metadata
pipeline_tag: other
DyneTrion: Spatiotemporally Coherent Generative Emulation of Protein Dynamics Across Timescales
This repository contains the pretrained weights for DyneTrion, a generative protein dynamics emulator that jointly enforces geometric symmetry, structural consistency, and temporal coherence within a single framework.
- Paper: DyneTrion: A Spatio-temporally Coherent Generative Emulator for Protein Dynamics Across Timescales
- GitHub Repository: fudan-generative-vision/DyneTrion
| 1ail_A | 1ifg_A | 2kxl_A | 2rcs_H |
|
|
|
|
Installation
# Create virtual environment (Python 3.10.12 is recommended)
python -m venv .venv
source .venv/bin/activate
# Install PyTorch (CUDA 12.4)
pip install torch==2.4.0 --index-url https://download.pytorch.org/whl/cu124
# Install other dependencies
pip install -r requirements.txt
Inference
Run inference using:
bash inference.sh
- Model checkpoint:
step_400000.pth - Input CSV:
datasets/inference/inference_data.csv - Frame number:
n_frame = 16 - Motion number:
n_motion = 2 - Frame sampling step:
sample_step = 40 - Extrapolation time:
extrapolation_time = 16 - Noise scale:
noise_scale = 1.0
Inference results will be saved to save_root (default: ./test/inference/).
For more details, please check the GitHub Repository.
Citation
If you find DyneTrion useful for your research, please cite the paper:
@article{cheng2025dynetrion,
title={DyneTrion: A Spatio-temporally Coherent Generative Emulator for Protein Dynamics Across Timescales},
author={Cheng, Kaihui and Cai, Zhiqiang and Tu, Peng and Yao, Yisong and Han, Limei and Wu, Libo and Zhu, Siyu and Yang, Tzuhsiung and Qi, Yuan},
journal={arXiv preprint arXiv:2607.15309},
year={2026}
}