神经光谱预测用于结构光与并联质谱学
bioRxiv : the preprint server for biology
|June 12, 2025
概括
一个新的深度学习模型ICEBERG模拟质谱碎片化,以识别未知的分子结构. 该工具提高了复合注释的准确性,加速了各种领域的科学发现.
科学领域:
- 分析化学 分析化学
- 计算化学计算化学
- 生物化学 生物化学
背景情况:
- 非定位的双重质谱 (MS/MS) 对于科学发现至关重要.
- 在MS/MS中区分异构结构是各种科学领域的重大挑战.
研究的目的:
- 为了介绍ICEBERG,一个模拟MS/MS碎片化的几何深度学习模型.
- 为了提高分子结构阐明和化合物注释的准确性和速度.
主要方法:
- 开发了ICEBERG,这是一个模拟质谱学中的碰撞诱导解离 (CID) 的深度学习模型.
- 产生了化学上可信的碎片及其相对强度,考虑到碰撞能量和极性.
- 将ICEBERG预测集成到一个管道中,根据MS/MS光谱相似性对候选结构进行排名.
主要成果:
- 在复合注释中实现了最先进的性能,在NIST'20 [M+H]+引证子集上达到40%的top-1精度.
- 92%的正确结构在同一数据集的前十个预测中被识别出来.
- 在识别生物标记物,农药降解产品,反应选和生物合成途径方面证明了成功的应用.
结论:
- ICEBERG提供一种基于深度学习的化学解释方法,用于结构阐释.
- 这种范式使复杂混合物的分子快速注释成为可能,推动科学发现.
- 该模型大大克服了使用MS/MS数据区分等离体结构的挑战.
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