Computing hydration free energies of small molecules with first principles accuracy

Free energies play a central role in characterising the behaviour of chemical systems and are among the most important quantities that can be calculated by molecular dynamics simulations. The free energy of hydration in particular is a well-studied physicochemical property of drug-like molecules and is commonly used to assess and optimise the accuracy of nonbonded parameters in empirical forcefields, and as a fast-to-compute surrogate of performance for protein-ligand binding free energy estimation. Machine learned potentials (MLPs) show great promise as more accurate alternatives to empirical forcefields, but are not readily decomposed into physically motivated functional forms, which has thus far rendered them incompatible with standard alchemical free energy methods that manipulate individual pairwise interaction terms. However, since the accuracy of free energy calculations is highly sensitive to the forcefield, this is a key area in which MLPs have the potential to address the shortcomings of empirical forcefields. In this work, we introduce an efficient alchemical free energy method compatible with MLPs, enabling, for the first time, calculations of biomolecular free energy with ab initio accuracy. Using a pretrained, transferrable, alchemically equipped MACE model, we demonstrate sub-chemical accuracy for the hydration free energies of organic molecules.