In this paper, we have demonstrated that the MACE framework may be used to construct a transferable machine learning interatomic potential for closed-shell drug-like molecules and selected derived radical species. We have shown that for the closed-shell structures, the MACE model reaches accuracy on par with a general-purpose transferable ANI-2x MLIP but can, in addition, describe selected open-shell structures to similar accuracy. One explanation for success in transferability is that the training set constructed in this work covers a wide range of local chemical environments. This suggests that the ZINC database is a good source of structures to base MLIP fitting frameworks on and molecular dynamics-based geometry sampling was sufficient to achieve high transferability. Moreover, the MACE framework seems to be well suited for reaching high accuracy at a relatively low training data-regime limit of fewer than 20,000 structures. Furthermore, the MACE model shows promise in an extrapolation test to non-equilibrium structures.