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JESTR:联合嵌入空间技术,用于对候选分子进行排名,以注释非目标代谢学数据
Apurva Kalia1, Yan Zhou Chen1, Dilip Krishnan2
1Department of Computer Science, Tufts University, Medford, MA 02155, United States.
Bioinformatics (Oxford, England)
|June 27, 2025
概括
作为一种新的工具,JESTR通过在一个联合空间中嵌入分子结构和光谱来改善代谢学注释,显著优于准确识别的现有方法.
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:
- 代谢学注释,将结构赋予质谱数据,仍然是一个重大挑战.
- 目前用于分子对光谱和光谱对分子指纹预测的方法产生较低的注释率.
研究的目的:
- 介绍JESTR,这是一种新的计算工具,旨在提高代谢学注释准确度.
- 为了利用分子和光谱的联合嵌入空间,超越了明确的特征构建.
主要方法:
- JESTR将分子结构和质谱嵌入到一个共享的表示空间中.
- 候选结构的排名是使用光谱嵌入和候选分子嵌入之间的共弦相似性.
- 该工具与多个数据集的现有注释方法进行了评估,包括与SIRIUS和CFM-ID进行比较.
主要成果:
- JESTR显著优于现有的注释工具,在 rank@[1-20] 中实现了平均55.5%-302.6%的改进.
- 在培训期间的规范化提高了5.72%的排名@1表现,并改善了目标分子和候选分子之间的歧视.
- 与预先训练的SIRIUS (31%的改进) 和CFM-ID (238%的改进) 模型相比,JESTR表现优越.
结论:
- JESTR为准确的代谢学注释提供了一个有希望的新方法.
- 这一进步有可能通过改善结构识别来释放对代谢组的更深入的见解.
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