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在基于变压器的小型化质谱仪中对未知的分子进行结构注释,启用了碎片树方法
Yiming Yang1, Shuang Sun1, Shuyuan Yang1
1Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China.
Communications chemistry
|May 13, 2024
概括
我们开发了一种新的AI方法,叫做变压器启用碎片树 (TeFT),用于从质谱数据中识别分子结构. 这个工具准确地预测结构,即使是来自便携式设备的低分辨率光谱.
科学领域:
- 分析化学 分析化学
- 计算化学的计算化学
- 化学中的人工智能.
背景情况:
- 使用双重质谱法 (MS/MS) 对小分子的结构注释是一个重大挑战,特别是用于现场分析的小型仪器.
- 准确的分子结构确定对于各种应用至关重要,包括药物发现和天然产品分析.
研究的目的:
- 为了引入一种新的深度学习方法,变压器启用了碎片树 (TeFT),仅从质谱谱光谱生成新的分子结构.
- 评估TeFT在各种数据集上的性能及其对现实世界低分辨率质谱数据的适用性.
主要方法:
- 开发TeFT方法,将各种碎片树模型与深度学习转换器模块集成在一起.
- 使用开源光谱数据库验证TeFT用于一般分子结构识别.
- 应用TeFT来分析微型质谱仪的低分辨率光谱,重点关注黄类酒精和传统中医药.
主要成果:
- 在开源数据库评估中,TeFT在识别大多数分子结构方面表现得非常准确.
- 该方法使用小型化质谱仪与低分辨率光谱实现了16种黄醇中8种的完整结构预测.
- TeFT成功证实了安伟阳囊中的一种化合物的结构,这是一种中国医学.
结论:
- 变压器启用碎片树 (TeFT) 方法提供了一个强大的方法,用于从质谱数据中进行新的分子结构注释.
- 对于具有较低质量分辨率的现场质谱应用,TeFT特别有效,特别是当碎片化规则清晰时.
- 这种由人工智能驱动的方法对加速复杂混合物和自然产品的结构阐明具有前景.
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