On the other hand, hybrid MM/ML models like Espaloma have replaced the discrete atom-typing scheme with continuous atomic representations generated by graph NNs. These representations are trained on small molecules, peptides, and nucleic acids, and are then mapped to MM parameters for atoms, bonds, angles, and torsions. This approach enhances chemical diversity, improves accuracy, and expands the applicability of MM potentials to more relevant molecular systems, e.g., protein-ligand complexes. The GRAPPA model (29) adopts a similar strategy but includes a transformer symmetry-preserving positional encoding to map the MM parameters. This design allows GRAPPA to outperform traditional MM potentials such as Amber99SB-ILDN (74) and Gaff-2.11 (75), achieving higher accuracy while maintaining the same computational efficiency. Its capabilities are demonstrated through MD simulations of the Satellite Tobacco Mosaic Virus (STMV).