ABSTRACT 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.

中文翻译：

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NMR-TS：从 NMR 光谱中从头识别分子


摘要 核磁共振（NMR）波谱是识别样品中分子的有效工具。尽管许多以前观察到的核磁共振谱都积累在公共数据库中，但它们只覆盖了化学空间的一小部分，并且分子识别通常是根据专家知识手动完成的。在这里，我们提出了 NMR-TS，一个基于机器学习的 Python 库，可以从 NMR 谱中自动识别分子。 NMR-TS 通过使用深度学习和密度泛函理论 (DFT) 计算光谱来发现 NMR 光谱与目标光谱匹配的候选分子。作为概念验证，我们从计算的光谱中识别出原型代谢物。每个光谱平均运行 5451 次 DFT 后，九个分子中的六个被正确识别，并且在其他情况下获得了邻近分子。这一令人鼓舞的结果意味着从头分子生成有助于化学结构的全自动识别。 NMR-TS 可在 https://github.com/tsudalab/NMR-TS 上获取。