  Accurate modeling of the solvent environment for biological molecules is
crucial for computational biology and drug design. A popular approach to
achieve long simulation time scales for large system sizes is to incorporate
the effect of the solvent in a mean-field fashion with implicit solvent models.
However, a challenge with existing implicit solvent models is that they often
lack accuracy or certain physical properties compared to explicit solvent
models, as the many-body effects of the neglected solvent molecules is
difficult to model as a mean field. Here, we leverage machine learning (ML) and
multi-scale coarse graining (CG) in order to learn implicit solvent models that
can approximate the energetic and thermodynamic properties of a given explicit
solvent model with arbitrary accuracy, given enough training data. Following
the previous ML--CG models CGnet and CGSchnet, we introduce ISSNet, a graph
neural network, to model the implicit solvent potential of mean force. ISSNet
can learn from explicit solvent simulation data and be readily applied to MD
simulations. We compare the solute conformational distributions under different
solvation treatments for two peptide systems. The results indicate that ISSNet
models can outperform widely-used generalized Born and surface area models in
reproducing the thermodynamics of small protein systems with respect to
explicit solvent. The success of this novel method demonstrates the potential
benefit of applying machine learning methods in accurate modeling of solvent
effects for in silico research and biomedical applications.
