Review and Prospect: Deep Learning in Nuclear Magnetic Resonance Spectroscopy. Issue 46 (25th June 2020)

• Record Type: Journal Article
• Authors: Chen, Dicheng; Wang, Zi; Guo, Di; Orekhov, Vladislav; Qu, Xiaobo
• Is Part Of: Chemistry. Volume 26:Issue 46(2020)
• Page Start: 10391
• Page End: 10401
• Publication Date: 2020-06-25
• Subjects: artificial intelligence -- computational chemistry -- deep learning -- NMR spectroscopy; Chemistry -- Periodicals; 540
• DOI: 10.1002/chem.202000246
• Languages: English
• ISSNs: 0947-6539

Abstract

Abstract: Since the concept of deep learning (DL) was formally proposed in 2006, it has had a major impact on academic research and industry. Nowadays, DL provides an unprecedented way to analyze and process data with demonstrated great results in computer vision, medical imaging, natural language processing, and so forth. Herein, applications of DL in NMR spectroscopy are summarized, and a perspective for DL as an entirely new approach that is likely to transform NMR spectroscopy into a much more efficient and powerful technique in chemistry and life sciences is outlined. Abstract : Applying automation : The applications of deep learning in NMR spectroscopy are summarized, and a perspective for deep learning as an entirely new approach that is likely to transform NMR spectroscopy into a much more efficient and powerful technique in chemistry and life sciences is outlined.