一个大型语言模型,用于导出光谱嵌入,以便在质谱学中准确识别化合物
Yang Xu1, Yixiao Ma2, Weijie Xu2
1State Key Laboratory for Novel Software Technology & School of Artificial Intelligence, Nanjing University, Nanjing, China. xuyang@lamda.nju.edu.cn.
Communications chemistry
|November 4, 2025
概括
使用大型语言模型的新方法LLM4MS显著改进了从质谱数据中识别化学化合物的方法. 这种方法通过提高光谱匹配的准确性和速度来加速发现.
科学领域:
- 分析化学 分析化学
- 计算化学的计算化学
- 科学中的人工智能.
背景情况:
- 精确识别复杂混合物的化学成分对于科学进步至关重要.
- 质谱是一项关键工具,但从光谱中识别化合物仍然是一个重大挑战.
- 现有的方法难以应对光谱数据的规模和复杂性.
研究的目的:
- 引入LLM4MS,一种利用大语言模型 (LLM) 的新方法,用于在质谱学中增强化合物识别.
- 为了提高对已知化学化合物的质谱匹配的准确性和速度.
- 将化学专家知识纳入光谱嵌入过程.
主要方法:
- 开发了LLM4MS,一种利用LLM中的知识来产生歧视性光谱嵌入的方法.
- 将潜在的化学专家知识纳入LLM4MS框架.
- 使用NIST23库对百万级in-silico库进行LLM4MS性能评估.
主要成果:
- 在NIST23测试组中,LLM4MS在NIST23测试组中实现了66.3%的回忆@1精度和92.7%的回忆@10精度.
- 与最先进的 Spec2Vec 方法相比,其准确性得到了 13.7% 的改善.
- 实现了超快的质谱匹配,每秒近15000个查询.
结论:
- LLM4MS显著提高了质谱学中的化合物识别能力.
- 该方法通过提供更准确和更快的光谱匹配来加速化学发现.
- LLM4MS代表了应用人工智能的突破,用于复杂的分析化学问题.
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