:
- 鉴定新型精神活性物质 (NPS) 是具有挑战性的,因为它们在质谱图书馆中不存在.
- 液体染色学高分辨率联质谱 (LC-HR-MS/MS) 是NPS检测的一个关键技术.
- 胺衍生物是新兴NPS的一个重要子类.
研究的目的:
- 开发一个图形神经网络 (GNN) 模型,用于检测胺衍生物.
- 使用从LC-HR-MS/MS数据中的碎片化模式进行分类.
- 克服传统光谱库匹配新型化合物的局限性.
主要方法:
- 通过将质量/电荷 (m/z) 值转换为碎片化学组成,生成了一个数据集.
- 从光谱数据构建完全连接的图形.
- 开发并优化了GNN分类模型,以加权损失函数调整类不平衡.
- 包括碰撞能量作为一个信息参数.
主要成果:
- 在测试子集上,GNN模型实现了0.86的回忆率和0.11的精度.
- 该模型证明了在人体血中成功检测到一个尖的安非他胺衍生物.
- 在复杂的生物矩阵中达到较低的假阳性率 (1%).
结论:
- 开发的GNN模型显示,即使没有光谱图书馆匹配,它也可以用于识别安非他胺衍生物.
- 这种计算方法提高了NPS在法医和临床环境中的检测能力.
- 该模型在生物样本中的表现突显了其实际适用性.
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