Protein Conformational Ensemble Generation

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CoMoFlow: Controllable Protein Conformational Ensemble Generation via Explicit Collective-Mode Modeling
Nov 6, 2026Yusong Wang, Zheyuan Gu, Ailiang Lin +5Protein Conformational Ensemble Generation Protein Structure Generation
Beijing University of Chinese Medicine · Peking University · New York University +2
Ablating Design Choices for Protein Ensemble Generators Across Structural Regimes
Nov 3, 2026Eoin Quinn, Oliver BentProtein Conformational Ensemble Generation Protein Structure Generation
InstaDeep London, UK · InstaDeep Paris, France
Predicting protein structures at any folding stage from sequence
Nov 3, 2026Lisheng Zhang, Qingsong Li, Zinan Xie +7Protein Conformational Ensemble Generation Protein Structure Prediction
Peking University International Cancer Institute, Peking University–Yunnan Baiyao International Medical Research Center, School of Basic Medical Sciences, Peking University Health Science Center, Peking University, 38 Xueyuan Road, Haidian District, Beijing 100191, China · Yuanpei College, Peking University, 5 Yiheyuan Road, Haidian District, Beijing 100871, China · The Hong Kong University of Science and Technology (Guangzhou), Nansha, Guangzhou, Guangdong, China +9
Modeling Atomic Conformational Ensembles of Proteins via Test-Time Supervision of Boltz-2 on Cryo-EM Density Maps
May 11, 2026Jay Shenoy, Miro Astore, Axel Levy +3Supervised Fine-Tuning Protein Structure Prediction
Department of Computer Science Stanford University, Flatiron Institute, & SLAC National Lab Stanford, CA 94305, USA · Center for Computational Biology & Center for Computational Mathematics Flatiron Institute New York, NY 10010, USA · Department of Electrical Engineering Stanford University & SLAC National Lab Stanford, CA 94305, USA +2
Learning Structure, Energy, and Dynamics: A Survey of Artificial Intelligence for Protein Dynamics
Apr 28, 2026Haocheng Tang, Liang Shi, Ya-Shi Zhang +3Protein Conformational Ensemble Generation Scientific ML
Mila – Quebec AI Institute · University of Pittsburgh · Work conducted during an internship at Mila +4
Polyformer: a generative framework for thermodynamic modeling of polymeric molecules
Apr 15, 2026Alessio Valentini, David Pekker, Chungwen Liang +2Protein Conformational Ensemble Generation Protein Structure Generation
PsiDagger, San Diego, CA · Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA · Department of Chemistry and The PULSE Institute, Stanford University, Stanford, CA +1
From Snapshots to Symphonies: The Evolution of Protein Prediction from Static Structures to Generative Dynamics and Multimodal Interactions
Mar 19, 2026Jingzhi Chen, Lijian XuProtein Conformational Ensemble Generation Protein Structure Prediction
Shenzhen University of Advanced Technology
Inference-time optimization for experiment-grounded protein ensemble generation
Feb 27, 2026Advaith Maddipatla, Anar Rzayev, Marco Pegoraro +5Diffusion Model Guidance Inference-Time Optimization
Institute of Science and Technology, Austria · University of Zurich, Switzerland · Tel Hai – University of Kiryat Shmona in the Galilee, Israel +4

Nov 6, 2026J/K move · Enter open · S save

CoMoFlow: Controllable Protein Conformational Ensemble Generation via Explicit Collective-Mode Modeling

Yusong Wang, Zheyuan Gu, Ailiang Lin, Minghao Shao+4

Beijing University of Chinese Medicine · Peking University · New York University+2

Protein Conformational Ensemble Generation Protein Structure Generation Flow-Based Generative Modeling

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Protein conformational ensemble generation aims to efficiently characterize the structural variations accessible to a protein beyond a single static structure. Recent flow-based methods efficiently generate conformational ensembles, but they model coordinated structural variation implicitly, which can limit ensemble fidelity and hinder direct control. To address this limitation, we propose CoMoFlow, a controllable framework for generating protein backbone conformational ensembles by explicitly modeling collective variation. CoMoFlow constructs protein-specific collective modes from the equilibrium structure, learns the distribution of their coefficients across the molecular dynamics (MD) ensemble, and uses sampled collective variations to guide SE(3) flow matching through feature conditioning and prior recentering. Experiments on ATLAS and de novo proteins show that CoMoFlow improves reconstruction of MD conformational distributions and coordinated structural variation relative to strong flow-based baselines, while enabling direct control over targeted conformational changes.