Recently, Machine Learning Interatomic Potentials have emerged as versatile surrogate models capable of accurately reproducing ab initio potential energy surfaces. However, most of their applications have been targeted at near-equilibrium closed-shell structures. In this talk I  introduce a transferable MACE interatomic potential that is applicable to open- and closed-shell drug-like molecules containing hydrogen, carbon, and oxygen atoms. Including an accurate description of radical species extends the scope of possible applications to bond dissociation energy (BDE) prediction, for example, in the context of cytochrome P450  metabolism. I will discuss the model's performance and compare it to that of alternative methods for BDE prediction. Additionally, I will provide an outlook for extending the model's scope to modelling the full C-H abstraction pathways.