We then optimized the CG model of PAMAM G2, for which main results are reported in Figure . PAMAM G2 is here used as an example of a more complex, symmetric branched macromolecule. As for PAMAM G1, a MARTINI CG model for PAMAM G2 was adapted from the literature and used for comparison. Also in this case, we show that Swarm-CG is capable of retrieving a reliable parametrization for such type of molecules, which performs even better vs. AA models compared to the existing CG parameters in terms of behavior of the G2 PAMAM dendrimer in the water, obtaining an improvement in the CG model performance in line with those discussed above for G1 PAMAM. 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 (cf. section 6.2). The mismatches between CG and AA-mapped geoms distributions were effectively reduced during optimization (Figure , green line). Geoms distributions of the optimized CG model correctly overlapped with those calculated from the AA-mapped trajectory in a 200 ns validation simulation (Figure ). However, average Rg and SASA values were approximately 1.3 Å (10%) and 8 nm 2 (16%) lower with respect to the AA-mapped reference (all values are reported in Table ). In this case, we can safely assume that the CG topology is valid. Thus, as we proved BP are correctly tuned according to a reference AA-mapped trajectory, the cause of the residual Rg and SASA offset cannot be directly attributed to errors in the bonded parametrization. In this perspective, this can be imputed: (i) to the non-bonded interaction parameters, which could possibly slightly underestimate interactions of the molecule with solvent or overestimate intramolecular interactions, or (ii) to sampling limitations in the AA-MD trajectory (oversampling of determined molecular configurations, possible for particularly complex molecules). We also underline that, since non-bonded terms used for the optimization of PAMAM G1 and G2 are identical, in the case of sub-optimal non-bonded parametrization Swarm-CG consequently adjusts the bonded terms to compensate and to reproduce at best in the CG model the behavior of the AA reference, exclusively based on geoms distributions. These observations highlight the potential of Swarm-CG as a diagnostic tool for CG modeling, which provides hints for model refinement beyond its primary purpose of automatic bonded parametrization. We compared the automatically optimized CG model of G2 PAMAM with the manually parametrized one obtained using parameters available from the literature via a 200 ns CG-MD simulation. Swarm-CG scoring function yielded a BP score of 123.8 for the manually optimized CG model. Again, the discrepancies in local geometrical features calculated with respect to the AA-reference trajectory were considerably reduced in the Swarm-CG optimized CG model (Figure ), which provides a BP score of 23.0 (Figure ).