We utilised two pre-existing datasets of Diels-Alder reactions (ds2) and [3+2] cycloadditions (ds3) to predict diene/dienophile and dipole/dipolarophile distortion energies, respectively. The pre-ML MAEs for the diene and dienophile distortion energy for ds2 were 2.80 and 2.04 kcal mol -1 respectively (Table ). Across all tested models, averaged predictions of the diene distortion energies for ds2 were well below 1 kcal mol -1 ; the best performing model was again SVR with an averaged MAE of 0.33 ± 0.08 kcal mol -1 (the results of the best models are visualised in Fig. ). Ridge regression had the highest averaged error across all predictions (0.61 kcal mol -1 ) however, its performance is still significantly below the 1 kcal mol -1 accuracy threshold that has typically been used for energy predictions. SVR is again the best performing model when predicting dienophile distortion energies with KRR also performing strongly (MAEs of 0.34 ± 0.05 and 0.38 ± 0.05 kcal mol -1 respectively). Greater fluctuation in model performance is seen when predicting the dienophile distortion energies, however, SVR tends to perform consistently well across the diene and dienophile distortion energy prediction tasks. Ds3 provides an interesting challenge due to the diversity and biological relevance of this bio-orthogonal dataset. The best reported ML predictions of the reaction barriers are between 2 and 3 kcal mol -1 . These results highlight the complexity of this dataset and the difficulty in predicting barriers approaching the desired accuracy of 1 kcal mol -1 . A prediction limit for this dataset may have been reached. The pre-ML AM1-DFT MAE for the dipole distortion energies was 3.59 kcal mol -1 (Table ). When predicting the distortion energy of the dipole, the best performing model again used SVR with an averaged test set MAE of 2.55 ± 0.13 kcal mol -1 . SVR again gave the best performing model for the dipolarophile distortion energy predictions with an averaged test MAE of 2.37 ± 0.12 kcal mol -1 . Across the datasets tested, prediction errors for the distortion energy models are either similar or lower than that those of the reaction barrier models. For full results on both cycloaddition datasets see ESI, section 4.3, Table and S6.