CSU-MS:一个对比的学习框架,用于从MS/MS光谱到分子结构的跨模态化合物识别
Ting Xie1, Hailiang Zhang1, Qiong Yang1
1College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, PR. China.
CSU-MS2 统一了使用对比学习的双重质谱 (MS/MS) 光谱和分子结构. 这种新的框架显著提高了复杂混合物中化合物识别的准确性,超过了现有的方法.
科学领域:
- 计算化学是一种计算化学.
- 生物信息学是一种生物信息学.
- 分析化学是一种分析化学.
背景情况:
- 双重质谱 (MS/MS) 对于识别复杂混合物中的化合物至关重要.
- 传统的光谱匹配方法受到图书馆覆盖范围和算法的限制.
- 准确的化合物识别对于代谢学和药物发现至关重要.
研究的目的:
- 开发一个新的框架,CSU-MS2,用于统一MS/MS光谱和分子结构.
- 提高复杂混合物中化合物识别的准确性和效率.
- 提供一种多功能工具,用于分子候选物的交叉模式检索.
主要方法:
- CSU-MS2采用交叉模式的对比学习来弥合MS/MS频谱和分子结构.
- 一个外部空间注意力聚合 (ESA) 模块动态调整光谱和结构特征.
- 该框架在in-silico数据上进行了预训练,并在实验MS/MS数据上进行了微调.
主要成果:
- CSU-MS2在超过100万个化合物的库中实现了75.45%的RECALL@1,超过了现有方法.
- 对各种数据集的验证表明了强大的概括性,血液代谢物的RECALL@10为91.67%.
- 该框架在化合物识别方面显著优于CFM-ID,SIRIUS,MetFrag和CMSSP.
结论:
- CSU-MS2为高通量化合物识别提供了一个变革性的解决方案.
- 该框架的统一嵌入空间可以直接检索分子候选物.
- 公共可访问的代码,模型和全面的数据库 (SSFDB) 促进了在代谢学和相关领域的广泛采用.
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