Next, we optimized the CG model of a G1 PAMAM dendrimer, a small structural variant belonging to a well-known family of dendrimers, here used as an example of relatively flexible and small symmetric branched macromolecule, for which main results are reported in Figure . Notably, a MARTINI CG model for PAMAM G5 is available from the literature and could be adapted (truncated) to obtain a smaller PAMAM G1. Geometrical features of the resulting CG model are compared to both those calculated from AA-mapped data and from the CG model adapted from the literature 63 (cf. section 6.2). We show that Swarm-CG is capable of optimizing the CG parametrization for such type of molecules, which performs even better vs. AA models compared to the CG parameters available in the literature, and adapted for smaller dendrimer generations, in terms of behavior of the dendrimer in the solvent (water). In particular, the simple truncation to G1 of the available MARTINI model for G5 PAMAM dendrimers (maintaining the very same bonded and non-bonded literature parameters) results into an overestimation of the Rg and SASA of the dendrimer in explicit water, which could be somewhat expected considered that Lee and Larson also highlighted similar slight size overestimations for G5 PAMAM when the model was developed. By providing the same non-bonded terms as an input, however, here we show that Swarm-CG is capable of easily optimizing the CG models to successfully improve the agreement with AA models for these branched macromolecules. The mismatches between CG and AA-mapped geoms distributions were again reduced by a factor of 2 during optimization, when comparing the initial set of BP to those of the optimized CG model (Figure , green line). The optimized CG model was further validated in a 200 ns simulation, in which geoms distributions correctly overlapped with those calculated from the AA-mapped trajectory (Figure ). Average Rg and SASA values were also in perfect agreement (i.e. both errors < 2%). BP optimization converged within 182 steps (Table ). We then tested the available manually-parametrized CG model of PAMAM G1 63 via a 200 ns CG-MD simulation. The Swarm-CG scoring function yielded a BP score of 123.7 for this CG model (Figure ). Noteworthy, the discrepancy in the local geometrical features with respect to the AA-mapped reference MD trajectory is considerably reduced in the optimized CG model produced by Swarm-CG, which yielded a BP score as low as 9.0 (Figure ). With respect to manual parametrization, the average Rg error in the optimized CG model provided by Swarm-CG (calculated respect to the AA reference model) is also reduced by 2.5 Å (~25%) using the optimized set of BP (see Table ).