Review and Prospect: Deep Learning in Nuclear Magnetic Resonance Spectroscopy
Dicheng Chen1,#, Zi Wang1,#, Di Guo2, Vladislav Orekhov3, Xiaobo Qu1,*
1 Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen 361005, China
2 School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China
3 Department of Chemistry and Molecular Biology, University of Gothenburg, Box 465, Gothenburg 40530, Sweden
* Email:quxiaobo <at> xmu.edu.cn or quxiaobo2009 <at> gmail.com
# Co-first authorship
Citation
Dicheng Chen#, Zi Wang#, Di Guo, Vladislav Orekhov, Xiaobo Qu*. Review and Prospect: Deep Learning in Nuclear Magnetic Resonance Spectroscopy, Chemistry -A European Journal, DOI: 10.1002/chem.202000246, 2020.
Link
http://dx.doi.org/10.1002/chem.202000246
Synopsis
Deep Learning (DL) can discover fruitful features embedded in large data sets and figure out the complex nonlinear mapping between inputs and outputs without prior knowledge. In view of the clear success, researchers in nuclear magnetic resonance (NMR) field start to pay attention to DL and explore it for addressing deficiencies of conventional methods. In this paper, we systematically summarize applications of DL in NMR spectroscopy including spectra reconstruction, denoising, chemical shift prediction and automated peak picking.
Figure 1. The flowchart of neural network training.
Main content
Firstly, we introduce two methods for fast reconstructing high-quality spectra from undersampled data, which use Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) network trained by synthetic NMR data, respectively. Their reconstruction performances are comparable with the state-of-the-art iterative methods, but they show outstanding advantages in computational time.
Secondly, in denoising, we mention a CNN trained by simulation data, which can infer the mapping between the spectra with lots of interference and the high Signal-to-Noise Ratio (SNR) spectra. Its robust performance for low SNR may promote the development of clinical applications.
Thirdly, researchers used different neural networks to create relationships between the information of compounds and their chemical shifts. The results show that, these networks can apparently approach the limits of empirical methods for predicting chemical shift and the accuracy is comparable to the ab initio quantum chemistry methods.
Finally, DL is demonstrated to be used for automated peak picking, and we focus on two networks. One is called NMR-Net, the other is trained by simulated spectra with labels. The result on realistic data shows that, their picking accuracies are consistent with the manually selected signal regions.
Furthermore, we outline a perspective for DL as entirely new approaches that is likely to transform NMR spectroscopy into a much more efficient and powerful technique in chemistry and life science.
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (NSFC) under grants 61971361, 61871341, and U1632274, the Joint NSFC-Swedish Foundation for International Cooperation in Research and Higher Education (STINT) under grant 61811530021, the National Key R&D Program of China under grant 2017YFC0108703, the Natural Science Foundation of Fujian Province of China under grant 2018J06018, the Fundamental Research Funds for the Central Universities under grant 20720180056, the Xiamen University Nanqiang Outstanding Talents Program, the Science and Technology Program of Xiamen under grant 3502Z20183053, the Swedish Research Council under grant 2015–04614, and the Swedish Foundation for Strategic Research under grant ITM17-0218.
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