HDSE-MS:通过层次距离结构编码对小分子进行并列质谱预测
Zhangqiang Liu1, Bingyi Wang2, Congcong Yang3
1School of Information Science and Engineering, Yunnan University, Kunming 650500, China.
Analytical chemistry
|December 9, 2025
概括
通过准确预测双重质谱 (MS/MS) 光谱,HDSE-MS 提高了在代谢学中的小分子识别. 这种新的方法增强了分子表示,以更好地阐明结构.
科学领域:
- 计算化学计算化学
- 分析化学 分析化学
- 生物信息学是一种生物信息学.
背景情况:
- 双重质谱 (MS/MS) 对于在代谢学中识别小分子至关重要.
- 有限的实验光谱库阻碍了基于MS的准确识别.
- 现有的频谱预测方法由于表示和枚举的局限性而难以通用.
研究的目的:
- 开发一种先进的MS/MS频谱预测模型,用于增强小分子识别.
- 提高频谱预测方法的分子表示能力.
- 解决当前预测模型中的概括性问题.
主要方法:
- 拟议的HDSE-MS,将传递信息的神经网络 (MPNN) 与变压器架构集成在一起.
- 采用分层距离结构编码 (HDSE),用于多层集群结构的图形粗化.
- 编码了层次距离作为变压器中的结构偏差,以建模子结构和远程依赖.
主要成果:
- 在高光谱相似度 (0.759,0.567,0.483) 的NIST23,MoNA和MassBank数据集上,HDSE-MS表现出卓越的性能.
- 在CASMI2022测试套件上获得了220.8的排名和0.098的Top-1准确度.
- 在实验中表现出强大的预测准确性,强大的概括性和可扩展性.
结论:
- HDSE-MS显著提升了MS/MS频谱预测,用于小分子识别.
- 该模型的层次结构编码增强了分子表示和预测能力.
- HDSE-MS为代谢学研究提供了一个可扩展和通用化的解决方案.
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