mlsanityData to 7 Oct 2026

Machine Learning Coarse-Grained Potentials of Protein Thermodynamics

arXiv 2212.07492 ↗First posted 14 Dec 2022q-bio.BM cs.LGcited by 37

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Abstract (arXiv)

A generalized understanding of protein dynamics is an unsolved scientific problem, the solution of which is critical to the interpretation of the structure-function relationships that govern essential biological processes. Here, we approach this problem by constructing coarse-grained molecular potentials based on artificial neural networks and grounded in statistical mechanics. For training, we build a unique dataset of unbiased all-atom molecular dynamics simulations of approximately 9 ms for twelve different proteins with multiple secondary structure arrangements. The coarse-grained models are capable of accelerating the dynamics by more than three orders of magnitude while preserving the thermodynamics of the systems. Coarse-grained simulations identify relevant structural states in the ensemble with comparable energetics to the all-atom systems. Furthermore, we show that a single coarse-grained potential can integrate all twelve proteins and can capture experimental structural features of mutated proteins. These results indicate that machine learning coarse-grained potentials could provide a feasible approach to simulate and understand protein dynamics.

Key facts. Cited by 37 papers in the map (data through 7 Oct 2026). Builds on 15 of its 94 references that are in the map. First posted on arXiv 14 Dec 2022.

Builds on

15 of 94 references.

References · 15

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  3. 2015Variational Inference with Normalizing Flowscited by 2,161
  4. 2015Convolutional Networks on Graphs for Learning Molecular Fingerprintscited by 751
  5. 2022TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentialscited by 179
  6. 2021SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effectscited by 103
  7. 2018Machine Learning of coarse-grained Molecular Dynamics Force Fieldscited by 68
  8. 2020OrbNet: Deep Learning for Quantum Chemistry Using Symmetry-Adapted Atomic-Orbital Featurescited by 63
  9. 2020Coarse Graining Molecular Dynamics with Graph Neural Networkscited by 56
  10. 2020TorchMD: A deep learning framework for molecular simulationscited by 55
  11. 2018Coarse-Graining Auto-Encoders for Molecular Dynamicscited by 30
  12. 2021Learning neural network potentials from experimental data via Differentiable Trajectory Reweightingcited by 26
3 more
  1. 2021Machine Learning Implicit Solvation for Molecular Dynamicscited by 16
  2. 2022Accurate Machine Learned Quantum-Mechanical Force Fields for Biomolecular Simulationscited by 9
  3. 2020Ensemble Learning of Coarse-Grained Molecular Dynamics Force Fields with a Kernel Approachcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 6; 2024: 10; 2025: 12; 2026: 9.

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  1. 2023Navigating protein landscapes with a machine-learned transferable coarse-grained modelcited by 22
  2. 2024TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulationscited by 15
  3. 2024EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic Interpolantscited by 15
  4. 2023DiAMoNDBack: Diffusion-denoising Autoregressive Model for Non-Deterministic Backmapping of Cα Protein Tracescited by 10
  5. 2023Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamicscited by 9
  6. 2024F3low: Frame-to-Frame Coarse-grained Molecular Dynamics with SE(3) Guided Flow Matchingcited by 9
  7. 2024Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimatorscited by 8
  8. 2025Transferable Generative Models Bridge Femtosecond to Nanosecond Time-Step Molecular Dynamicscited by 8
  9. 2025Accelerating Protein Molecular Dynamics Simulation with DeepJumpcited by 6
  10. 2025MarS-FM: Generative Modeling of Molecular Dynamics via Markov State Modelscited by 6
  11. 2024Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular Representationscited by 5
  12. 2023Analyzing the Performance of Deep Encoder-Decoder Networks as Surrogates for a Diffusion Equationcited by 4
25 more
  1. 2023Top-down machine learning of coarse-grained protein force-fieldscited by 4
  2. 2024Generative artificial intelligence for computational chemistry: a roadmap to predicting emergent phenomenacited by 3
  3. 2024AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamicscited by 3
  4. 2025Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentialscited by 3
  5. 2024Constraint Decoupled Latent Diffusion for Protein Backmappingcited by 2
  6. 2025Protein Structure-Function Relationship: A Kernel-PCA Approach for Reaction Coordinate Identificationcited by 1
  7. 2025TICA-Based Free Energy Matching for Machine-Learned Molecular Dynamicscited by 1
  8. 2025AI-based Methods for Simulating, Sampling, and Predicting Protein Ensemblescited by 1
  9. 2025A Standardized Benchmark for Machine-Learned Molecular Dynamics using Weighted Ensemble Samplingcited by 1
  10. 2026Bridging the Simulation-to-Experiment Gap with Generative Models using Adversarial Distribution Alignmentcited by 1
  11. 2026Learning Structure, Energy, and Dynamics: A Survey of Artificial Intelligence for Protein Dynamicscited by 1
  12. 2026Implicit Machine Learning Force Fields Accelerate Molecular Dynamics Simulationscited by 1
  13. 2023A cyclical route linking fundamental mechanism and AI algorithm: An example from tuning Poisson's ratio in amorphous networkscited by 0
  14. 2024Deep Signature: Characterization of Large-Scale Molecular Dynamicscited by 0
  15. 2024OpenQDC: Open Quantum Data Commonscited by 0
  16. 2025Deep Neural Network for Phonon-Assisted Optical Spectra in Semiconductorscited by 0
  17. 2025Predicting the Energy Landscape of Stochastic Dynamical System via Physics-informed Self-supervised Learningcited by 0
  18. 2025Universally applicable and tunable graph-based coarse-graining for Machine learning force fieldscited by 0
  19. 2025Operator Forces For Coarse-Grained Molecular Dynamicscited by 0
  20. 2026Coarse-Grained Boltzmann Generatorscited by 0
  21. 2026FlashSchNet: Fast and Accurate Coarse-Grained Neural Network Molecular Dynamicscited by 0
  22. 2026Hessian Matching for Machine-Learned Coarse-Grained Molecular Dynamicscited by 0
  23. 2026Temperature transferable Machine Learned Coarse Grained model for proteinscited by 0
  24. 2026Learning Topological Representations of Protein Structure and Dynamicscited by 0
  25. 2026Transferable Implicit Solvent Machine Learning Potential for Drugs and Proteins Approaching Ab Initio Accuracycited by 0