• Installation
• Available Models
• Inference
  • Input Format
  • Run Inference
  • Output Format
• Cite
• Acknowledgements
• License

🎉Here we present ODesign, an all-atom generative world model for all-to-all biomolecular interaction design. ODesign allows scientists to specify epitopes on arbitrary targets and generate diverse classes of binding partners with fine-grained control.

ODesign is also available at https://odesign.lglab.ac.cn, allowing users to generate binding partners without coding expertise.

Please feel free to contact us via email if you have any questions. You can also join our discussion group on WhatsApp or WeChat.

Installation

Step 1 — Clone the Repository

git clone https://github.com/The-Institute-for-AI-Molecular-Design/ODesign.git
cd ODesign

Step 2 — Prepare the Environment (cuda 12.1)

conda create -n odesign python=3.10
conda activate odesign
pip install -r requirements.txt -f https://data.pyg.org/whl/torch-2.3.1+cu121.html

Alternatively, you can build and run the docker image using the following commands:

docker build -t odesign -f Dockerfile .

docker run --gpus all -it --rm --shm-size=8g \
  -v /path/to/ckpt_root_dir:/app/ODesign/ckpt \
  -v /path/to/data_root_dir:/app/ODesign/data \
  -v $(pwd)/outputs:/app/ODesign/outputs \
  -v $(pwd)/inference_demo.sh:/app/ODesign/inference_demo.sh \
  odesign bash

Available Models

ODesign currently provides the following pre-trained model variants. Each model supports a specific modality and design mode:

Model Name	Design Modality	Design Mode	Hugging Face
odesign_base_prot_flex	protein	flexible-receptor	odesign_base_prot_flex.pt
odesign_base_prot_rigid	protein	rigid-receptor	odesign_base_prot_rigid.pt
odesign_base_ligand_rigid	ligand	rigid-receptor	odesign_base_ligand_rigid.pt
odesign_base_na_rigid	nucleic acid	rigid-receptor	odesign_base_na_rigid.pt

Checkpoints of OInvFold module for different design modalities are also stored at Hugging Face.

You can download all available checkpoints using the following command. Alternatively, you may manually download specific checkpoints from the Hugging Face links listed above.

cd ODesign
bash ./ckpt/get_odesign_ckpt.sh [ckpt_root_dir]

Inference

Input Format

Please refer to Section B.1 & B.2 in our Supplementary Information for details about the input JSON format. Example input JSON files for each task can be found in the examples directory.

Run Inference

Step 1 — Download Required Inference Data

Before running inference for the first time, please download the required data using the following command:

cd ODesign
bash ./data/get_odesign_data.sh [data_root_dir] [inference_only]

get_odesign_data.sh accepts the following arguments:

Argument	Description	Default
data_root_dir	Directory where downloaded data will be stored.	./data
inference_only	If set to true, only inference-related files will be downloaded. Set false if you also need training data.	true

Please note that the training data and code are still under preparation, which will be released soon. Stay tuned!

Once the script completes, you should see:

🎉 SUCCESS: All required data files have been downloaded!

Step 2 — Run the Inference Demo

After data preparation, launch the inference process using:

bash inference_demo.sh

This script generates molecular designs based on the selected model and input json file. You can configure inference behavior by editing the following arguments in inference_demo.sh. An example for multi-GPU inference using torchrun is also provided in inference_demo.sh.

Argument	Description	Example
infer_model_name	Model used for inference. Available options: odesign_base_prot_flex, odesign_base_prot_rigid, odesign_base_ligand_rigid, odesign_base_na_rigid.	odesign_base_prot_flex
design_modality	Must be specified as dna or rna if using nucleic acid design model. Available options: protein, ligand, dna, rna	rna
data_root_dir	Directory where downloaded data is stored.	./data
ckpt_root_dir	Directory where model checkpoints are stored.	./ckpt
input_json_path	Path to the input design specification JSON file.	./examples/.../odesign_input.json
exp_name	Custom label for inference output directory. If left empty, a default name is auto-generated.	protein_binding_protein_design
seeds	Random seeds used during generation. Supports multiple seeds.	[42] or [42, 123]
N_sample	Number of generated samples per seed.	5
use_msa	Utilize MSA information during inference (only set to true if the input JSON includes MSA).	false
num_workers	Number of dataloader workers.	4
CUDA_VISIBLE_DEVICES	GPU device for inference.	0

Output Format

When inference completes, results will be saved in the outputs/ directory which has the following structure:

outputs
└── <exp_name>
    └── <timestamp>
        ├── .hydra
        ├── errors
        ├── <sample_name_1>
        │   ├── seed_XXX
        │   │   ├── predictions
        │   │   │   ├── <sample_name_1>_seed_XXX_bb_0_seq_0.cif
        │   │   │   ├── <sample_name_1>_seed_XXX_bb_0_seq_1.cif        
        │   │   │   └── ...
        │   │   └── traceback.pkl
        │   ├── seed_YYY
        │   │   ├── predictions
        │   │   │   ├── <sample_name_1>_seed_YYY_bb_0_seq_0.cif
        │   │   │   └── ...
        │   │   └── traceback.pkl
        ├── <sample_name_2>
        │   ├── ...
        └── run.log

Folder / File	Description
<exp_name>/	Folder corresponding to the user-defined exp_name in inference_demo.sh.
<timestamp>/	Automatically generated run folder to separate multiple runs.
.hydra/	Stores Hydra-generated configuration files.
errors/	Stores error logs if failures occur during inference (empty if the run completes successfully).
<sample_name>/	Named after the sample name field in the input JSON. A JSON may define multiple sample cases.
seed_<value>/	Contains outputs generated using a specific random seed.
predictions/	Contains inverse-folded molecular design results.
*.cif	The designed molecules after inverse folding in CIF format.
traceback.pkl	Serialized traceback information (the constructed input atom_array).
run.log	Full inference execution log.

Please note that the number of designed sequences per backbone structure (default: 1) can be specified by the argument exp.invfold_topk.

Cite

If you use ODesign in your work, please cite the following:

@misc{zhang2025odesign,
      title={ODesign: A World Model for Biomolecular Interaction Design}, 
      author={Odin Zhang and Xujun Zhang and Haitao Lin and Cheng Tan and Qinghan Wang and Yuanle Mo and Qiantai Feng and Gang Du and Yuntao Yu and Zichang Jin and Ziyi You and Peicong Lin and Yijie Zhang and Yuyang Tao and Shicheng Chen and Jack Xiaoyu Chen and Chenqing Hua and Weibo Zhao and Runze Ma and Yunpeng Xia and Kejun Ying and Jun Li and Yundian Zeng and Lijun Lang and Peichen Pan and Hanqun Cao and Zihao Song and Bo Qiang and Jiaqi Wang and Pengfei Ji and Lei Bai and Jian Zhang and Chang-yu Hsieh and Pheng Ann Heng and Siqi Sun and Tingjun Hou and Shuangjia Zheng},
      year={2025},
      eprint={2510.22304},
      archivePrefix={arXiv},
      primaryClass={q-bio.BM},
      url={https://arxiv.org/abs/2510.22304}, 
}

Acknowledgements

This project draws in part from Protenix and OpenFold, supported by the Apache 2.0 License. Thanks for their great work and code.

License

Both source code and model parameters of ODesign are released under the Apache 2.0 License.