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JESTR:联合嵌入空间技术,用于对候选分子进行排序,以对非目标代谢学数据进行注释

Apurva Kalia¹, Dilip Krishnan², Soha Hassoun^1,3

¹Department of Computer Science, Tufts University, Medford, MA 02155, USA.

|November 28, 2024

概括

JESTR是一种新的代谢学注释方法,将分子和光谱嵌入到一个联合空间中. 这种新的方法显著提高了将分子结构分配到质谱数据的准确性,超过了现有的工具.

科学领域:

代谢学代谢学代谢学
计算化学是一种计算化学.
生物信息学是一种生物信息学.

背景情况:

代谢学注释,即从质谱数据中识别分子结构的过程,面临着低准确率的挑战.
现有的分子对光谱和光谱对分子指纹预测方法在提高注释效率方面存在局限性.

研究的目的:

介绍JESTR,一种用于增强代谢学注释的新型计算范式.
通过利用分子和光谱的联合嵌入空间来解决当前注释技术的局限性.

主要方法:

JESTR将分子结构和质谱嵌入到一个共享的潜空间中.
候选分子结构与查询光谱对比,使用关节空间中的共弦相似性进行排名.
该模型在训练期间与候选分子进行调整,以改进性能.

主要成果:

与现有工具相比,JESTR在代谢学注释方面表现优越,在三个数据集中, rank@[1-5]的平均改善率为23.6% - 71.6%.
在训练期间的规范化显著提高了11.4%的排名@1性能,并提高了模型在正确和不正确的分子分配之间区分的能力.
JESTR方法为准确的代谢物识别提供了一个有希望的新途径.

结论:

JESTR代表了代谢学注释的重大进步,提供了更高的准确性和更好的分子结构识别.
联合嵌入空间范式为光谱数据分析的未来发展提供了一个强大的框架.
这项工作通过使代谢产物更可靠地识别,促进了对代谢组的更深入的了解.

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