As foundational physically inspired ML potentials expand their applicability to multiple (bio)molecular spaces, their design must be carefully optimized to ensure effective extrapolation to unknown conformations, which often arise in simulations of biological processes. Incorporating active or transfer learning algorithms into the training of these ML potentials provides a powerful approach to exploring a vast conformational space, thereby facilitating compliance with the EAST requirements. The reduction of model parameters, resulting from improved training sample generation, along with efficient large-scale ML algorithms, such as those implemented in the JAX library (154), will improve inference time and enable QM-based biomolecular simulations on nano-to microsecond timescales. Another crucial factor in developing robust ML potentials is incorporating physical models to accurately capture long-range interactions during their construction. As discussed above, recent efforts have shown that explicitly incorporating physically inspired functional forms to treat these interactions in ML potential development can overcome the limitations of the locality assumption -a key feature for scalability and transferability -without increasing the complexity of molecular representations. Despite these advancements, a deeper understanding of how the choice of physical models affects the performance of ML potentials remains lacking, particularly in complex simulations such as ligand binding in proteins or allosteric response. Alternative approaches, such as ML-augmented semiempirical models and hybrid MM/ML methods, also face challenges that require further investigation. Among these challenges are ensuring robust extrapolation to unrepresented electronic environments (e.g., charged systems, excited states) and optimizing their integration with existing computational frameworks (e.g., DFTB, FHI-aims, GROMACS, CP2K).