The presented FID-Net-PS method is an alternative approach to the conventional pure shift NMR methods, such as the PSYCHE, for obtaining virtually decoupled spectra at very high resolution, both in 1D and in 2D, and at high spectral sensitivity. The FID-Net-PS model was trained on sets of synthetic spin-echo spectra, where the scalar couplings are modulated, which aids the model in learning to transform highly complex multiplet patterns. The applications of this FID-Net-PS model have been demonstrated on complex organic molecules such as kanamycin, estradiol, and a mixture of 11 amino acids. Since FID-Net-PS not only performs a virtual decoupling, but also predicts uncertainties of the transformation, quantifications are reliable, as shown with analyses of the kanamycin and glucose mixtures. Overall, the benefits associated with the presented method will not be limited to 1D spectra, but as we have shown can easily be extended to higher dimensions including experiments such as TOCSY, NOESY, COSY, and any other spectra with 1 H in any one of the dimensions. It is anticipated that the main strength of the FID-Net-PS approach is when analyzing very low-sensitivity samples, where strong overlaps are observed in conventional spectra and no signals are observed in traditional pure-shift spectra.