The straightforward method to calculate MBIS charges in vacuo necessitates a fully converged ground-state electronic density associated with the geometry of interest. Using an ab initio method such as density functional theory (DFT) to obtain this electronic density would, however, defeat the purpose of our proposed ML/MM scheme, since this step would constitute the bottleneck of the method. In view of this, we decided to leverage the known capabilities of the ANI architecture to accurately reproduce atomic charges, and trained a novel neural network model, which we will refer to as ANI-MBIS-q, that directly predicts MBIS charges from the local chemical environment (coordinates and atomic numbers) of each atom, bypassing the need for the electronic density. Furthermore, since ANI-MBIS-q is implemented in PyTorch, it can leverage its autograd mechanism to produce also the gradients of the atomic charges with respect to the coordinates, which is fundamental for enabling the implementation of our ML/MM scheme.