马西斯坦特:通过SELFIES编码通过EI-MS光谱对De Novo分子结构预测的深度学习模型
John Mommers1, Lazar Barta2, Marcin Pietrasik2
1Envalior, Engineering Materials, Urmonderbaan 22, 6167 RD Geleen, the Netherlands.
Journal of chromatography. A
|July 24, 2025
概括
科学家们开发了MASSISTANT,这是一个深度学习模型,用于从质谱数据中识别未知的分子. 这种人工智能工具可以从EI-MS光谱中预测分子结构,从而提高分析的准确性和速度.
科学领域:
- 分析化学 分析化学
- 计算化学计算化学
- 人工智能的人工智能
背景情况:
- 气色谱-电子冲击质谱 (GC-EI-MS) 对于识别挥发性和半挥发性化合物至关重要.
- 对未知化合物的EI-MS光谱的手动解释是耗时的,需要大量的专业知识.
- 现有的EI-MS数据库不完整,限制了新型分子结构的识别.
研究的目的:
- 介绍 MASSISTANT,这是一个新的深度学习模型,用于从EI-MS光谱中进行新的分子结构预测.
- 为了利用SELFIES编码来直接生成分子结构.
- 为质谱科学家提供一个自动化工具,用于增强化合物识别.
主要方法:
- 开发了一个名为MASSISTANT的深度学习模型.
- 使用SELFIES编码来表示分子结构.
- 训练模型在低分辨率EI-MS600Da以下化合物的光谱上.
- 评估包括NIST数据库在内的精选和广泛数据集的性能.
主要成果:
- 马西斯坦直接从EI-MS光谱中预测分子结构.
- 性能因数据集策划而异,在集中数据集上达到高达54%的准确预测 (塔尼莫托得分=1),而在完整的NIST数据集上为10%.
- 该模型展示了深度神经网络在解释复杂的碎片化模式方面的能力.
结论:
- 像MASSISTANT这样的深度学习模型可以有效地从EI-MS数据中生成化学有效的分子结构.
- 马西斯坦提供了一个强大的工具来帮助科学家解释质谱分析.
- 这种方法增强了GC-EI-MS中的分子结构,亚结构和功能组的阐明.
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