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AlphaFold Meets Flow Matching for Generating Protein Ensembles

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arxiv 2402.04845 v2 pith:P6SIWK7Y submitted 2024-02-07 q-bio.BM cs.LG

Bowen Jing , Bonnie Berger , Tommi Jaakkola This is my paper
classification q-bio.BMcs.LG
keywords alphafoldensemblesmethodproteinsalphaflowconformationalflowgenerative
verification ladder T0 review T1 audit T2 compute T3 formal
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The biological functions of proteins often depend on dynamic structural ensembles. In this work, we develop a flow-based generative modeling approach for learning and sampling the conformational landscapes of proteins. We repurpose highly accurate single-state predictors such as AlphaFold and ESMFold and fine-tune them under a custom flow matching framework to obtain sequence-conditoned generative models of protein structure called AlphaFlow and ESMFlow. When trained and evaluated on the PDB, our method provides a superior combination of precision and diversity compared to AlphaFold with MSA subsampling. When further trained on ensembles from all-atom MD, our method accurately captures conformational flexibility, positional distributions, and higher-order ensemble observables for unseen proteins. Moreover, our method can diversify a static PDB structure with faster wall-clock convergence to certain equilibrium properties than replicate MD trajectories, demonstrating its potential as a proxy for expensive physics-based simulations. Code is available at https://github.com/bjing2016/alphaflow.

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Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Spectral Diffusion for Protein Dynamics

    q-bio.BM 2026-07 conditional novelty 6.5 of 10

    Diffusion over DCT spectral volumes of Cα displacements yields fast, temperature-conditioned protein trajectories with RMSF Pearson r of 0.844 on held-out mdCATH.

  2. Align Your Structures: Generating Trajectories with Structure Pretraining for Molecular Dynamics

    cs.LG 2026-04 conditional novelty 6.5 of 10

    Structure-pretrained diffusion plus an equivariant temporal interpolator generates chemically realistic MD trajectories on small molecules, tetrapeptides, and proteins by separating spatial and temporal learning.

  3. Neural spectroscopy of AlphaFold2 reveals encoded protein conformational landscapes

    cs.LG 2026-07 conditional novelty 6.0 of 10

    Scaled Gaussian Convolution, a deterministic scaling-plus-smoothing of AlphaFold2's weights, reveals a ubiquitin conformational landscape whose contact-loss order, flexibility pattern, and funnel topology match experi...

  4. S1-Omni: A Unified Multimodal Reasoning Model for Scientific Understanding, Prediction, and Generation

    cs.AI 2026-07 conditional novelty 6.0 of 10

    A single 32B multimodal model with task-specific decoders handles roughly 200 scientific tasks across molecules, materials, proteins, spectra, and images, and outperforms general LLMs on most of 66 evaluated tasks.

  5. Feynman-Kac-Flow: Inference Steering of Conditional Flow Matching to an Energy-Tilted Posterior

    cs.LG 2025-09 conditional novelty 6.0 of 10

    Feynman-Kac particle steering, previously diffusion-only, is derived for conditional flow matching and used to generate chirality-correct chemical transition states.

  6. Sesame: Opening the door to protein pockets

    q-bio.BM 2025-08 conditional novelty 6.0 of 10

    Sesame uses flow matching to turn apo protein backbones into holo-like backbones, outperforming a Schrödinger-bridge baseline on geometric benchmarks but with limited docking validation.

  7. FlowBack-Adjoint: Physics-Aware and Energy-Guided Conditional Flow-Matching for All-Atom Protein Backmapping

    physics.chem-ph 2025-08 conditional novelty 6.0 of 10

    FlowBack-Adjoint fine-tunes a flow-matching backmapping model with molecular mechanics energy gradients, reducing clashes and bond errors and producing lower-energy all-atom protein reconstructions.

  8. Hierarchical Rectified Flow Matching with Mini-Batch Couplings

    cs.CV 2025-07 conditional novelty 6.0 of 10

    Mini-batch couplings in data and velocity space simplify the hierarchy of velocity distributions in hierarchical rectified flow matching, improving low-step generation quality.

  9. Energy-Based Flow Matching for Generating 3D Molecular Structure

    cs.LG 2025-08 conditional novelty 5.0 of 10

    IDFlow trains a flow matching network to refine its own predicted 3D molecular structure, improving docking and protein backbone generation over HarmonicFlow and FrameFlow baselines.

  10. MolFORM: Multi-modal Flow Matching for Structure-Based Drug Design

    cs.CE 2025-07 conditional novelty 5.0 of 10

    A flow-matching model with direct preference optimization fine-tuning generates protein-binding molecules faster than diffusion baselines, with improved docking scores on the CrossDocked2020 benchmark.

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