MLSolv-A: A Novel Machine Learning-Based Prediction of Solvation Free Energies from Pairwise Atomistic Interactions

The importance of solvation or hydration mechanism and accompanying free energy change has made various in silico calculation methods for the solvation energy one of the most important application in computational chemistry[1-25]. The solvation free energy directly influences many chemical properties in solvated phases and plays a dominant role in various chemical reactions: drug delivery[2, 16, 18, 26], organic synthesis[27], electrochemical redox reactions[28-31], etc. The atomistic computer simulation approaches for the solvent and the solute molecules directly offer the microscopic structure of the solvation shell, which surrounds the solutes molecule[7, 8, 13, 17, 18, 32]. The solvation shell structure could provide us detailed physicochemical information like microscopic mechanisms on solvation or the interplay between the solvent and the solute molecules when we use an appropriate force field and molecular dynamics parameters. However, those explicit solvation methods we stated above need an extensive amount of numerical calculations since we have to simulate each individual molecule in the solvated system. The practical problems on the explicit solvation model restrict its applications to classical molecular mechanics simulations[7, 8, 17] or a limited number of QM/MM approaches[13, 32]. For classical mechanics approaches for macromolecules or calculations for small compounds at quantum-mechanical level, the idea of implicit solvation enables us to calculate solvation energy with feasible time and computational costs when one considers a given solvent as a continuous and isotropic medium in the Poisson-Boltzmann equation[1, 3-6, 9, 11, 15, 23, 24]. Many theoretical advances have been introduced to construct the continuum solvation model, which involves parameterized solvent properties: the polarizable continuum model (PCM)[9], the conductor-like screening model (COSMO)[1] and its variations[6, 33], generalized Born approximations like solvation model based on density (SMD)[5] or solvation model 6, 8, 12, etc. (SMx)[4, 11]. The structure-property relationship (SPR) is rather a new approach, which predicts the solvation free energy with a completely different point of view when compared to computer simulation approaches with precisely defined theoretical backgrounds[34, 35].