Empirical approaches to chemical shift prediction provide a less expensive alternative to electronic structure calculations by harnessing pre-existing knowledge such as large datasets of experimentally measured chemical shifts. Additive methods have been developed to predict chemical shift based on the cumulative effects of local substituents, as implemented in ChemDraw. More sophisticated machine learning (ML) methods encode each atom as a one-dimensional vector using an atom-based connectivity scheme. For example, a hierarchically ordered spherical description of environment (HOSE) code predicts chemical shifts based on the measured similarity to database entries or by using fully-connected neural networks. When trained against a large number of experimentally measured chemical shifts, these methods have achieved predictive accuracies of 1.7 ppm for C chemical shifts and 0.2 ppm for 1 H shifts (expressed as mean absolute error, MAE). These earlier ML approaches tend to rely upon feature engineering : expertcrafted rules are required to encode atomic environment, which can suffer from human bias and incompleteness, and which are often trained separately for different atom types (e.g., different models are developed for tetrahedral and trigonal carbon atoms). In particular, the rise of feature learning, as embodied by graph neural networks (GNNs), has enabled 'end-to-end' learning from molecular structures and avoids rule-based encoding. Jonas and Kuhn have developed a GNN to predict the C and 1 H chemical shifts and achieved an accuracy of 1.43 ppm for C and 0.28 ppm for 1 H (MAE for the testing set) using 2D molecular connectivity as input.