Work

Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning

Year: 2019

Type: preprint

Abstract: Nuclear magnetic resonance (NMR) spectroscopy serves as an indispensable tool in chemistry and biology but often suffers from long experimental time. We present a proof-of-concept of application of deep learning and neural network for high-quality, reliable, and very fast NMR spectra reconstruction from limited experimental data. We show that the neural network training can be achieved using solely synthetic NMR signal, which lifts the prohibiting demand for a large volume of realistic training data usually required in the deep learning approach.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1904.05168

Authors: Qu, Xiaobo, Huang, Yihui, Lu, Hengfa, Qiu, Tianyu, Guo, Di, Agback, Tatiana, Orekhov, Vladislav, Chen, Zhong

Institutions: Xiamen University, Xiamen University of Technology, Swedish University of Agricultural Sciences, University of Gothenburg

Language: English

Topic: Advanced NMR Techniques and Applications

Subfield: Spectroscopy

Field: Chemistry

Domain: Physical Sciences

Open Access status: green

Funders: Swedish Foundation for International Cooperation in Research and Higher Education, Stiftelsen för Strategisk Forskning, National Natural Science Foundation of China, Natural Science Foundation of Fujian Province, Vetenskapsrådet, Fundamental Research Funds for the Central Universities

Awards: Sensing disorder: a novel, time-efficient approach for mapping functional conformational landscapes of IDPs, Innovativ experimentell modellering av dynamiska proteiner, G4295220613, G6661857097, G7578747682, G7934993622, G8137567645, G8139786028, G852543961, G8984937464

OpenAlex ID: W4321361555

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Cited by: 6

Related to: 10