深度学习预测电子喷雾电离的化学衍生分子的双重质谱
Bin Chen1, Hailiang Li1, Rongfu Huang2
1Key Laboratory of Green Chemistry & Technology of Ministry of Education, College of Chemistry, Sichuan University, Chengdu, Sichuan, 610064, China.
Nature communications
|September 27, 2024
概括
深度学习准确地预测化学衍生分子 (CDM) 的质谱,克服了化学分析中的关键障碍. 这种进步有助于发现未知的污染物和化学物质,这些化学物质可能会对健康产生影响.
科学领域:
- 分析化学 分析化学
- 计算化学计算化学
- 环境科学 环境科学
背景情况:
- 化学衍生增强液体染色学-质谱学 (LC-MS) 分析复杂的混合物.
- 一个主要的限制是缺乏化学衍生分子 (CDM) 的参考光谱.
研究的目的:
- 开发一个深度学习模型 (DeepCDM),用于预测CDM的电喷射电离联质谱.
- 建立一个 in silico 光谱库 (DnsBank) 对 dansylated 分子.
主要方法:
- 利用转移学习,将通用频谱预测模型适应于CDM.
- 通过实验获得的双重质谱的小数据集来训练模型.
- 通过预测dansilated分子的光谱来证明DeepCDM的有效性.
主要成果:
- DeepCDM实现了CDM的高可预测性,大大改善了通用模型.
- 专门的光谱图书馆DnsBank提高了dansylated化合物的准确注释率.
- 成功应用了在工业废水中识别危险污染物的方法.
结论:
- 在非目标分析中,DeepCDM为CDM的高通量识别提供了一种多功能解决方案.
- 这种方法有助于发现具有潜在健康风险的未知人为化学物质.
- DeepCDM可以适应各种类型的CDM,扩大其适用性.
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