mlsanityData to 7 Oct 2026

EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic Interpolants

arXiv 2410.09667 ↗First posted 12 Oct 2024cs.LG physics.chem-ph q-bio.BMcited by 15

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Abstract (arXiv)

Mapping the conformational dynamics of proteins is crucial for elucidating their functional mechanisms. While Molecular Dynamics (MD) simulation enables detailed time evolution of protein motion, its computational toll hinders its use in practice. To address this challenge, multiple deep learning models for reproducing and accelerating MD have been proposed drawing on transport-based generative methods. However, existing work focuses on generation through transport of samples from prior distributions, that can often be distant from the data manifold. The recently proposed framework of stochastic interpolants, instead, enables transport between arbitrary distribution endpoints. Building upon this work, we introduce EquiJump, a transferable SO(3)-equivariant model that bridges all-atom protein dynamics simulation time steps directly. Our approach unifies diverse sampling methods and is benchmarked against existing models on trajectory data of fast folding proteins. EquiJump achieves state-of-the-art results on dynamics simulation with a transferable model on all of the fast folding proteins.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 30 of its 42 references that are in the map. First posted on arXiv 12 Oct 2024.

Builds on

30 of 42 references.

References · 30

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18 more
  1. 2023SE(3) diffusion model with application to protein backbone generationcited by 214
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 9; 2026: 5.

15 papers.

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  1. 2025Transferable Generative Models Bridge Femtosecond to Nanosecond Time-Step Molecular Dynamicscited by 8
  2. 2025ConfRover: Simultaneous Modeling of Protein Conformation and Dynamics via Autoregressioncited by 7
  3. 2025Accelerating Protein Molecular Dynamics Simulation with DeepJumpcited by 6
  4. 2025MarS-FM: Generative Modeling of Molecular Dynamics via Markov State Modelscited by 6
  5. 2024JAMUN: Bridging Smoothed Molecular Dynamics and Score-Based Learning for Conformational Ensemblescited by 4
  6. 2025Finite-Time Analysis of Discrete-Time Stochastic Interpolantscited by 4
  7. 2026Scalable Spatio-Temporal SE(3) Diffusion for Long-Horizon Protein Dynamicscited by 4
  8. 2025TEMPO: Temporal Multi-scale Autoregressive Generation of Protein Conformational Ensemblescited by 3
  9. 2026Atomic Trajectory Modeling with State Space Models for Biomolecular Dynamicscited by 2
  10. 2025GeoAda: Efficiently Finetune Geometric Diffusion Models with Equivariant Adapterscited by 1
  11. 2025AI-based Methods for Simulating, Sampling, and Predicting Protein Ensemblescited by 1
  12. 2025Explicit and Effectively Symmetric Schemes for Neural SDEs on Lie Groupscited by 1
3 more
  1. 2026Learning Structure, Energy, and Dynamics: A Survey of Artificial Intelligence for Protein Dynamicscited by 1
  2. 2026Riemannian Flow Models with Reinforcement Learning for Molecular Crystal Structure Predictioncited by 0
  3. 2026SupraTITO: Transferable Generative Molecular Dynamics for Supramolecular Systemscited by 0