AI-based Methods for Simulating, Sampling, and Predicting Protein Ensembles
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Abstract (arXiv)
Advances in deep learning have opened an era of abundant and accurate predicted protein structures; however, similar progress in protein ensembles has remained elusive. This review highlights several recent research directions towards AI-based predictions of protein ensembles, including coarse-grained force fields, generative models, multiple sequence alignment perturbation methods, and modeling of ensemble descriptors. An emphasis is placed on realistic assessments of the technological maturity of current methods, the strengths and weaknesses of broad families of techniques, and promising machine learning frameworks at an early stage of development. We advocate for "closing the loop" between model training, simulation, and inference to overcome challenges in training data availability and to enable the next generation of models.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 30 of its 83 references that are in the map. First posted on arXiv 21 Sep 2025.
Builds on
30 of 83 references.
References · 30
- 2023Denoising Diffusion Samplerscited by 149
- 2017VAMPnets: Deep learning of molecular kineticscited by 136
- 2022Flow Annealed Importance Sampling Bootstrapcited by 127
- 2024Iterated Denoising Energy Matching for Sampling from Boltzmann Densitiescited by 116
- 2024AlphaFold Meets Flow Matching for Generating Protein Ensemblescited by 110
- 2025Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matchingcited by 85
- 2023Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamicscited by 72
- 2018Machine Learning of coarse-grained Molecular Dynamics Force Fieldscited by 69
- 2023Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamicscited by 66
- 2020Coarse Graining Molecular Dynamics with Graph Neural Networkscited by 56
- 2024Generative Modeling of Molecular Dynamics Trajectoriescited by 51
- 2024Transferable Boltzmann Generatorscited by 46
18 more
- 2024Protein Conformation Generation via Force-Guided SE(3) Diffusion Modelscited by 45
- 2022Machine Learning Coarse-Grained Potentials of Protein Thermodynamicscited by 37
- 2022Flow-matching -- efficient coarse-graining of molecular dynamics without forcescited by 36
- 2023Str2Str: A Score-based Framework for Zero-shot Protein Conformation Samplingcited by 29
- 2025No Trick, No Treat: Pursuits and Challenges Towards Simulation-free Training of Neural Samplerscited by 28
- 2025Progressive Inference-Time Annealing of Diffusion Models for Sampling from Boltzmann Densitiescited by 26
- 2023Navigating protein landscapes with a machine-learned transferable coarse-grained modelcited by 23
- 2024Structure Language Models for Protein Conformation Generationcited by 17
- 2024EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic Interpolantscited by 16
- 2025Temperature-Annealed Boltzmann Generatorscited by 15
- 2020Molecular Latent Space Simulatorscited by 14
- 2025Amortized Sampling with Transferable Normalizing Flowscited by 12
- 2024F3low: Frame-to-Frame Coarse-grained Molecular Dynamics with SE(3) Guided Flow Matchingcited by 9
- 2025Multiscale guidance of protein structure prediction with heterogeneous cryo-EM datacited by 8
- 2024P2DFlow: A Protein Ensemble Generative Model with SE(3) Flow Matchingcited by 6
- 2025Learning residue level protein dynamics with multiscale Gaussianscited by 4
- 2024Machine Learning of Slow Collective Variables and Enhanced Sampling via Spatial Techniquescited by 2
- 2025Learning conformational ensembles of proteins based on backbone geometrycited by 1
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