mlsanityData to 7 Oct 2026

JAMUN: Bridging Smoothed Molecular Dynamics and Score-Based Learning for Conformational Ensembles

arXiv 2410.14621 ↗First posted 18 Oct 2024physics.bio-ph cs.LG q-bio.BMcited by 4

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Abstract (arXiv)

Conformational ensembles of protein structures are immensely important both for understanding protein function and drug discovery in novel modalities such as cryptic pockets. Current techniques for sampling ensembles such as molecular dynamics (MD) are computationally inefficient, while many recent machine learning methods do not transfer to systems outside their training data. We propose JAMUN which performs MD in a smoothed, noised space of all-atom 3D conformations of molecules by utilizing the framework of walk-jump sampling. JAMUN enables ensemble generation for small peptides at rates of an order of magnitude faster than traditional molecular dynamics. The physical priors in JAMUN enables transferability to systems outside of its training data, even to peptides that are longer than those originally trained on. Our model, code and weights are available at https://github.com/prescient-design/jamun.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 29 of its 102 references that are in the map. First posted on arXiv 18 Oct 2024.

Builds on

29 of 102 references.

References · 29

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2020Denoising Diffusion Implicit Modelscited by 9,655
  3. 2022Flow Matching for Generative Modelingcited by 5,426
  4. 2022Elucidating the Design Space of Diffusion-Based Generative Modelscited by 2,994
  5. 2023Stochastic Interpolants: A Unifying Framework for Flows and Diffusionscited by 897
  6. 2018Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point cloudscited by 735
  7. 2021E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentialscited by 701
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  10. 2023The Impact of Positional Encoding on Length Generalization in Transformerscited by 319
  11. 2022e3nn: Euclidean Neural Networkscited by 171
  12. 2023Equivariant flow matchingcited by 131
17 more
  1. 2024AlphaFold Meets Flow Matching for Generating Protein Ensemblescited by 105
  2. 2023Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamicscited by 72
  3. 2023Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamicscited by 62
  4. 2024Generative Modeling of Molecular Dynamics Trajectoriescited by 48
  5. 2019Neural Empirical Bayescited by 46
  6. 2024Transferable Boltzmann Generatorscited by 43
  7. 20233D molecule generation by denoising voxel gridscited by 38
  8. 2023Protein Discovery with Discrete Walk-Jump Samplingcited by 38
  9. 2023Str2Str: A Score-based Framework for Zero-shot Protein Conformation Samplingcited by 29
  10. 2022DiffMD: A Geometric Diffusion Model for Molecular Dynamics Simulationscited by 24
  11. 2024Structure-based drug design by denoising voxel gridscited by 18
  12. 2023Score dynamics: scaling molecular dynamics with picoseconds timestep via conditional diffusion modelcited by 16
  13. 2024EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic Interpolantscited by 15
  14. 2023Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular Dynamicscited by 14
  15. 2024Scalable Normalizing Flows Enable Boltzmann Generators for Macromoleculescited by 10
  16. 2023H-Packer: Holographic Rotationally Equivariant Convolutional Neural Network for Protein Side-Chain Packingcited by 6
  17. 2023CREMP: Conformer-rotamer ensembles of macrocyclic peptides for machine learningcited by 5

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Cited by year

Citing papers per year: 2025: 4.

4 papers.

  1. 2025Scalable Equilibrium Sampling with Sequential Boltzmann Generatorscited by 36
  2. 2025Accelerating Protein Molecular Dynamics Simulation with DeepJumpcited by 6
  3. 2025HollowFlow: Efficient Sample Likelihood Evaluation using Hollow Message Passingcited by 5
  4. 2025Matching the Optimal Denoiser in Point Cloud Diffusion with (Improved) Rotational Alignmentcited by 0