Nuclear magnetic resonance spectroscopy (NMR) plays a key role for the analysis of a plethora of molecules, including natural products and drug-like organic molecules. For such cases, 1H NMR spectra have proven imperative because of their high sensitivity and atomic resolution. However, these spectra are complicated by overlapped complex multiplet patterns. Here we show a deep-learning approach, which transforms spin-echo modulated 1H NMR spectra into highly sensitive and high-resolution singlet NMR spectra, that is, virtual homonuclear decoupled pure shift spectra. The approach was evaluated on experimental NMR spectra of complex organic compounds, where it outperforms current methods. The method also predicts uncertainties of the transformation and therefore allows for quantifications, which is a key strength of NMR. We believe that our approach will provide significant advantages when characterizing low-sensitivity samples and systems with exchangeable protons, where signals are not observed in traditional pure-shift spectra and substantial overlaps hamper analysis from conventional spectra.
Kakita et al. (Mon,) studied this question.