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HSQCid:一个强大的工具,为铺平道路,以快速NMR实验为基础的自然产品的高通量结构重复

Bin Yuan¹, Chen Zhang¹, Chaoqun Ji²

¹Jiangxi Provincial Key Laboratory for Pharmacodynamic Material Basis of Traditional Chinese Medicine, Ganjiang Chinese Medicine Innovation Center, Nanchang 330000, PR China.

Analytical chemistry

|February 5, 2025

概括

一个新的AI工具,HSQCid,通过使用1H-13C HSQC光谱来增强天然产品的识别. 这种方法提供了一种更快,更准确的结构复制方法,改进了传统的技术.

科学领域:

自然产品化学自然产品化学
计算化学的计算化学
人工智能的人工智能

背景情况:

由于发现的化合物数量众多,结构复制对于天然产品 (NP) 研究至关重要.
核磁共振 (NMR) 光谱,特别是13C NMR,是结构识别的黄金标准,但可能耗时.
现有的NP脱复制的替代方法尚未得到广泛采用.

研究的目的:

开发一个高效的人工智能 (AI) 工具,用于自然产品的结构识别.
通过使用NMR光谱数据,提高结构复制的速度和准确性.
介绍HSQCid,一种AI工具,利用1H-13C HSQC光谱和化学结构之间的对比学习.

主要方法:

利用1H-13C HSQC光谱和化学结构之间的对比学习.
将图形神经网络 (GNN) 和变压器作为结构编码器进行比较,偏爱GNN.
在大约40万个预测数据点的大数据集上训练和测试模型.
使用17971个HSQC实验数据集作为外部测试数据验证了模型.

主要成果:

在实验数据上,HSQCid在实验数据上实现了74.5%和94.8%的top-1和top-10精度.
结合额外的结构特征,比如从1H NMR中附加到碳的的数量,至少提高了12%的top-1精度.
拟议的AI方法显著优于基准13C峰值匹配方法.

结论:

HSQCid提供了一种高效和准确的方法,用于自然产品的结构复制.
人工智能工具展示了高通量自然产品分析的潜力.
HSQCid有助于推进自然产品研究中的高效结构识别.

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