Computational details. NMR isotropic chemical shifts in the present work are predicted using a GNN derived from Schnet. The network receives 3D molecular structures via a vector of atom types and a vector of interatomic distances. The network is directly trained against chemical shifts for individual atoms. Three subsets of organic structures from the NMRShiftDB are used in this work, referred to as NMR8K, DFT8K, and Exp5K. The NMR8K dataset contains 8,000 neutral molecules with molecular weights up to 500, comprising elements: C, H, O, N, F, Cl, P, S. 3,016 of these structures have associated 1 H NMR experimental spectra; 6,000 have associated C spectra. These structures were processed with a computational workflow to generate the DFT8K dataset used for our GNN training. Our workflow involved embedding and molecular mechanics (MM) conformational analysis with the MMFF94 force field implemented in rdkit. The most stable MM conformers were then optimized at the M06-2X/def2-TZVP 86 level of theory, for which isotropic shielding constants were then calculated with gauge-independent atomic orbital (GIAO) 87 method at the mPW1PW91/6-311+G(d,p) 88 level of theory. This combination of MM and DFT methods has been used successfully for structure assignments with NMR chemical shift predictions. This workflow produced 7,455 DFT optimized structures with 117,997 1 H and 9,9105 C calculated chemical shift values, which make up the DFT8K dataset. The NMR8K and DFT8K datasets were then compared to prepare a clean experimental dataset from which apparent outliers are absent. This produced 5,631 structures labeled with 59,413 experimental 13 C chemical shifts, which make up the Exp5K dataset.