Molecular mechanics (MM) potentials represent molecular systems as atomic point masses interacting through physical models that describe bonded and non-bonded interatomic interactions. While efficient and capable of handling long-time-scale simulations, MM potentials have notable limitations in accurately describing QM effects in complex (bio)molecules. These limitations arise from geometrical and electronic constraints in the parameterization procedure, which affect their transferability and generalizability. To address these shortcomings, recent efforts have focused on using QM-based ML potentials to simulate intramolecular interactions within the solute, while solvent-solvent and solvent-solute interactions are treated using MM potentials. For instance, the hybrid ANI-2x/AMOEBA approach (24) was developed to achieve CCSD(T) precision using the ANI-2x potential for describing interatomic interactions in proteins and nucleic acids, combined with the AMOEBA polarizable potentials for modeling interactions with the chemical environment. Comparisons between simulations using AMOEBA alone and the ANI-2x/AMOEBA approach demonstrate that incorporating ANI-2x significantly improves the quality and accuracy of the simulations. Instead of relying on a pre-trained model, the ANA2B model ( ) employs an anisotropic MPNN to estimate intramolecular interactions and a fully connected NNs to parameterize short-range intermolecular pairwise interactions. These short-range interactions are further complemented by D3 dispersion correction and other long-range intermolecular interactions, which are modeled using physically motivated terms derived from MM potentials. Although ANA2B is not explicitly trained on condensed-phase systems, it demonstrates strong transferability, making it a promising candidate for simulations in such environments. Building on this concept, the ONIOM (Our own N-layered Integrated molecular Orbital and molecular Mechanics) approach combines ML potentials at multiple levels of theory (e.g., semi-empirical, coupled-cluster, and DFT) to describe the core parts of biomolecules. This multiscale strategy enables highly accurate quantum refinement of different protein-drug/inhibitor systems with computational costs significantly lower than those of DFT methods. Such capabilities make ONIOM particularly suitable for modern structural determination methods of biomacromolecules (e.g., Cryo-EM, MicroED), advancing the application of ML-driven methodologies in biomolecular simulations.