Diastereoisomers of a given compound are distinguishable by NMR (Scheme 1a), as are diastereotopic atoms or groups within the same molecule (Scheme 1b). Furthermore, molecular conformations give rise to different chemical shifts that may appear as distinct signals or as ensemble-averaged values depending on the interconversion rate relative to the NMR timescale (Scheme 1c). Such phenomena are not conveniently captured by the commonly-used descriptions of atomic environments that only encode local connectivity. Although DFT chemical shift predictions are now routinely used to differentiate stereoisomers, empirical approaches based on the 2D molecular graph fail this task absolutely. We reasoned that this challenge could be directly addressed by a model that uses a spatial representation of atomic environments in the form of a 3D molecular graph. Interatomic distances, including both bonded and nonbonded interactions, are an inherent part of this description, which is therefore able to capture variations in chemical shift across diastereoisomeric molecules, diastereotopic groups within a single chiral molecule, and spatially distinct molecular conformations. Unlike the valence bond model of chemical structure, 3D representations of local atomic environments such as atom-centered symmetry functions, do not require pre-conceived rules concerning topology, chemical bonding, or other physicochemical descriptors. These and related representations have been widely applied to predict atomic and molecular properties by ML methods. We surmised that the prediction of NMR chemical shift, being strongly influenced by local environment and stereochemistry, would be amenable to such an approach, although this has received limited attention. Using a sorted Coulomb matrix to represent atomic environments, von Lilienfeld and co-workers have predicted shielding tensors for small organic molecules by kernel ridge regression (KRR), obtaining MAEs of 3.9 ppm for 13 C and 0.28 ppm for 1 H relative to DFT values. However, the moderate levels of accuracy and reliance on DFT optimized structures as inputs limit practical applications to chemical structure elucidation.