Metal ions play a significant role in chemistry, biology, and material science. Approximately one-third of the proteins in the Protein Data Bank contain metals, serving essential structural and catalytic functions. Metal ions are also key building blocks in supramolecular chemistry, enabling the formation of complex metallo-organic cage structures and metal-organic frameworks (MOFs). Their unique properties stem from their strong directional interactions and coordination patterns, which are unavailable in carbon-based chemistry. Supramolecular structures often feature repeated metal binding sites within their architecture and even across different systems (Figure ). For instance, the majority of metal sites in palladium-based supramolecular cages are derivatives of the tetrakis(pyridine)palladium(II) building block. Various methodologies have been used to model metal-containing systems, including quantum mechanics (QM), molecular mechanics (MM) such as molecular dynamics (MD) and Monte Carlo (MC) simulations, and hybrid QM/MM. These techniques have been applied to model metalloproteins, metallo-organic cages, and MOFs. MM approaches, in particular, offer a significant advantage over QM methods in terms of computational costs. MM based approaches for deriving force-field parameters for metal ions, include the nonbonded, cationic dummy, and covalent models (Figure ). Non-bonded models describe metal centers as van der Waals spheres with integer charge, describing ligandmetal interactions through electrostatic Coulombic and Lennard-Jones (LJ) terms. Li and Merz have reported parameter sets for 56 metals, aiming to reproduce hydration-free energy (HFE), coordination number (CN), ion-oxygen distances (IOD) of aqua complexes. However, achieving this often involves identifying parameters that compromise accuracy for these specific observables, since no parameter set can reproduce all of them. Zhang sampled a larger LJ parameter space to identify parameters capable of reproducing HFE and CN for a set of 47 ions. The dummy model, originally developed by Åqvist and Warshel, places cationic dummy particles bonded to metal in a predefined coordination geometry. These particles exclusively interact with ligands via electrostatic interactions. This model provides an improvement over non-bonded models by simultaneously reproducing HFE and CN. However, its predefined configuration reduces flexibility, and only a limited number of metal ions have been parametrized. While both non-bonded and cationic dummy models can describe bond forming and breaking processes, they often exhibit unexpected behavior when transferred to different systems and fail to account for charge transfer. The covalent bond model explicitly describes bonds, angles, torsions, van der Waals and Coulombic interactions between coordinating ligands and metal ions, allowing it to account for charge transfer effects but unable to account for ligand exchange. Parametrizing the different terms is time-consuming, often relying on equilibrium bonded parameters and force constants obtained from QM calculations. The two most common approaches for obtaining bond force constants involve calculating the Hessian matrix or employing forcesmatching techniques. Among Hessian-based methods, the Seminario method is the most widely used, involving the projection of the Hessian Matrix elements onto relevant bonded parameters (Figure ; Approach 1). This procedure has been successfully applied to model small metallo-organic molecules, metalloproteins, MOFs, and supramolecular cages. In this approach, dihedral parameters involving metal ions are omitted as they usually have minimal impact on the structure. Cole and co-workers have improved the Seminario method by addressing the problem of double counting bending interactions. The LJ parameters are typically taken from non-bonded model parameters or the Universal Force-Field, which covers 126 elements, while partial charges are obtained using the restrained electrostatic potential (RESP) method, available in various software including AmberTools (via Gaussian, GAMESS-US), 78 R.E.D. server (Gaussian, GAMESS-US, Firefly), 79 psi4/resp (psi4), and psiRESP (psi4). The Seminario method and RESP calculations are computationally costly, restricting their use to small systems. For larger structures, such as metalloproteins, a cluster representing the metal binding site is often used for parametrization (Figure ; Approach 2). These parameters are then transferred to the larger system, usually involving significant manual intervention. To speed up this process, Li and Merz developed MCPB.py, offering a semi-automatic approach to derive bonded parameters and charges. MCPB.py is compatible with the AMBER forcefield, and while it is suitable for proteins with a few distinct metal sites, its use becomes laborious for supramolecular structures, as they often consist of multiple identical metal sites. Due to differences in atom order, each site would require separate QM calculations, resulting in a time-consuming procedure. Ideally, an approach similar to those available for protein parametrization (e.g., pdb2gmx in GROMACS and LeAP in AMBER), where residue parameters are tabulated and automatically assigned, tailored to metal sites and neighboring residues would significantly streamline the modelling of supramolecular structures containing repeating metal site units (Figure ).