Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning
Accelerated Nuclear Magnetic Resonance Spectroscopy with Deep Learning
复制标题
深度学习加速核磁共振波谱分析
DOI：
10.1002/anie.201908162
复制
发表时间：
2020-04-15
影响因子：
16.6
通讯作者：
Chen, Zhong
中科院分区：
文献类型：
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作者：
Qu, Xiaobo;Huang, Yihui;Chen, Zhong
核磁共振（NMR）波谱是化学和生物学中不可或缺的工具，但常常受到实验时间长的困扰。我们提出了应用深度学习和神经网络的概念验证，可根据有限的实验数据进行高质量、可靠且非常快速的 NMR 谱重建。我们证明，仅使用合成 NMR 信号即可实现神经网络训练，这提高了深度学习方法中通常所需的大量实际训练数据的禁止性需求。
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 large volume of realistic training data usually required in the deep learning approach.