In a second application, we focused on the prediction of metabolites of medicinally relevant compounds. As such, we considered a second dataset consisting of 24 activation energies for hydrogen atom transfer by the cytochrome P450 enzyme from organic compounds, computed at B3LYP/6-311++G(2d,2p)//B3LYP/6-31G(d) (Fe=SVP) level of theory. 5 of the HAT reactions involve compounds with elements outside of the domain of the training data of our surrogate model and hence were discarded in our analysis. The models trained on the resulting extremely small dataset were subsequently applied to predict activation energies for 6 additional computed HAT reaction profiles for unseen substrates.