Rapid progress in machine learning offers new opportunities for the automated analysis of multidimensional NMR spectra ranging from protein NMR to metabolomics applications. Most recently, it has been demonstrated how deep neural networks (DNN) designed for spectral peak picking are capable of deconvoluting highly crowded NMR spectra rivaling the facilities of human experts. Superior DNN-based peak picking is one of a series of critical steps during NMR spectral processing, analysis, and interpretation where machine learning is expected to have a major impact. In this perspective, we lay out some of the unique strengths as well as challenges of machine learning approaches in this new era of automated NMR spectral analysis. Such a discussion seems timely and should help define common goals for the NMR community, the sharing of software tools, standardization of protocols, and calibrate expectations. It will also help prepare for an NMR future where machine learning and artificial intelligence tools will be common place.

机器学习的快速发展为从蛋白质核磁共振到代谢组学应用的多维核磁共振光谱的自动分析提供了新的机会。最近，已经证明了为光谱峰值拾取而设计的深度神经网络 (DNN) 如何能够解卷积高度拥挤的 NMR 光谱，与人类专家的设施相媲美。卓越的基于 DNN 的峰值拾取是 NMR 光谱处理、分析和解释过程中的一系列关键步骤之一，预计机器学习将产生重大影响。从这个角度来看，我们列出了机器学习方法在自动化 NMR 光谱分析的新时代中的一些独特优势和挑战。这样的讨论似乎很及时，应该有助于确定 NMR 社区的共同目标，共享软件工具，协议标准化，并校准预期。它还将有助于为机器学习和人工智能工具将普遍使用的核磁共振未来做好准备。