NMR-TS: de novo molecule identification from NMR spectra
NMR-TS: de novo molecule identification from NMR spectra
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DOI：
10.1080/14686996.2020.1793382
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发表时间：
2020-01-31
影响因子：
5.5
通讯作者：
Tsuda, Koji
中科院分区：
文献类型：
--
作者：
Zhang, Jinzhe;Terayama, Kei;Tsuda, Koji
核磁共振（NMR）光谱是用于鉴定样品中分子的有效工具。尽管许多先前观察到的NMR光谱在公共数据库中积累，但它们仅涵盖了一小部分化学空间，并且通常根据专家知识手动完成分子识别。本文中，我们提出了基于机器学习的Python库NMR-TS，以自动从其NMR频谱中识别分子。 NMR-TS发现候选分子，其NMR光谱通过使用深度学习和密度功能理论（DFT）分配的光谱匹配目标光谱。作为概念验证，我们从其计算的光谱中鉴定出原型代谢物。在每个光谱平均运行5451 DFT之后，正确鉴定了九个分子中的六个，并且在其他情况下还获得了近端分子。这种令人鼓舞的结果表明，从头分子的产生可以有助于化学结构的完全自动化。 NMR-TS可从https://github.com/tsudalab/nmr-ts. [Graphics]获得。
Nuclear magnetic resonance (NMR) spectroscopy is an effective tool for identifying molecules in a sample. Although many previously observed NMR spectra are accumulated in public databases, they cover only a tiny fraction of the chemical space, and molecule identification is typically accomplished manually based on expert knowledge. Herein, we propose NMR-TS, a machine-learning-based python library, to automatically identify a molecule from its NMR spectrum. NMR-TS discovers candidate molecules whose NMR spectra match the target spectrum by using deep learning and density functional theory (DFT)-computed spectra. As a proof-of-concept, we identify prototypical metabolites from their computed spectra. After an average 5451 DFT runs for each spectrum, six of the nine molecules are identified correctly, and proximal molecules are obtained in the other cases. This encouraging result implies that de novo molecule generation can contribute to the fully automated identification of chemical structures. NMR-TS is available at https://github.com/tsudalab/NMR-TS.[GRAPHICS].