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Swarm-CG_Paper+SI_ChemRxiv_Preprint.pdf (4.79 MB)

Swarm-CG: Automatic Parametrization of Bonded Terms in Coarse-Grained Models of Simple to Complex Molecules via Fuzzy Self-Tuning Particle Swarm Optimization

revised on 08.07.2020, 07:33 and posted on 08.07.2020, 10:36 by Charly Empereur-mot, Luca Pesce, Davide Bochicchio, Claudio Perego, Giovanni M. Pavan
We present Swarm-CG, a versatile software for the automatic parametrization of bonded parameters in coarse-grained (CG) models. By coupling state-of-the-art metaheuristics to Boltzmann inversion, Swarm-CG performs accurate parametrization of bonded terms in CG models composed of up to 200 pseudoatoms within 4h-24h on standard desktop machines, using an AA trajectory as reference and default
settings of the software. The software benefits from a user-friendly interface and two different usage modes (default and advanced). We particularly expect Swarm-CG to support and facilitate the development of new CG models for the study of molecular systems interesting for bio- and nanotechnology.
Excellent performances are demonstrated using a benchmark of 9 molecules of diverse nature, structural complexity and size. Swarm-CG usage is ideal in combination with popular CG force
fields, such as e.g. MARTINI. However, we anticipate that in principle its versatility makes it well suited for the optimization of models built based also on other CG schemes. Swarm-CG is available with all its dependencies via the Python Package Index (PIP package: swarm-cg). Tutorials and demonstration data are available at:


European Research Council (ERC) Consolidator Grant no. 818776 (DYNAPOL) to GM Pavan

Swiss National Science Foundation (SNSF) grant 200021_175735 to GM Pavan

Swiss National Science Foundation (SNSF) grant IZLIZ2_183336 to GM Pavan


Email Address of Submitting Author


Politecnico di Torino



ORCID For Submitting Author


Declaration of Conflict of Interest

No conflicts of interest