To choose the optimal model hyperparameters, during training we performed a grid search with 5-fold crossvalidation for the regularization parameter C varying it from 10 -9 to 10 9 in 10fold increments and the kernel coefficient gamma using 'scale', 'auto', 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 5, 10, 15, and 25 as possible options. The best performing model was then used to predict the HFEs of the test set ligands and the prediction errors (ΔGexperimental -ΔGcalculated, in kcal/mol) and relative errors (ΔGcalculated*100/ΔGexperimental, in per cent (%)) were recorded. This procedure was repeated 10,000 times for random train/test splits, all in a 70:30 ratio.