化学嵌入:使用增强的MS/MS数据和多维分子嵌入进行代谢物识别的深度学习框架
Muhammad Faizan-Khan1, Roger Giné1,2, Josep M Badia1
1Department of Electronic Engineering, Universitat Rovira i Virgili, Tarragona 43007, Spain.
Briefings in bioinformatics
|February 13, 2026
概括
ChemEmbed使用化学结构嵌入来增强质谱 (MS/MS) 光谱,以改进代谢物识别. 这种机器学习方法在识别代谢学研究中未知的化合物方面显著优于现有的工具.
科学领域:
- 计算化学是一种计算化学.
- 代谢学 代谢学 代谢学
- 生物信息学是一种生物信息学.
背景情况:
- 质谱学 (MS/MS) 对代谢学至关重要,但识别未知的化合物受到光谱图书馆覆盖范围的限制.
- 现有的机器学习方法难以应对MS/MS光谱数据和代谢物结构的复杂性和稀疏性.
研究的目的:
- 开发一种新的机器学习方法,ChemEmbed,用于在MS/MS数据中增强代谢物识别.
- 通过整合化学结构信息来解决当前光谱注释工具的局限性.
主要方法:
- ChemEmbed使用化学结构的多维,连续向量表示.
- 通过将多个碰撞能量的数据合并并结合计算的中性损失来增强MS/MS光谱.
- 一个卷积神经网络 (CNN) 处理了增强的光谱和结构数据.
主要成果:
- 在超过42%的病例中,ChemEmbed正确识别了候选代谢物,在超过76%的病例中,在前五名中.
- 该方法在CASMI 2016年和2022年基准中与Sirius 6相比显示出更高的性能.
- 化学嵌入成功地在注释反复未识别光谱 (ARUS) 数据集中识别了25种以前未知的化合物.
结论:
- 化学嵌入提供了一个强大的和可扩展的解决方案,以加速代谢物识别在非定向质谱.
- 化学结构嵌入与增强的MS/MS光谱的整合代表了计算代谢学中的重大进步.
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