Having a method or strategy that provides high-sensitivity pure shift NMR spectra, while retaining ultra-high resolution, would solve a wide range of challenges concerning the detection of valuable spectral information at ultrahigh resolution. In the current era of artificial intelligence and deep learning it is natural to leverage these tools to improve the sensitivity of pure shift NMR and NMR in general. It is worth noting that some recent deep-learning applications in NMR can automate protein structural analysis and improve spectral resolution in solid-state NMR. Along the same lines, JRESRGAN was recently developed for peak deconvolution along the direct dimensions of metabolite JRES NMR spectra and the current FID-Net deep learning architecture has made advances possible in NMR spectral processing of proteins. Below we present a combined NMR and deep learning approach to provide highsensitivity, high-resolution, pure-shift 1 H spectra of small molecules. The presented strategy employs a new neural network architecture based on FID-Net 36 , named FID-Net-PS. In comparison to the original FID-Net architecture, three major modifications have been made to obtain high-quality pure shift spectra (i) the input has been tailored and consists of five 1D spectra that are analyzed simultaneously, Fig. , where the scalar couplings have been evolved for different times (ii) a refinement step is included at the end of the model, and (iii) uncertainties are also predicted on a point-by-point basis similar to previously. The full model is described in Fig. . The approach can be used to yield pure shift spectra of both 1D and 2D