Transferable machine learning interatomic potential for bond dissociation energy prediction of drug-like molecule

We present 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 prediction, for example, in the context of cytochrome P450 (CYP) metabolism. The transferability of the MACE potential was validated on the COMP6 dataset, containing only closed-shell molecules, where it reaches better accuracy than the readily available general ANI-2x potential. MACE achieves similar accuracy on two CYP metabolism-specific datasets, which include open- and closed-shell structures. This model enables us to calculate the aliphatic C-H bond dissociation energy (BDE), which allows us to compare reaction energies of hydrogen abstraction, which is the rate limiting step of the aliphatic hydroxylation reaction catalysed by CYPs. On the "CYP 3A4" dataset MACE achieves BDE RMSE of 1.37 kcal/mol and a better BDE rank predictions than currently used AM1 semi-empirical method.