Our results show that MACE accurately predicts BDEs, achieving a root mean square error (RMSE) of 1.37 kcal/mol on a set of compounds with observed CYP 3A4 isoform sites of metabolism. In addition, MACE's BDE ranks show a good correlation with the DFT BDE ranks, improving on the predictions of the semi-empirical AM1 and GFN2-xTB methods or the graph neural network ALFABET model. We further test the model's extrapolation ability by applying it to non-equilibrium structures obtained by dissociating a C-H bond. Finally, we slightly extend the training set of the near-equilibrium isolated closedand open-shell structures by additionally including examples of reactants and products of multiple hydrogen abstraction by methoxy radical reactions. This modification was enough to find reasonable minimum energy paths (MEPs) with the nudged elastic band method thus demonstrating the feasibility of extension of the model to calculate the hydrogen abstraction reaction barriers, not only reaction energies.