In previous studies, we have developed graph neural network (GNN) based implicit solvent model (GNNIS), which was trained on forces extracted from classical MD simulations with explicit solvent . While GNNIS yielded excellent results compared to the reference explicit-solvent simulations as well as experimental observables, replicating this approach directly for QM calculations is made prohibitively expensive by the necessary reference QM/MM calculations for the training data. Furthermore, all of these approaches were tailored for classical force fields, which are limited in accuracy and transferability. Use cases that justify more expensive QM calculations include studies, in which the electronic structure of the system itself is important, the topology of the system cannot be described with a classical approach, or when no suitable parameters are available.