The second application area focuses on P450 metabolism of medicinally relevant compounds. As discussed in the Methodology Section, predictive models were trained on a tiny dataset of 19 HAT activation energies, and subsequently, these models were applied to predict 6 barriers for unseen substrates, in line with the work by Gingrich et al.. We again started with a multivariate linear regression model, but we considered only (a subset of) the descriptors of the hydrocarbons to reduce collinearity and avoid overfitting (Section S6). With the trained model, based only on the predicted spin density and buried volume, an MAE of 1.12 kcal/mol is obtained for the unseen substrates, in line with the accuracy reached by the predictive model proposed by Gingrich et al. (1.15 kcal/mol). Adding more descriptors, the MAE of the model increases (Section S6).