<p>In
recent years, the transformative potential of deep neural networks (DNNs) for analysing
and interpreting NMR data has clearly been recognised. However, most
applications of DNNs in NMR to date either struggle to outperform existing
methodologies or are limited in scope to a narrow range of data that closely
resemble the data that the network was trained on. These limitations have prevented
a widescale uptake of DNNs in NMR. Addressing this, we introduce FID-Net, a
deep neural network architecture inspired by WaveNet, for performing analyses
on time domain NMR data. We first demonstrate the effectiveness of this
architecture in reconstructing non-uniformly sampled (NUS) biomolecular NMR spectra.
It is shown that a single network is able to reconstruct a diverse range of 2D NUS
spectra that have been obtained with arbitrary sampling schedules, with a range
of sweep widths, and a variety of other acquisition parameters. The performance
of the trained FID-Net in this case exceeds or matches existing methods
currently used for the reconstruction of NUS NMR spectra. Secondly, we present
a network based on the FID-Net architecture that can efficiently virtually
decouple <sup>13</sup>C<sub>α</sub>-<sup>13</sup>C<sub>β</sub> couplings in
HNCA protein NMR spectra in a single shot analysis, while at the same time leaving
glycine residues unmodulated. The ability for these DNNs to work effectively in
a wide range of scenarios, without retraining, paves the way for their
widespread usage in analysing NMR data. </p>
Real-time prediction of 1H and 13C chemical shifts with DFT accuracy using a 3D graph neural network
