Here, we have presented a novel way of developing a machine-learned QM implicit-solvent approach. The method separates the task of modeling the solvent into evaluating a continuum-based QM implicit-solvent method and adding a machine-learned explicit-solvent correction term. This term can be parametrized from classical MD simulations, which are known to capture solute-solvent effects well, thus requiring no additional training data. The resulting QM-GNNIS model has been demonstrated to outperform state-ofthe-art QM-based implicit-solvent methods and provides access to novel explanations for experiments. The gradients and Hessians, in addition to energies, enable broad applications, and the additive nature of the method allows easy implementation with existing QM software packages.