In many research fields, innovation passes through the design and development of new types of functional materials and molecular systems with controllable properties. The shape and functions of such complex nanostructures typically originates from the collective behavior of a large number of interacting molecules, as it is the case e.g. in lipid membranes, supramolecular polymers, crystals, cages, etc. The investigation of these molecular systems at a sufficiently high (submolecular) resolution is most often a prohibitive task both experimentally and computationally, especially when these are composed of large, soft and flexible macromolecules in solution. Alongside with experimental studies, molecular modeling techniques such as Molecular Dynamics (MD) or Monte-Carlo simulations have turned out to be cornerstone tools to this purpose. Recent advances in computational hardware and simulation software have made possible to study and model increasingly larger molecular systems, allowing the investigation of their structural properties with great (atomisticlevel) detail. However, the large number of degrees of freedom of these calculations still limits classical all-atom MD simulations (AA-MD) to the study of systems with a maximum of ~10 6 atoms (including the solvent, in e.g. explicit solvent simulations) and within the timescales of nano-to microseconds. Furthermore, AA-MD may typically suffer of limited sampling, especially in the simulation of complex molecules, with the risk of entrapment and oversampling of local minima and metastable states. As a consequence, AA-MD simulations cannot be practically employed for the observation of many crucial phenomena and molecular events occurring on long characteristic timescales. A typical approach to overcome these limitations is coarse-graining, which consists in simplifying the description of the molecular model, reducing the resolution by grouping several atoms in coarse-grained (CG) beads (aka pseudo-atoms). The objective of CG modeling is to reduce the number of degrees of freedom to be treated in the simulations, while still providing a physically-relevant representation of the molecular systems. Different CG frameworks have gained popularity by allowing to simulate complex molecular systems, and their dynamical properties, such as lipid bilayers, vesicles, proteins, and various types of nanomaterials. Perhaps one of the most widely used CG schemes is the MARTINI force field, which maps molecular fragments composed of ~3-4 heavy atoms into each CG bead. In principle, in this scheme each CG bead interacts with the others in the system (solute-solute and solutesolvent interactions) via a non-directional 12-6 Lennard-Jones (LJ) potential, parametrized according to the estimated partitioning of its associated molecular fragment between aqueous and hydrophobic environments. MARTINI provides a predefined set of CG beads spanning a range of polarities, together with a matrix of LJ interactions between bead types (i.e. the MARTINI force field). This offers a relative transferability of the FF, which is useful to create CG molecular models for various types of molecules, from biomolecules such as lipids, peptides and proteins, to synthetic molecules such as polymers, fullerenes, etc. In the MARTINI scheme, building an adequate CG model for a molecule of interest requires to map the constitutive molecular fragments in the AA model to CG beads, which types are opportunely chosen based on chemical analogy and polarities of the groups, setting up the non-bonded interactions in the CG model. The non-bonded parameters can then be refined to obtain accurate pairwise interactions between the molecular species in the system. For example, enhanced sampling methods such as umbrella sampling or metadynamics can be applied to calculate dimerization free-energies between pairs of molecular species in the CG vs. AA models, which allows to optimize the CG models by matching the two observables. When available, experimental data can also be exploited. Then, the user has to determine appropriate intramolecular bonded interactions between CG beads, namely the bonds, angles and dihedrals parameters, in terms of equilibrium values and force constants. Noteworthy, while the optimization of bonded parameters may seem to some extent less important than that of non-bonded ones for the correct modeling of inter-molecular interactions, it is in principle just as relevant, since C3symmetric benzotrithiophene (BTT) decorated by L-phenylalanine (BTT-F) and octaethylene glycol side-chains, naphtalene diimide (NDI)-based and Zn-porphyrin-based molecules. (b) Examples of cyclic structures: β-Cyclodextrin and a pillar [5]arene. (c) Complex hyper-branched polymer structures: spermine dendron and poly(amidoamine) dendrimers of generation 1 and 2 (PAMAM G1 and G2). Each panel indicates the color coding of the CG MARTINI beads types (see SI for exact mapping data).