GNNs for atomic property prediction: GNNs 30, 52, 58-66 do not depend on pre-computed descriptors and are able to learn underlying regularities directly from the molecular graph, represented either in 2D form, encoding interatomic connectivity, or in 3D form, where spatial information is included. GNNs have recently been applied to end-to-end (i.e., structure-to-property) learning of molecular properties such as molecular energies and HOMO/LUMO gaps and have been extended to the prediction of bond properties within molecules. In this work, our network was modeled after the Schnet deep learning architecture of Müller and coworkers , combined with edge updates. The model is implemented using Tensorflow, and all underlying code is openly accessible and documented. This was then trained to predict C and 1 H chemical shifts as the target properties. A schematic of our network is shown in Fig. . From a query 3D molecular structure, two input vectors are constructed with rdkit 72 containing (i) element types and (ii) interatomic distances less than 5 Å. Discrete node feature vectors (of size 256) are then generated by categorizing each element type through an embedding layer, while continuous edge feature vectors are generated by an expansion of the interatomic distances as a series of 256 radial basis functions (RBFs). This is described by Eqn. 1, where the continuous vector 𝑒 !" # " represents the initial "edge" linking atoms i and j and is expressed in terms of the interatomic distance 𝑑 $% and constants µ and 𝛿. These constants are chosen such that the range of the input features can be covered by the centers of the RBFs; in this work 𝛿 = 0.04 and µ = 0.