The Density Functional Theory (DFT) calculations were carried out using Gaussian16, incorporating three different basis set and functional combinations (geometry: B3-LYP/6-31G**, energy: m062x/def2tzvp, NMR: mPW1PW91/6-31G* or 6-31G**, without solvent and with cpcm chloroform or water solvent model) for which the optimal settings were employed in all subsequent experiments (detailed results in Supplementary Text 2). The DFT calculation pipeline employed the ICOLOS workflow manager for conformational search, which utilizes Schrödinger's MacroModel, followed by an RMS filter step (0.5 Å) and a DFT tight-binding (XTB) energy calculation step. The energies were then used to sort conformers by ascending values, with the 10 lowest energy conformers selected for further calculations using the Gaussian 16 software. The chosen conformers underwent geometry optimization using DFT, followed by DFT energy and NMR calculations with the DP4-AI processing pipeline. Lastly, we implemented a machine learning-based approach using the Scalable Graph Neural Network model (ML), a graph-based model trained on the NMRShiftDB2 database to accurately reproduce experimental 1 H and C chemical shifts for small, drug-like molecules ().