Machine learning has been proposed to bridge this gap and in previous studies , we have shown for the case of water that a graph neural network (GNN) trained on reference forces from explicit-solvent simulations can represent the solvent in a probabilistic way and, hence, accurately simulate a system using many fewer degrees of freedom. This reduces the computational effort significantly while providing comparable results to explicit-solvent simulations. The approach is inspired by the concept of ∆-learning and incorporates the functional form of the traditional GB-SA model as a physics-based regularization to reproduce the reference forces. This ensures the model's correct physical behavior.