Review and Prospect: Deep Learning in Nuclear Magnetic Resonance Spectroscopy

TL;DR

This paper reviews how deep learning techniques are applied to NMR spectroscopy, highlighting recent advances and future prospects for transforming data analysis in chemistry and life sciences.

Contribution

It provides a comprehensive overview of DL applications in NMR and discusses potential future developments in the field.

Findings

DL has significantly improved data analysis in NMR spectroscopy

Deep learning approaches are poised to revolutionize NMR data processing

The review outlines future directions for DL in NMR research

Abstract

Since the concept of Deep Learning (DL) was formally proposed in 2006, it had a major impact on academic research and industry. Nowadays, DL provides an unprecedented way to analyze and process data with demonstrated great results in computer vision, medical imaging, natural language processing, etc. In this Minireview, we summarize applications of DL in Nuclear Magnetic Resonance (NMR) spectroscopy and outline a perspective for DL as entirely new approaches that are likely to transform NMR spectroscopy into a much more efficient and powerful technique in chemistry and life science.