mlsanityData to 7 Oct 2026

Generative Coarse-Graining of Molecular Conformations

arXiv 2201.12176 ↗First posted 28 Jan 2022cs.LG physics.chem-ph physics.comp-phcited by 21

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Abstract (arXiv)

Coarse-graining (CG) of molecular simulations simplifies the particle representation by grouping selected atoms into pseudo-beads and drastically accelerates simulation. However, such CG procedure induces information losses, which makes accurate backmapping, i.e., restoring fine-grained (FG) coordinates from CG coordinates, a long-standing challenge. Inspired by the recent progress in generative models and equivariant networks, we propose a novel model that rigorously embeds the vital probabilistic nature and geometric consistency requirements of the backmapping transformation. Our model encodes the FG uncertainties into an invariant latent space and decodes them back to FG geometries via equivariant convolutions. To standardize the evaluation of this domain, we provide three comprehensive benchmarks based on molecular dynamics trajectories. Experiments show that our approach always recovers more realistic structures and outperforms existing data-driven methods with a significant margin.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 36 of its 73 references that are in the map. First posted on arXiv 28 Jan 2022.

Builds on

36 of 73 references.

References · 36

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24 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 8; 2024: 3; 2025: 4; 2026: 3.

21 papers.

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  1. 2022e3nn: Euclidean Neural Networkscited by 171
  2. 2022MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Designcited by 66
  3. 2022Flow-matching -- efficient coarse-graining of molecular dynamics without forcescited by 36
  4. 2023Chemically Transferable Generative Backmapping of Coarse-Grained Proteinscited by 14
  5. 2023DiffSDS: A language diffusion model for protein backbone inpainting under geometric conditions and constraintscited by 10
  6. 2023DiAMoNDBack: Diffusion-denoising Autoregressive Model for Non-Deterministic Backmapping of Cα Protein Tracescited by 10
  7. 2023PrefixMol: Target- and Chemistry-aware Molecule Design via Prefix Embeddingcited by 7
  8. 2023CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer Generationcited by 7
  9. 2024Diffusion Twigs with Loop Guidance for Conditional Graph Generationcited by 4
  10. 2023Backdiff: a diffusion model for generalized transferable protein backmappingcited by 3
  11. 2023Ophiuchus: Scalable Modeling of Protein Structures through Hierarchical Coarse-graining SO(3)-Equivariant Autoencoderscited by 3
  12. 2025Split-Flows: Measure Transport and Information Loss Across Molecular Resolutionscited by 3
9 more
  1. 2023On the Relationships between Graph Neural Networks for the Simulation of Physical Systems and Classical Numerical Methodscited by 2
  2. 2024Constraint Decoupled Latent Diffusion for Protein Backmappingcited by 2
  3. 2024Equivariant Blurring Diffusion for Hierarchical Molecular Conformer Generationcited by 2
  4. 2025Energy-Based Coarse-Graining in Molecular Dynamics: A Flow-Based Framework without Datacited by 2
  5. 2025An Iterative Framework for Generative Backmapping of Coarse Grained Proteinscited by 1
  6. 2025Operator Forces For Coarse-Grained Molecular Dynamicscited by 0
  7. 2026Coarse-Grained Boltzmann Generatorscited by 0
  8. 2026Recovering molecules from coarse-grained beads: free-energy-conditioned generative backmapping across chemical spacecited by 0
  9. 2026A strategic roadmap for an atomistic machine-learning ecosystemcited by 0