Surprisingly, DFT and ML displayed quite similar performance. This could be explained by the fact that the ML network was trained on shifts of real NMR data, which could have led to the better performance observed. Moreover, the DFT calculations for the entire HMDB dataset were considering the ten most populated conformers. Therefore, results could be optimized by increasing the While predicting NMR shifts as close to experiment as possible is important, it is not the ultimate goal. We hypothesized that it is likely that a relatively high error could be tolerated in various downstream analysis tasks, such as database search and structure identification, so long as the errors were systematic.