一个端到端的深度学习框架,用于将质谱转化为de-novo分子
Eleni E Litsa1, Vijil Chenthamarakshan2, Payel Das3
1Department of Computer Science, Rice University, Houston, TX, USA.
Communications chemistry
|June 23, 2023
概括
Spec2Mol是一个新的深度学习模型,仅从质谱中推化学结构. 这种方法有助于药物发现和生物标记物识别新型分子,这些新分子在现有数据库中没有被发现.
科学领域:
- 计算化学计算化学
- 化学信息学 化学信息学
- 化学中的人工智能.
背景情况:
- 阐明化学结构对于药物发现,精准医学和生物标志物识别至关重要.
- 目前的方法依赖于质谱和光谱数据库,对于新型分子而言失败.
- 需要计算工具来从质谱数据中预测分子结构.
研究的目的:
- 提出Spec2Mol,一种深度学习架构,用于仅使用质谱的分子结构建议.
- 解决新化学化合物的现有方法的局限性.
- 为了使在参考光谱数据库中缺少的分子的结构阐明.
主要方法:
- 开发了Spec2Mol,这是一个由Speech2Text模型启发的编码-解码深度学习架构.
- 编码器从质谱中生成嵌入的内容.
- 解码器预先训练了各种化学结构,重建了SMILES (简化分子输入线输入系统) 序列.
主要成果:
- Spec2Mol成功地从质谱中直接识别了关键的分子亚结构.
- 基于推和原始结构之间的分子相似性来评估性能.
- 实现了与现有的碎片树方法相比较的性能,特别是对于新型或未列出的化合物.
结论:
- Spec2Mol提供了一个强大的深度学习解决方案,用于从质谱学推分子结构.
- 该模型显示了加速药物发现和相关领域的巨大潜力.
- Spec2Mol为分析新型化学实体提供了有价值的工具,因为参考数据很少.
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