Finally, we also attempted to predict the experimentally reported HAT selectivities reported by Zuo et al., which were subsequently computationally recovered through DFT calculations by Ma and co-workers. In Figure , the performances of the various model architectures are presented. For all models, the predictions are in qualitative agreement with the experimentally observed regioselectivity for at least 4 out of 6 compounds (with our best model -the ensembled FFNN -obtaining a perfect score of 6 out of 6). Also quantitatively, our models reach good accuracy as compared to the computed DFT barriers in this application. The most accurate model is again the FFNN with 4 ensembles (MAE = 0.84 kcal/mol, RMSE = 1.07 kcal/mol, R 2 = 0.74), with only one prediction resulting in an error over 2 kcal/mol with respect to the DFT calculations. The RF model, despite being the least successful at qualitatively predicting the most likely HAT position, reaches only a slightly worse quantitative accuracy (MAE = 0.94 kcal/mol, RMSE = 1.37 kcal/mol; R 2 = 0.64).