一个频谱到结构的扩散模型,用于新的小分子生成.
Guohao Zong1, Jun Gao2, Yuanyuan Qi3
1Zhengzhou Tobacco Research Institute of China National Tobacco Corporation, Zhengzhou, Henan 450001, China.
Analytical chemistry
|November 24, 2025
概括
DiffNovo是一种新的光谱到结构框架,可以准确地识别质谱中的小分子. 这种计算工具增强了分子结构阐释在诸如代谢学和药物发现等领域.
科学领域:
- 计算化学是一种计算化学.
- 化学信息学 化学信息学
- 质谱学分析的分析.
背景情况:
- 从质谱中识别小分子结构对于代谢学,蛋白学和药物发现至关重要.
- 解释复杂的质谱来重建分子结构是一个重要的计算挑战.
研究的目的:
- 开发一个先进的计算框架,DiffNovo,用于从质谱数据中增强小分子识别.
- 提高使用碎片化模式重建分子结构的准确性和效率.
主要方法:
- 提出了DiffNovo,这是一个基于BART的SMILES编码器-解码器和高分辨率频谱编码器集成的频谱到结构扩散框架.
- 利用一种有条件扩散模型,采用前置增加噪声的过程和以光谱碎片化模式为指导的反向消除噪声的过程.
主要成果:
- 与现有方法相比,DiffNovo在NIST20数据集上表现出优异的性能.
- 在SMILES级别识别,基于指纹的相似性和物理化学性质对齐方面取得了更高的准确性.
结论:
- DiffNovo有效地从质谱中重建分子结构,对光谱数据显示高保真度.
- 该框架为各种科学领域的计算小分子识别提供了重大进展.
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