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Abstract
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: www.github.com/GMPavanLab/SwarmCG.
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: www.github.com/GMPavanLab/SwarmCG.
