FIDDLE:一种深度学习方法,用于从双重质谱中预测化学公式
Yuhui Hong1, Sujun Li1, Yuzhen Ye1
1Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, IN, USA.
Nature communications
|December 12, 2025
概括
深度学习工具FIDDLE可以从质谱数据中增强分子公式的识别. 这种方法显著提高了高通量分析的准确性和速度,优于现有的计算方法.
科学领域:
- 计算化学是一种计算化学.
- 质谱测量质量谱测量
- 生物信息学是一种生物信息学.
背景情况:
- 通过双重质谱测量进行分子鉴定对于小分子分析至关重要.
- 当前的计算公式识别方法在准确性,速度和可扩展性方面面临挑战,特别是对于较大的分子,阻碍了高吞吐量工作流.
研究的目的:
- 开发和评估FIDDLE (Formula IDentification by Deep LEarning),一种基于深度学习的新方法,用于加速和改进分子公式识别.
- 对各种质谱数据集的最新方法进行FIDDLE性能评估.
主要方法:
- 开发了FIDDLE,这是一个深度学习模型,从Q-TOF和Orbitrap仪器中训练了超过38,000个分子和100万个MS/MS光谱.
- 评估了FIDDLE在公式识别中的速度和准确性.
- 将FIDDLE的性能与自上而下的 (SIRIUS) 和自下而上的 (BUDDY) 计算方法进行了比较.
主要成果:
- 与现有方法相比,FIDDLE可以加快配方识别的速度超过10倍.
- 在培训数据上实现了88.3%的top-1准确率和93.6%的top-5准确率.
- 在准确度方面,SIRIUS和BUDDY的表现超过了10%.
- 在外部代谢学数据集中,FIDDLE在前五名中获得了75.1% (正离子模式) 和66.2% (负离子模式) 的精度,与SIRIUS和BUDDY相结合时改善到80.0%和73.8%.
结论:
- 在计算分子公式识别方面,FIDDLE提供了显著的进步.
- 深度学习方法提高了基于高通量质谱的分析的速度和准确性.
- 在改善代谢学研究和其他小分子识别工作流程方面,FIDDLE显示出前景.
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