The top panels of Figure show the predicted BDE rank correlations with respect to the DFT BDE reference. To compare the 60 molecules, we compute BDE ranks with the two methods and plot one rank versus the other for each of the molecules, overlaid over each other in the top row of plots in Figure . The middle row of panels shows the distributions of Kendall's τ correlation coefficients, each coefficient corresponding to the rank of BDEs of a single molecule. The predicted vs reference BDE parity plots are in the bottom row of Figure . In addition to MACE, we evaluate two semi-empirical methods, AM1 48 and GFN2-xTB, and ALFABET, 1 a graph neural network model created to predict bond dissociation enthalpies of small organic molecules directly based on their 2D SMILES representation. and ALFABET 1 in their ability to predict BDEs. The top panels show BDE rank correlations for different molecules in the "CYP 3A4" dataset. In each plot, multiple BDE rank predictions are overlaid over each other with the resulting grey scale indicating the density of points. The middle graphs show distributions of Kendall's τ correlation coefficients for all of the molecules in the "CYP 3A4" dataset, one τ for one molecule's BDE rank. In the bottom row are parity plots of predicted vs DFT BDE directly. The BDEs are computed on the initial geometries optimised with the respective method ("D vs M" of Figure ). The ALFABET model was fitted to reproduce bond dissociation enthalpies computed via M06-2X/def2-TZVP, however the rank of bond dissociation enthalpies is expected to be comparable to the bond dissociation energies used here as the reference.