mlsanityData to 7 Oct 2026

Protein Conformation Generation via Force-Guided SE(3) Diffusion Models

arXiv 2403.14088 ↗First posted 21 Mar 2024q-bio.BM cs.LGcited by 44

On the map: Diffusion models → Flow matching, time series and science

Abstract (arXiv)

The conformational landscape of proteins is crucial to understanding their functionality in complex biological processes. Traditional physics-based computational methods, such as molecular dynamics (MD) simulations, suffer from rare event sampling and long equilibration time problems, hindering their applications in general protein systems. Recently, deep generative modeling techniques, especially diffusion models, have been employed to generate novel protein conformations. However, existing score-based diffusion methods cannot properly incorporate important physical prior knowledge to guide the generation process, causing large deviations in the sampled protein conformations from the equilibrium distribution. In this paper, to overcome these limitations, we propose a force-guided SE(3) diffusion model, ConfDiff, for protein conformation generation. By incorporating a force-guided network with a mixture of data-based score models, ConfDiff can generate protein conformations with rich diversity while preserving high fidelity. Experiments on a variety of protein conformation prediction tasks, including 12 fast-folding proteins and the Bovine Pancreatic Trypsin Inhibitor (BPTI), demonstrate that our method surpasses the state-of-the-art method.

Key facts. Cited by 44 papers in the map (data through 7 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 20 of its 51 references that are in the map. First posted on arXiv 21 Mar 2024.

Builds on

20 of 51 references.

Closest ancestors · 7

Same direction, newest first

  1. 2023SE(3)-Stochastic Flow Matching for Protein Backbone Generation3 Oct
  2. 2023AbDiffuser: Full-Atom Generation of in vitro Functioning Antibodies28 Jul
  3. 2023EigenFold: Generative Protein Structure Prediction with Diffusion Models5 Apr
  4. 2023SE(3) diffusion model with application to protein backbone generation5 Feb
  5. 2022Protein structure generation via folding diffusion30 Sep
  6. 2022Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem8 Jun
  7. 2022Protein Structure and Sequence Generation with Equivariant Denoising Diffusion Probabilistic Models26 May

Other references · 13

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 8,964
  3. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  4. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  5. 2022Classifier-Free Diffusion Guidancecited by 5,717
  6. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,595
  7. 2022Imagen Video: High Definition Video Generation with Diffusion Modelscited by 1,765
  8. 2022Riemannian Score-Based Generative Modellingcited by 216
  9. 2023Learning Interpolations between Boltzmann Densitiescited by 42
  10. 2021Deeptime: a Python library for machine learning dynamical models from time series datacited by 36
  11. 2023Str2Str: A Score-based Framework for Zero-shot Protein Conformation Samplingcited by 29
  12. 2023Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learningcited by 26
1 more
  1. 2024Energy based diffusion generator for efficient sampling of Boltzmann distributionscited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 12; 2025: 18; 2026: 14.

44 papers.

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  1. 2025Energy-Weighted Flow Matching for Offline Reinforcement Learningcited by 70
  2. 2024Transferable Boltzmann Generatorscited by 43
  3. 2024On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs)cited by 39
  4. 2024ProteinBench: A Holistic Evaluation of Protein Foundation Modelscited by 23
  5. 2024The dark side of the forces: assessing non-conservative force models for atomistic machine learningcited by 23
  6. 2024Structure Language Models for Protein Conformation Generationcited by 16
  7. 2024Dynamic PDB: A New Dataset and a SE(3) Model Extension by Integrating Dynamic Behaviors and Physical Properties in Protein Structurescited by 8
  8. 2025Siamese Foundation Models for Crystal Structure Predictioncited by 8
  9. 2024Improving Antibody Design with Force-Guided Sampling in Diffusion Modelscited by 7
  10. 2025ConfRover: Simultaneous Modeling of Protein Conformation and Dynamics via Autoregressioncited by 7
  11. 2025UniSim: A Unified Simulator for Time-Coarsened Dynamics of Biomoleculescited by 7
  12. 2025BoltzNCE: Learning Likelihoods for Boltzmann Generation with Stochastic Interpolants and Noise Contrastive Estimationcited by 7
32 more
  1. 2025Unlocking hidden biomolecular conformational landscapes in diffusion models at inference timecited by 7
  2. 2024Force-Guided Bridge Matching for Full-Atom Time-Coarsened Dynamics of Peptidescited by 6
  3. 2025Aligning Protein Conformation Ensemble Generation with Physical Feedbackcited by 5
  4. 2026Scalable Spatio-Temporal SE(3) Diffusion for Long-Horizon Protein Dynamicscited by 4
  5. 2024AlphaFolding: 4D Diffusion for Dynamic Protein Structure Prediction with Reference and Motion Guidancecited by 3
  6. 2025PolyConf: Unlocking Polymer Conformation Generation through Hierarchical Generative Modelscited by 3
  7. 2025Learning residue level protein dynamics with multiscale Gaussianscited by 3
  8. 2025Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentialscited by 3
  9. 2025TEMPO: Temporal Multi-scale Autoregressive Generation of Protein Conformational Ensemblescited by 3
  10. 2026Protein Language Model Embeddings Improve Generalization of Implicit Transfer Operatorscited by 3
  11. 2024Differentially Private Kernel Density Estimationcited by 2
  12. 2024HJ-sampler: A Bayesian sampler for inverse problems of a stochastic process by leveraging Hamilton-Jacobi PDEs and score-based generative modelscited by 2
  13. 2025PETIMOT: A Novel Framework for Inferring Protein Motions from Sparse Data Using SE(3)-Equivariant Graph Neural Networkscited by 2
  14. 2025Generative Modeling of Full-Atom Protein Conformations using Latent Diffusion on Graph Embeddingscited by 2
  15. 2026Unified Biomolecular Trajectory Generation via Pretrained Variational Bridgecited by 2
  16. 2026Atomic Trajectory Modeling with State Space Models for Biomolecular Dynamicscited by 2
  17. 2024Efficient Antibody Structure Refinement Using Energy-Guided SE(3) Flow Matchingcited by 1
  18. 2025Learning conformational ensembles of proteins based on backbone geometrycited by 1
  19. 2025ProtPainter: Draw or Drag Protein via Topology-guided Diffusioncited by 1
  20. 2025AI-based Methods for Simulating, Sampling, and Predicting Protein Ensemblescited by 1
  21. 2025EPO: Diverse and Realistic Protein Ensemble Generation via Energy Preference Optimizationcited by 1
  22. 2026Learning Structure, Energy, and Dynamics: A Survey of Artificial Intelligence for Protein Dynamicscited by 1
  23. 2025Potential Score Matching: Debiasing Molecular Structure Sampling with Potential Energy Guidancecited by 0
  24. 2026Probing the Geometry of Diffusion Models with the String Methodcited by 0
  25. 2026From Snapshots to Symphonies: The Evolution of Protein Prediction from Static Structures to Generative Dynamics and Multimodal Interactionscited by 0
  26. 2026PI-Mamba: Linear-Time Protein Backbone Generation via Spectrally Initialized Flow Matchingcited by 0
  27. 2026Generating DDPM-based Samples from Tilted Distributionscited by 0
  28. 2026ConforNets: Latents-Based Conformational Control in OpenFold3cited by 0
  29. 2026CARD: Coarse-to-fine Autoregressive Modeling with Radix-based Decomposition for Transferable Free Energy Estimationcited by 0
  30. 2026Deep Adaptive Dimension Reduction for Bayesian Inference in Inverse Problemscited by 0
  31. 2026Learning Implicit Bias in Generative Spaces for Accelerating Protein Dynamics Emulationcited by 0
  32. 2026PhiFold: Towards Dynamic Protein Design with Physics-Structured Covariance Modelingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024F3low: Frame-to-Frame Coarse-grained Molecular Dynamics with SE(3) Guided Flow Matching
  2. 2023On Accelerating Diffusion-based Molecular Conformation Generation in SE(3)-invariant Space
  3. 2025From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering
  4. 2023Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics
  5. 2022DiffBP: Generative Diffusion of 3D Molecules for Target Protein Binding
  6. 2025Energy-Based Flow Matching for Generating 3D Molecular Structure
  7. 2023Swallowing the Bitter Pill: Simplified Scalable Conformer Generation
  8. 2025Protein-SE(3): Benchmarking SE(3)-based Generative Models for Protein Structure Design