MIST-CF:从双重质谱的化学公式推断
Samuel Goldman1, Jiayi Xin2, Joules Provenzano3
1Computational and Systems Biology, Massachusetts Institute of Technology, Cambridge 02139, Massachusetts, United States.
Journal of chemical information and modeling
|September 19, 2023
概括
通过从光谱中学习,MIST-CF从质谱数据中预测化学公式,在没有复杂的碎片树的情况下,精度提高了10%. 这种数据驱动的方法提高了研究中的代谢物识别.
科学领域:
- 代谢学 代谢学 代谢学
- 计算化学的计算化学
- 生物信息学是一种生物信息学.
背景情况:
- 双重质谱 (MS/MS) 数据的化学公式注释对于阐明代谢物结构至关重要.
- 目前的方法依赖于耗时,专有和专家参数化的碎片树构建和评分.
研究的目的:
- 开发一个先进的计算框架,MIST-CF,用于从MS/MS数据中准确地预测化学公式.
- 通过采用数据驱动的深度学习方法来规避碎片化树构建的需要.
主要方法:
- 将以前的扩展频谱变压器方法转化为一个以能源为基础的建模框架,名为MIST-CF.
- 利用公式转换器神经网络架构来学习化学公式的排名和引入任务.
- 在大型开放访问数据库和CASMI2022挑战数据集上训练和评估模型.
主要成果:
- 与大型开放访问数据库上的其他神经网络架构相比,在top 1准确度方面实现了10%的绝对改进.
- 在没有手动策划的情况下,在CASMI2022挑战 (积极模式) 中表现出与获胜项目的几乎相当的性能.
- 成功地利用MS2碎片峰值来预测MS1前体化学公式,使用数据驱动学习.
结论:
- 在代谢学中,MIST-CF为化学公式预测提供了一个强大的,数据驱动的策略.
- 该模型绕过了传统的资源密集型方法,直接从光谱数据中学习.
- 这种方法显著提高了从MS/MS数据中代谢物识别的效率和准确性.
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