Because it has a relatively large surface area, SOM is an important sorbent for organic and inorganic substances. Mainly negatively charged functional groups increase the cation exchange capacity. Conversely, less-soluble hydrophobic substances can be bound to hydrophobic areas, which are also present in SOM. Furthermore, SOM is crucial for the development of a stable soil structure through various aggregation processes . As an important part of ecosystems, soils fulfill many functions both for humans and the environment. The presence of chemical contaminants and physical soil degradation potentially endangers these soil functions . Examples of contaminants include persistent organic pollutants (POPs) , micro-and nanoplastics , heavy metals , and antibiotics 8 , among others. Ongoing research is being conducted to determine the influence of these various compounds on the soil. Different modeling approaches have been presented for undertaking such investigations . Molecular dynamics (MD) simulations are a powerful method for revealing the dynamics and interactions that occur at the nanoscale level. It is of major interest to better understand sorption processes with SOM. For instance, interactions between carbon nanotubes and nanoplastic particles were previously investigated by our group, using the Martini force field . Among other aspects, this allowed the cavity and trapping effects to be investigated, which are similar to the adsorption processes that occur in SOM. A systematic modeling approach was introduced in the form of the Vienna Soil Organic Matter Modeler (VSOMM) . This is a tool for producing condensed-phase models of SOM, more specifically HSs. It uses a fragment database to construct HS molecular structures based on input parameters. Thus, it provides the user with a series of distinct models, that are diverse in structure and composition, reflecting the heterogeneity and complexity of SOM. Numerous SOM systems have been investigated using this modeler 2,18-21 , including interactions with proteins and mineral surfaces , among others. However, the applicability of MD simulations can be limited by the system size and time scales of the processes of interest. For instance, modeling supramolecular SOM structures, and their interactions with long-chained carbohydrates or complex proteins, would require to take into account a large number of atoms in the simulation. The range of adsorption processes and free energy calculations to be investigated is often limited by computational resources as well. In such cases, the efficiency of the MD method could be compromised. One way to speed up simulations is to use a coarse-grained instead of an atomistic representation of the molecules . Thereby, atoms are grouped into new particles, so-called beads, which effectively reduce the number of particles and interactions in the system. The link between the two representations is known as mapping. Furthermore, coarse-graining includes finding new interaction parameters for the beads. Over the last few decades, various coarse-graining procedures have been introduced, among them approaches leveraging machine learning methodologies . Similarly to atomistic methods, force fields for coarsegrained simulations exist . Of these, the Martini force field is the most commonly used. Martini uses a building block approach with pre-parametrized beads that are applicable to a broad range of chemical compounds. Recently, a new version, Martini 3, was published, which involved a complete reparametrization of the force field . New types of beads were introduced, including three different applicable bead sizes. In addition, a variety of molecules that have been parametrized for Martini are available. However, the absence of a SOM model that aligns with the latest version of the Martini force field, while adhering to the concept of parametrizing new molecular systems, hinders the simulation of various SOM compositions. Addressing this gap is the primary focus of this study. While the first version of the VSOMM has been transferred to a coarse-grained representation using Dissipative particle dynamics (DPD) , a generalized coarse-grained version of the VSOMM2 compatible with Martini 3 would benefit from integrating the already parametrized molecules. This integration would enable the investigation of their interactions with SOM. Thus, employing a coarsegrained representation would facilitate simulations of adsorption processes and free energy calculations for macromolecular SOM systems, enhancing computational efficiency. Therefore, this study aims to present and test a coarse-grained representation of selected VSOMM2 models. This is done by parametrizing the fragments of the modeler using Martini 3 and with the Swarm-CG 33 tool. As an example, a coarse-grained model of the Leonardite Humic Acid (LHA) is created, which has been previously studied using MD simulations . The accuracy of the model is tested against thermodynamic properties and atomistic simulations.