Perspective: Coarse-grained models for biomolecular systems.

By W. Noid. Journal of Chemical Physics, 2013

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Ensemble learning of coarse-grained molecular dynamics force fields with a kernel approach. (2020, background): “Although the deﬁnition of a coarse-graining mapping scheme is per se an interesting problem 23,62–64 , here we start by assuming that a mapping is given.”
Coarse graining molecular dynamics with graph neural networks. (2020, methodology): “In a “top down” scheme, coarse graining frameworks are explicitly designed to re-produce certain macroscale emergent properties 14 .”
Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials. (2016, background): “The assumption of independent degrees of freedom is a good approximation for nonbonded pair potentials when CG sites are dilute and for bond potentials when the CG bonds are stiff [81].”
Machine learning for molecular simulation (2019, background): “An alternative way to learn (6) is by using force matching [66, 67].”
Physics-driven coarse-grained model for biomolecular phase separation with near-quantitative accuracy (2021, methodology): “There are numerous possible approaches for parameterizing biomolecular coarse-grained models 19 , from bottom-up strategies that rely on higher-resolution models 9,10,20–22 , to knowledge-driven approaches”