基于大型语言模型的复合碰撞截面预测
Zeyu Zhu1, Chengyi Xie2, Shaojie Lin1
1Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China.
Analytical chemistry
|September 1, 2025
概括
通过使用化学大语言模型 (CLLM),HyperCCS提高了离子移动性质谱的复合注释精度. 这种新的框架改善了碰撞截面 (CCS) 预测,优于现有的多种分子方法.
科学领域:
- 计算化学
- 分析化学
- 生物信息学
背景情况:
- 碰撞截面 (CCS) 对于离子移动性质谱 (IM-MS) 中精确的化合物识别至关重要.
- 目前的计算CCS预测方法在有限的数据和不充分的多式联网功能处理方面扎,导致性能不足.
- 准确的CCS预测对于构建IM-MS应用的大规模化合物数据库至关重要.
研究的目的:
- 开发一个新的计算框架,HyperCCS,用于准确的碰撞截面 (CCS) 预测.
- 利用化学大型语言模型 (CLLM) 来捕获复杂的分子信息.
- 有效地整合多式联网功能,以提高IM-MS的预测性能.
主要方法:
- 在广泛的SMILES序列上预先训练的化学大型语言模型 (CLLM) 进行了微调.
- 开发了一种跨模态特征融合模块,将CLLM衍生特征与其他异质数据集成.
- 在基准数据集 (METLIN-CCS,AllCCS2) 和内部实验数据上评估HyperCCS.
主要成果:
- 超CCS在各种分子质量,附加型和离子模式中展示了强大的CCS预测,超过了现有的方法.
- 该框架准确地分辨出异构体,并将预测推断到实验数据上的高质量分析物.
- SHAP分析和废弃研究证实了CLLM特征和聚变机制的显著贡献.
结论:
- 在IM-MS的计算CCS预测方面,HyperCCS提供了显著的进步.
- 集成CLLM和跨模式融合有效地解决了先前预测模型的局限性.
- HyperCCS为代谢学和结构生物学研究提供了高吞吐量,可适应的计算工具.
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