However, the requirement of DFT optimized structures as inputs significantly limits a model's practicality and applicability. Therefore, we opted to retrain the ExpNN-dft model using 3D structures obtained from inexpensive molecular mechanics (MM) calculations (MMFF94) as input, retaining experimental chemical shifts from Exp5K as targets. Transfer learning was again employed for this retraining. This time, however, to reflect the fact that the training data contains modified molecular geometries, the six hidden layers in the edge updating block were optimized (Fig. ), while all other parameters were held fixed. This second round of transfer learning led to a 13 C MAE of 1.43 ppm against experiment. This final GNN model, named ExpNN-ff, retains the high accuracy of the previous models while processing MM input structures, facilitating real-time C chemical shift prediction.