Using a smooth overlap of atomic positions (SOAP) kernel to evaluate the correlation between local atomic environments, Ceriotti and co-workers performed Gaussian Process Regression in a seminal work to predict shielding tensors of molecular solids with RMSEs of 4.3 ppm for 13 C and 0.49 ppm for 1 H. Their model was able to assign the crystal polymorphic of cocaine from a selection of candidate structures by comparing against experimental chemical shifts. Another machine learning model, IMPRESSION, involving Kernel Ridge Regression was developed by Butts and co-workers, where they leverage DFT-computed NMR parameters to predict 1 JCH scalar couplings and C and 1 H chemical shifts with an MAE of 0.87 Hz , 0.23 ppm and 2.45 ppm respectively for an independent test set. Communitypowered approach has also been sought to improve the prediction of NMR properties, where they develop a combined model which was 7-19 times more accurate than existing prediction models. Herein, we develop a GNN model to predict isotropic C and 1 H chemical shifts from a 3D representation of atomic environments. The favorable levels of accuracy and speed permit structural and stereochemical assignments to be carried out for large and flexible organic molecules that would be enormously challenging for quantum chemical approaches.