NMR-TS: de novo molecule identification from NMR spectra. Issue 1 (31st January 2020)

• Record Type: Journal Article
• Authors: Zhang, Jinzhe; Terayama, Kei; Sumita, Masato; Yoshizoe, Kazuki; Ito, Kengo; Kikuchi, Jun; Tsuda, Koji
• Is Part Of: Science and technology of advanced materials. Volume 21:Issue 1(2020)
• Page Start: 552
• Page End: 561
• Publication Date: 2020-01-31
• Subjects: NMR -- deep learning -- molecule generation -- density functional theory; 404 Materials informatics / Genomics; Materials -- Technological innovations -- Periodicals; 620.112
• DOI: 10.1080/14686996.2020.1793382
• Languages: English
• ISSNs: 1468-6996

Abstract

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 . Abstract :