Initial 3D geometries were then embedded from each molecule's SMILES representation using a distance geometry approach (SI Text 2), which was followed by conformational analysis with MMFF, culminating in the optimization of M06-2X/def2-TZVP geometries and empirically-scaled mPW1PW91/6-311+G(d,p) chemical shifts for each of these 8,000 structures. This process was automated by a parallel Python workflow that takes structures from a 2D molecular database (NMR8K), performs conformational analysis, submits and monitors Gaussian jobs, and finally parses outputs (see SI Text2 for details on the automated workflow and DFT calculation methods). A new dataset, DFT8K, is populated by DFT optimized geometries and the corresponding computed chemical shifts (around 120,000 1 H and 100,000 C DFT chemical shifts in total, Fig. ). To obtain DFT-predicted isotropic chemical shifts we applied an empirical scaling formula to the raw shielding tensor values. DFT optimized geometries (inputs) and chemical shifts (prediction targets) from the DFT8K dataset were then used to train a GNN. 500 structures were used to evaluate the validation loss during model training, and another 500 structures were held-out as an external test set (Fig. ). We refer to this ML model as DFTNN. Since and basis set used for the structure optimization and chemical shift calculation. To minimize prediction errors associated with the use of DFT reference data, we sought to further optimize performance by subjecting our GNN to additional refinement with TL, incorporating experimental data. Importantly, we also devised a strategy to check and clean these experimental data using the results of DFT calculations as described below.