Predicting solvation free energies with an implicit solvent machine learning potential

Solvation free energy, and notably hydration free energy, is generally recognized as a fundamental thermodynamic quantity of interest in computational chemistry. Defined as the work done when transferring a molecule from the gas phase to the solution (water in the case of hydration free energy), it enables the computation of several key physicochemical properties of molecules, such as solubility, partition coefficients, activity coefficients, and binding free energies in solutions [1, 2]. These properties are of great importance to the pharmaceutical, environmental, and materials sciences [3-9], prompting the organization of blind prediction SAMPL challenges [10-12] with hydration free energy as one of the main targets. In addition, Mobley et al. compiled and curated a FreeSolv database of experimentally measured hydration free energies for small neutral molecules in water [13, 14]. A wide spectrum of methods is available to calculate solvation free energy, ranging from traditional approaches such as continuum solvation models [15, 16] to recent machine learning (ML) algorithms [17-26] and their combinations [27-29]. The alchemical methods with Molecular Dynamics (MD) simulations [14, 30, 31] are typically assumed to be highly accurate but computationally expensive [32, 33]. However, both the fidelity and the efficiency highly depend on the explicitly treated degrees of freedom and the employed potential energy model.