The following sections describe the results obtained for automatic bonded parametrization of CG models included in the Swarm-CG benchmark (Figure ), using default settings of the program. Here we focus on synthetic structures as: (i) being the core activity of our group, we have a good amount of available data to test the performance and accuracy of Swarm-CG and (ii) because it is for the simulation of synthetic molecular systems that the development of de novo AA and CG models from scratch is most often required, while the accuracy of such models is clearly critical for the reliability of the results that these can provide. This can be a time-consuming activity, in which the advantages of an automatic tool such as Swarm-CG combined with a general CG force field such as MARTINI are more evident. The benchmark synthetic molecules that we use herein were selected for their structural diversity in terms of molecular flexibility, symmetry and complexity (cf. section 6.2). We first created AA models and generated AA-MD trajectories (up to 1 µs of simulation) for each single molecule in explicit solvent (while most of the cases studied herein are in water, the approach is versatile to treat molecules in various solvents, as it is shown in the case of the NDI and porphyrin based structures of Figure , studied in methyl cyclohexane), guaranteeing well-sampled references for the automatic bonded parametrization of the CG models. CG models were already available from the literature for 7 of the 9 benchmarking molecules (BTA, BTT, NDI, Zn-porphyrin based molecule, β-Cyclodextrin, PAMAM dendrimers of generation G1 and G2 ), which allowed us to challenge Swarm-CG performances with respect to manually parametrized CG-models previously developed by expert molecular modelers. To this end, we used the available CG models as provided (notably for what pertains to mapping and non-bonded interactions) and allowed Swarm-CG to modify exclusively the equilibrium values and force constants of each bonded potential function defined in the available CG topology (cf. section 6.2). For the 2 other molecules (pillar [5]arene and spermine dendron ), CG models were built in the framework of MARTINI and optimized using AA models previously reported by our group (cf. section 6.2). For all models, bonded parametrizations were evaluated using: (i) Swarm-CG scoring function, which assesses the local geometrical features of a CG model, together with (ii) Rg and SASA, which provide a global evaluation of its molecular dynamics. We first tackle relatively small and flexible molecules forming supramolecular polymers in solution, that we use as examples to discuss in detail the differences between execution mode 1 vs. mode 2. Then we report the results of Swarm-CG for the parametrization of relatively rigid cyclic molecular structures. Finally, we increase molecular complexity by parametrizing complex hyper-branched directional and nondirectional macromolecules, such as dendrons and dendrimers. Since the simulation times used in the optimization runs (10-25 ns by default in the examples reported herein) might be insufficient in some cases to get well-converged Rg and SASA data at each step of the optimization process, Rg and SASA values presented for the selected (i.e. best scored) set of BP are all issued from 200 ns validation simulations that are conducted at the end of the optimization procedure. This also allowed to verify that all optimized models are stable in CG simulation using a standard integration time step of 20 fs. All average Rg values obtained for optimized CG models using execution mode 1 are summarized in Table and compared to the available manually parametrized CG models. The number of iterative optimization steps used for each model is determined according to the formula previously described in section 2.3, while execution times are reported in Table (hardware is detailed in Table ).