Interestingly, these results appear to be contradictory to some reported metrics for TS geometry prediction models. For example, the OA-ReactDiff+rec model achieved an RMSD of 0.129 Å between predicted and reference TS structures, yet the associated energy barrier difference was reported as only 4.9 kcal mol -1 . Furthermore, Figure in the original OA-ReactDiff publication shows a logarithmic correlation between RMSD and the energy difference between TSs. To explore this apparent discrepancy, the optimised SN2 TS was instead perturbed by only rotating dihedral angles while bond lengths and angles remained fixed. This generated structures with RMSDs of 0.148, 0.163, and 0.323 Å from the optimised TS. Single-point energy calculations on these structures revealed energy differences of 1.0, 0.5 and 2.0 kcal mol -1 , respectively. These results are also visualised in Figure ; the larger differences in RMSD but significantly lower differences in energies suggest that changes in bond lengths and angles have a far more significant impact on TS energy than dihedral variations, indicating that TS geometry prediction models may be successful at predicting accurate bond lengths and angles while inaccuracies occur from inaccurate dihedral predictions. This result also highlights the limitations of RMSD as a metric for geometry prediction accuracy, as RMSD values appear to correlate poorly with chemically meaningful energy changes, making it an unpractical measure for assessing the quality of predicted TS geometries. When comparing the metrics across different studies, diffusion models emerge as the current state-ofthe-art for TS geometry prediction in both 3D input and 2D graph-based approaches. The success of diffusion models in TS geometry predictions aligns with their success at other generative tasks, such as image generation. For the 3D input methods, the best performing model, OA-ReactDiff+rec, achieved an RMSD of 0.129 Å from the reference structures. Furthermore, when single-point energy calculations are performed on structures predicted by this model and compared to the energies of the corresponding reactants, the results begin to approach the limits of chemical accuracy ; the energy barrier MAE calculated using OA-ReactDiff+rec predicted TSs is 4.4 kcal mol -1 . When comparing the 2D graph-based models, direct comparisons are challenging due to difference in training and test datasets and inconsistencies in reporting metrics (RMSD for G2S and D-MAE for TSDiff).