Journal
SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS
Volume 21, Issue 1, Pages 552-561Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/14686996.2020.1793382
Keywords
NMR; deep learning; molecule generation; density functional theory
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Funding
- Cabinet Office, Government of Japan [SIP]
- Core Research for Evolutional Science and Technology [JPMJCR1502]
- Exploratory Research for Advanced Technology [JPMJER1903]
- New Energy and Industrial Technology Development Organization [P15009]
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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] .
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