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NMR-TS: De Novo Molecule Identification from NMR Spectra

preprint
submitted on 01.04.2020, 07:17 and posted on 03.04.2020, 17:34 by Jinzhe Zhang, Kei Terayama, Masato Sumita, Kazuki Yoshizoe, Kengo Ito, Jun Kikuchi, Koji Tsuda
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 any 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.

History

Email Address of Submitting Author

tsuda@k.u-tokyo.ac.jp

Institution

University of Tokyo

Country

Japan

ORCID For Submitting Author

0000-0002-4288-1606

Declaration of Conflict of Interest

No conflict of interest

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in Science and Technology of Advanced Materials

ChemRxiv

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