mlsanityData to 14 Oct 2026

Navigating protein landscapes with a machine-learned transferable coarse-grained model

arXiv 2310.18278 ↗First posted 27 Oct 2023q-bio.BM physics.bio-ph physics.chem-phcited by 23

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Abstract (arXiv)

The most popular and universally predictive protein simulation models employ all-atom molecular dynamics (MD), but they come at extreme computational cost. The development of a universal, computationally efficient coarse-grained (CG) model with similar prediction performance has been a long-standing challenge. By combining recent deep learning methods with a large and diverse training set of all-atom protein simulations, we here develop a bottom-up CG force field with chemical transferability, which can be used for extrapolative molecular dynamics on new sequences not used during model parametrization. We demonstrate that the model successfully predicts folded structures, intermediates, metastable folded and unfolded basins, and the fluctuations of intrinsically disordered proteins while it is several orders of magnitude faster than an all-atom model. This showcases the feasibility of a universal and computationally efficient machine-learned CG model for proteins.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). Builds on 22 of its 136 references that are in the map. First posted on arXiv 27 Oct 2023.

Builds on

22 of 136 references.

References · 22

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2017Graph Attention Networkscited by 8,923
  3. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
  4. 2021E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentialscited by 708
  5. 2022MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fieldscited by 474
  6. 2022TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentialscited by 182
  7. 2021SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effectscited by 104
  8. 2023Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamicscited by 72
  9. 2018Machine Learning of coarse-grained Molecular Dynamics Force Fieldscited by 69
  10. 2020Coarse Graining Molecular Dynamics with Graph Neural Networkscited by 56
  11. 2022Geometrically Equivariant Graph Neural Networks: A Surveycited by 56
  12. 2023Ewald-based Long-Range Message Passing for Molecular Graphscited by 46
10 more
  1. 2022So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systemscited by 39
  2. 2022Machine Learning Coarse-Grained Potentials of Protein Thermodynamicscited by 37
  3. 2022Flow-matching -- efficient coarse-graining of molecular dynamics without forcescited by 36
  4. 2023Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic sizecited by 33
  5. 2018Coarse-Graining Auto-Encoders for Molecular Dynamicscited by 30
  6. 2021Machine Learning Implicit Solvation for Molecular Dynamicscited by 17
  7. 2022Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulationscited by 9
  8. 2023Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamicscited by 9
  9. 2022Contrastive Learning of Coarse-Grained Force Fieldscited by 7
  10. 2016Predictive Coarse-Grainingcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 5; 2025: 7; 2026: 11.

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  1. 2024Transferable Boltzmann Generatorscited by 46
  2. 2025Consistent Sampling and Simulation: Molecular Dynamics with Energy-Based Diffusion Modelscited by 23
  3. 2026Enhanced Diffusion Sampling: Efficient Rare Event Sampling and Free Energy Calculation with Diffusion Modelscited by 13
  4. 2025Transferable Generative Models Bridge Femtosecond to Nanosecond Time-Step Molecular Dynamicscited by 11
  5. 2024Predicting solvation free energies with an implicit solvent machine learning potentialcited by 10
  6. 2025BoltzNCE: Learning Likelihoods for Boltzmann Generation with Stochastic Interpolants and Noise Contrastive Estimationcited by 7
  7. 2024Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular Representationscited by 5
  8. 2024AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamicscited by 3
  9. 2025Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentialscited by 3
  10. 2025AI-based Methods for Simulating, Sampling, and Predicting Protein Ensemblescited by 1
  11. 2026Learning Structure, Energy, and Dynamics: A Survey of Artificial Intelligence for Protein Dynamicscited by 1
  12. 2026ConSolv: Solvent-Conditional Machine Learning Implicit Solvent Potentialcited by 1
11 more
  1. 2026Implicit Machine Learning Force Fields Accelerate Molecular Dynamics Simulationscited by 1
  2. 2024Efficient Generation of Molecular Clusters with Dual-Scale Equivariant Flow Matchingcited by 0
  3. 2025Universally applicable and tunable graph-based coarse-graining for Machine learning force fieldscited by 0
  4. 2025Operator Forces For Coarse-Grained Molecular Dynamicscited by 0
  5. 2026Coarse-Grained Boltzmann Generatorscited by 0
  6. 2026FlashSchNet: Fast and Accurate Coarse-Grained Neural Network Molecular Dynamicscited by 0
  7. 2026Hessian Matching for Machine-Learned Coarse-Grained Molecular Dynamicscited by 0
  8. 2026All-atomistic Transferable Neural Potentials for Protein Solvationcited by 0
  9. 2026Temperature transferable Machine Learned Coarse Grained model for proteinscited by 0
  10. 2026Transferable Implicit Solvent Machine Learning Potential for Drugs and Proteins Approaching Ab Initio Accuracycited by 0
  11. 2026Looking Under the Streetlight: Evaluation in Generative Molecular Dynamicscited by 0