ArticleThe Journal of chemical physics2008
The multiscale coarse-graining method. I. A rigorous bridge between atomistic and coarse-grained models.
Article in The Journal of chemical physics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 184 papers.
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184 citing papers in PubMed.
- Coarse-Graining Reshapes Knot Dynamics in Polymers and Proteins.The journal of physical chemistry. B · 2026Article
- No-Harm Missing-Friction Correction for Potential-of-Mean-Force-Matched Coarse Graining.ACS omega · 2026Article
- Structure and Dynamics of the HIV-1 Envelope Protein on the Virion Envelope.Journal of the American Chemical Society · 2026Article
- NNP/CG-MM: Embedding of All-Atom Neural Network Potentials into a Coarse-Grained Molecular Mechanics Environment.Journal of chemical theory and computation · 2026Article
- Valency-Limited Molecular Dynamics Simulations of Stickers-and-Spacers Polymers Reveal a Tradeoff Between Condensation and Organization.bioRxiv : the preprint server for biology · 2026Article
- Membrane remodeling by the collective action of caveolin-1.Nature communications · 2026Article
- MOFF2: A Transferable Coarse-Grained Protein Force Field for Predictive Condensate Simulations.bioRxiv : the preprint server for biology · 2026Article
- Challenges and Opportunities in Machine Learning for Light-Emitting Polymers.Macromolecular rapid communications · 2026Review
- AI-based methods for simulating, sampling, and predicting protein ensembles.Current opinion in structural biology · 2026Review
- Structure and Dynamics of the HIV-1 Envelope Protein on the Virion Envelope.bioRxiv : the preprint server for biology · 2026Article
- Probabilistic Forecasting for Coarse-Grained Molecular Dynamics.Journal of chemical theory and computation · 2026Article
- Active subspace learning for coarse-grained molecular dynamics.The Journal of chemical physics · 2026Article
- Approximation of forces and torques from anisotropic pairwise interactions using multivariate polynomials.The Journal of chemical physics · 2026Article
- Lattice instability drives formation of protofilament clusters at the microtubule plus-end tips.Biophysical journal · 2026Article
- Learning data-efficient coarse-grained molecular dynamics from forces and noise.Nature communications · 2026Article
- Progressive Backmapping of Highly Coarse-Grained Protein Models.bioRxiv : the preprint server for biology · 2026Article
- Mapping Still Matters: Coarse-Graining with Machine Learning Potentials.Journal of chemical information and modeling · 2026Article
- Recent advances in machine learning and coarse-grained potentials for biomolecular simulations.Biophysical journal · 2026Review
- Energy-Based Coarse-Graining in Molecular Dynamics: A Flow-Based Framework without Data.Journal of chemical theory and computation · 2026Article
- Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials.Journal of chemical theory and computation · 2026Article
124 more citing papers are in PubMed but not listed here.
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8 authors.
Funding
Abstract
Coarse-grained (CG) models provide a computationally efficient method for rapidly investigating the long time- and length-scale processes that play a critical role in many important biological and soft matter processes. Recently, Izvekov and Voth introduced a new multiscale coarse-graining (MS-CG) method [J. Phys. Chem. B 109, 2469 (2005); J. Chem. Phys. 123, 134105 (2005)] for determining the effective interactions between CG sites using information from simulations of atomically detailed models. The present work develops a formal statistical mechanical framework for the MS-CG method and demonstrates that the variational principle underlying the method may, in principle, be employed to determine the many-body potential of mean force (PMF) that governs the equilibrium distribution of positions of the CG sites for the MS-CG models. A CG model that employs such a PMF as a "potential energy function" will generate an equilibrium probability distribution of CG sites that is consistent with the atomically detailed model from which the PMF is derived. Consequently, the MS-CG method provides a formal multiscale bridge rigorously connecting the equilibrium ensembles generated with atomistic and CG models. The variational principle also suggests a class of practical algorithms for calculating approximations to this many-body PMF that are optimal. These algorithms use computer simulation data from the atomically detailed model. Finally, important generalizations of the MS-CG method are introduced for treating systems with rigid intramolecular constraints and for developing CG models whose equilibrium momentum distribution is consistent with that of an atomically detailed model.
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