Figure depicts a schematic representation of the process adopted in the present work for generating virtually decoupled pure-shift spectra with high sensitivity. The spin-echo-based pulse sequences, Fig. , generate J-modulated multiplet patterns in 1D/2D 1 H NMR spectra, with the spin-echo blocks inserted immediately before acquisition in the direct dimension. Thus, each experiment consists of recording five spectra at spin-echo evolution durations of 0 ms, 10 ms, 30 ms, 50 ms, and 90 ms, Fig. , which create a unique pattern in the data for each miltiplet type, as discussed previously. It is important to note that these unique patterns, generated by evolving the scalar couplings, are used as input to the FID-Net-PS model and allow the DNN to separate the different multiplet types and virtually decouple these. The FID-Net-PS model, Fig. and Fig. , was trained fully on synthetic data, as also done for previous. Using synthetic data for training has several advantages: Firstly, an unlimited amount of realistic training data can be generated to ensure that the model is trained to an appropriate level and, secondly, the ground truth of the training data is known, which facilitates more accurate training and initial theoretical evaluations. Details of the generation of training data can be found in supporting information and training parameters are provided in Table . The target for the training is the corresponding synthetic pure shift NMR spectra, that is, the virtually decoupled 1D 1 H spectrum, where all scalar coupling multiplet patterns are collapsed into singlets. Overall, the FID-Net-PS model was trained to generate pure shift spectra (DNN-PS) as well as also to provide the uncertainty of the transformation on a pointby-point basis, Fig. , which facilitates quantitative applications. Initially, the FID-Net-PS model was evaluated using synthetic data, where some examples are shown in Fig. . These evaluations show that the trained FID-Net-PS model accurately predicts both pure shift spectra and uncertainties. Subsequently, and as detailed below, the trained FID-Net-PS model was evaluated on experimental NMR spectra of kanamycin, estradiol, a mixture of kanamycin and glucose, and on a mixture of 11 amino acids. Fig. shows the conventional experimental 1D 1 H NMR spectrum, the PSYCHE pure shift spectrum, and FID-Net-PS generated DNN-PS spectra obtained on a sample of kanamycin, which is a glycosidic antibiotic composed of three nearly identical saccharide units. From Fig. it is seen that the PSYCHE approach is able to resolve the overlap at the expense of spectral sensitivity, however, the DNN-PS spectrum generated by FID-Net-PS has considerably improved spectral resolution without compromising spectral sensitivity. The evaluation of the FID-Net-PS approach was also carried out on 1D 1 H spectra of estradiol, a complex human hormonal steroid molecule, Fig. . Estradiol, like kanamycin, has a relatively low spectral resolution in the conventional 1D 1 H NMR spectrum, which is well resolved in the DNN-PS spectrum. From the two examples, it is clear that the resolutions of the conventional 1D 1 H spectra are inadequate to resolve the chemical shifts of the severely overlapped peaks, even when recorded at a high magnetic field of 800 MHz. On the other hand, the PSYCHE pure shift spectra resolve most of the chemical shifts, leading to significant resolution enhancement, however, at the expense of substantial loss in sensitivity. In contrast, the spectral resolution of the DNN-PS spectrum is similar to that of the PSYCHE pure shift spectrum, while the sensitivity of the DNN-PS spectrum is substantially larger than the PSYCHE spectrum. Here the DNN-PS spectrum is derived from five spin-echo spectra acquired at different coupling evolution times and processed using the trained FID-Net-PS model. Judging the sensitivity of spectra obtained from non-linear transformations, such as DNNs, need special considerations because the 'noise' is not uniform, as also clearly seen in Figs. and. Firstly, the FID-Net-PS model was trained to report point-by-point uncertainties, which are useful for evaluating prediction ambiguities for all resonances and as expected, these values are higher for overlapped spectral regions. As detailed below, the sensitivity of the FID-Net-PS model is further evaluated by using complex mixtures, where the relative concentrations are known.