The first correction relies on a Brønsted relationship to estimate the AM1 reaction activation energy from the AM1 reaction energy, while the second correction addresses systematic errors associated with the AM1 method, with DFT as a reference. DFT-accurate transferable MLIPs have the potential to replace AM1 in this approach, improving the accuracy and speed of high-throughput reaction-energy or even reaction-activation energy prediction, as highlighted in the lower branch of Figure . Reaction pathways catalysed by CYPs proceed via radical intermediates, necessitating methods capable of treating open-shell structures for studying small molecule metabolism. However, there are few methods to study radicals without invoking the full electronic structure treatment that is too computationally expensive for high-throughput predictions. Most notable are semi-empirical methods like AM1 48 or GFN2-xTB, which are the simplest form of electronic structure theory, with integral approximations and parameterisation fitted to experimental data or higher-level ab initio results. As such, they are in between ab initio methods and empirical force fields, offering faster computation but lower accuracy compared to the former, and greater transferability across a wider range of chemistry problems at a higher computational cost than the latter. A few empirical force fields have been created specifically for modelling reactions and open-shell structures, particularly the ReaxFF potentials. While various ReaxFF parametrisations have been used to study a wide range of challenging problems, there is still room for improvement to better match ab initio potential energy surfaces. To address this specific need, we present a proof-of-concept work introducing a transferable and computationally efficient MACE interatomic potential that encompasses druglike molecules and radical species. MACE, an equivariant message-passing neural network, demonstrates exceptional speed, accuracy and data efficiency, surpassing existing methods on a range of benchmark tasks. Our training dataset is derived from the ZINC database 56 and consists of drug-like molecules containing carbon, hydrogen and oxygen ([C,H,O]) atoms, as well as derived sp 3 carbon radicals. The choice to limit this study to compounds containing [C,H,O] chemical elements was motivated by smaller chemical space and easier development of the models, with the intention to subsequently expand the scope of chemical elements and CYP-catalysed reaction mechanisms to possibly integrate these models in the site of metabolism prediction framework illustrated in Figure . We have tested the model's transferability on CYP substrates and the MLIP benchmark COMP6 dataset. The results demonstrate that our model achieves better accuracy than a general ANI-2x potential on a relevant part of the COMP6 dataset while extending the area of applicability of the model to sp 3 carbon radicals.