Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules

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 (BDE) prediction, for example, in the context of cytochrome
P450 (CYP) metabolism. The transferability of the MACE potential was
validated on the COMP6 data set, 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
data sets, which include open- and closed-shell structures. This model
enables us to calculate the aliphatic C–H BDE, which allows
us to compare reaction energies of hydrogen abstraction, which is
the rate-limiting step of the aliphatic hydroxylation reaction catalyzed
by CYPs. On the “CYP 3A4” data set, MACE achieves a
BDE RMSE of 1.37 kcal/mol and better prediction of BDE ranks than
alternatives: the semiempirical AM1 and GFN2-xTB methods and the ALFABET
model that directly predicts bond dissociation enthalpies. Finally,
we highlight the smoothness of the MACE potential over paths of sp3C–H bond elongation and show that a minimal extension
is enough for the MACE model to start finding reasonable minimum energy
paths of methoxy radical-mediated hydrogen abstraction. Altogether,
this work lays the ground for further extensions of scope in terms
of chemical elements, (CYP-mediated) reaction classes and modeling
the full reaction paths, not only BDEs.