UConn Health, Farmington, CT 06030-3305, USA.
J Magn Reson. 2019 Sep;306:162-166. doi: 10.1016/j.jmr.2019.07.044. Epub 2019 Jul 16.
Machine learning has been used in NMR in for decades, but recent developments signal explosive growth is on the horizon. An obstacle to the application of machine learning in NMR is the relative paucity of available training data, despite the existence of numerous public NMR data repositories. Other challenges include the problem of interpreting the results of a machine learning algorithm, and incorporating machine learning into hypothesis-driven research. This perspective imagines the potential of machine learning in NMR and speculates on possible approaches to the hurdles.
机器学习在 NMR 中已经应用了几十年，但最近的发展表明其即将迎来爆发式增长。尽管存在许多公共 NMR 数据存储库，但机器学习在 NMR 中的应用仍然面临着可用训练数据相对较少的障碍。其他挑战包括解释机器学习算法结果的问题，以及将机器学习纳入假设驱动研究的问题。本文着眼于机器学习在 NMR 中的潜力，并对可能的方法进行了推测。
