# ODesign **Repository Path**: jacky010/ODesign ## Basic Information - **Project Name**: ODesign - **Description**: No description available - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2026-01-22 - **Last Updated**: 2026-01-22 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README
ODesign
[![Web Server](https://img.shields.io/badge/Web_Server-Access-9A8CF0?logo=internet-explorer)](https://odesign.lglab.ac.cn/) [![Technical Report](https://img.shields.io/badge/Technical_Report-Download-9A8CF0?logo=adobe-acrobat-reader)](https://odesign1.github.io/static/pdfs/technical_report.pdf) [![Project Page](https://img.shields.io/badge/Project_Page-Access-9A8CF0?logo=adobe-acrobat-reader)](https://odesign1.github.io/) [![WhatsApp](https://img.shields.io/badge/WhatsApp-Join-25D366?logo=whatsapp)](https://chat.whatsapp.com/BfO6E7EGYpwDdjAAreKDVq?mode=wwt) [![WeChat](https://img.shields.io/badge/WeChat-Join-07C160?logo=wechat)](https://github.com/The-Institute-for-AI-Molecular-Design/ODesign/blob/main/imgs/odesign_wechat_qr_code.jpg)
- [Installation](#installation) - [Available Models](#available-models) - [Inference](#inference) - [Input Format](#input-format) - [Run Inference](#run-inference) - [Output Format](#output-format) - [Usage](#usage) - [Protein Generation](#protein-generation) - [Ligand Generation](#ligand-generation) - [Nucleic Acid Generation](#nucleic-acid-generation) - [Partial Diffusion](#partial-diffusion) - [Training](#training) - [Cite](#cite) - [Acknowledgements](#acknowledgements) - [License](#license) πŸŽ‰Here we present [ODesign](https://odesign1.github.io/static/pdfs/technical_report.pdf), 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](mailto:odesign@lglab.ac.cn) if you have any questions. You can also join our discussion group on [WhatsApp](https://chat.whatsapp.com/BfO6E7EGYpwDdjAAreKDVq?mode=wwt) or [WeChat](https://github.com/The-Institute-for-AI-Molecular-Design/ODesign/blob/main/imgs/odesign_wechat_qr_code.jpg). This work is supported by Lingang Laboratory, Zhejiang University, The Chinese University of Hong Kong, and Shanghai Artificial Intelligence Laboratory. For the full list of funding sources, please refer to our technical report [ODesign: A World Model for Biomolecular Interaction Design](https://odesign1.github.io/static/pdfs/technical_report.pdf).
ODesign Video
# Installation **Step 1 β€” Clone the Repository** ```bash git clone https://github.com/The-Institute-for-AI-Molecular-Design/ODesign.git cd ODesign ``` **Step 2 β€” Prepare the Environment (cuda 12.1)** - **pip** ```bash 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 ``` - **Docker** ```bash 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 \ -v $(pwd)/train_demo.sh:/app/ODesign/train_demo.sh \ odesign bash ``` - **Apptainer** ```bash apptainer build odesign.sif odesign.def apptainer run --nv \ --writable-tmpfs \ -B ckpt:/app/ODesign/ckpt \ -B data:/app/ODesign/data \ -B outputs:/app/ODesign/outputs \ -B $(pwd)/inference_demo.sh:/app/ODesign/inference_demo.sh \ -B $(pwd)/train_demo.sh:/app/ODesign/train_demo.sh \ odesign.sif 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](https://huggingface.co/The-Institute-for-AI-Molecular-Design/ODesign/resolve/main/ckpt/odesign_base_prot_flex.pt?download=true) | | `odesign_base_prot_rigid` | protein | rigid-receptor | [odesign_base_prot_rigid.pt](https://huggingface.co/The-Institute-for-AI-Molecular-Design/ODesign/resolve/main/ckpt/odesign_base_prot_rigid.pt?download=true) | | `odesign_base_ligand_rigid` | ligand | rigid-receptor | [odesign_base_ligand_rigid.pt](https://huggingface.co/The-Institute-for-AI-Molecular-Design/ODesign/resolve/main/ckpt/odesign_base_ligand_rigid.pt?download=true) | | `odesign_base_na_rigid` | nucleic acid | rigid-receptor | [odesign_base_na_rigid.pt](https://huggingface.co/The-Institute-for-AI-Molecular-Design/ODesign/resolve/main/ckpt/odesign_base_na_rigid.pt?download=true) |
ODesign Design Mode
Checkpoints of OInvFold module for different design modalities are also stored at [Hugging Face](https://huggingface.co/The-Institute-for-AI-Molecular-Design/OInvFold/tree/main). You can download all available checkpoints using the following command. Alternatively, you may manually download specific checkpoints from the Hugging Face links listed above. ```bash 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](https://odesign1.github.io/static/pdfs/technical_report.pdf) for details about the input JSON format. Example input JSON files for each task can be found in the [examples](https://github.com/The-Institute-for-AI-Molecular-Design/ODesign/tree/main/examples) directory. ## Run Inference **Step 1 β€” Download Required Inference Data** Before running inference for the first time, please download the `components.v20240608.cif` and `components.v20240608.cif.rdkit_mol.pkl` from [Google Drive](https://drive.google.com/drive/folders/1wPmwIrC3G52q1JFY0RXY95tjKDl7YEln?usp=drive_link), and place these files under your specified `data_root_dir`. **Step 2 β€” Run the Inference Demo** After data preparation, launch the inference process using: ```bash 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 └── └── β”œβ”€β”€ .hydra β”œβ”€β”€ errors β”œβ”€β”€ β”‚ β”œβ”€β”€ seed_XXX β”‚ β”‚ β”œβ”€β”€ predictions β”‚ β”‚ β”‚ β”œβ”€β”€ _seed_XXX_bb_0_seq_0.cif β”‚ β”‚ β”‚ β”œβ”€β”€ _seed_XXX_bb_0_seq_1.cif β”‚ β”‚ β”‚ └── ... β”‚ β”‚ └── traceback.pkl β”‚ β”œβ”€β”€ seed_YYY β”‚ β”‚ β”œβ”€β”€ predictions β”‚ β”‚ β”‚ β”œβ”€β”€ _seed_YYY_bb_0_seq_0.cif β”‚ β”‚ β”‚ └── ... β”‚ β”‚ └── traceback.pkl β”œβ”€β”€ β”‚ β”œβ”€β”€ ... └── run.log ``` | Folder / File | Description | | --------------- | ----------------------------------------------------------------------------------------------------------------------------- | | `/` | Folder corresponding to the user-defined `exp_name` in `inference_demo.sh`. | | `/` | 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). | | `/` | Named after the sample `name` field in the input JSON. A JSON may define multiple sample cases. | | `seed_/` | 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`. ## Usage ### Protein Generation #### Protein-binding Protein Protein-binding Protein ODesign can generate proteins that bind to specific protein targets. You need to provide a reference structure containing the target protein and specify the hotspot residues that define the binding interface. The model will generate a new protein chain that interacts with the target at the specified hotspot. To run this example, use: ```bash bash inference_demo.sh ``` with `infer_model_name=odesign_base_prot_flex` and `input_json_path=./examples/protein_design/prot_binding_prot/odesign_input.json`. #### Ligand-binding Protein Ligand-binding Protein ODesign can generate proteins that bind to specific small molecule ligands. You need to provide a reference structure containing the ligand and specify the hotspot atoms on the ligand. The model will generate a new protein chain that forms interactions with the ligand at the specified hotspot. To run this example, use: ```bash bash inference_demo.sh ``` with `infer_model_name=odesign_base_prot_flex` and `input_json_path=./examples/protein_design/lig_binding_prot/odesign_input.json`. #### Atom Scaffold Atom Scaffold ODesign can scaffold proteins around specific atoms or functional groups. This is useful for designing proteins that interact with specific chemical moieties. You need to specify the condition atoms that define the scaffold constraints, and the model will generate a protein structure that positions these atoms correctly. To run this example, use: ```bash bash inference_demo.sh ``` with `infer_model_name=odesign_base_prot_flex` and `input_json_path=./examples/protein_design/atom_scaffold/odesign_input.json`. #### Motif Scaffold

Motif Scaffold

ODesign can scaffold functional protein motifs by generating surrounding protein structure. This is useful for stabilizing functional motifs or creating new protein folds around known functional elements. You need to specify the motif regions from the reference structure and the desired scaffold length. To run this example, use: ```bash bash inference_demo.sh ``` with `infer_model_name=odesign_base_prot_flex` and `input_json_path=./examples/protein_design/motif_scaffold/odesign_input.json`. ### Ligand Generation #### Protein-binding Ligand Protein-binding Ligand ODesign can generate small molecule ligands that bind to specific protein targets. You need to provide a reference structure containing the target protein and specify the hotspot residues that define the binding pocket. The model will generate a new ligand molecule that interacts with the target at the specified hotspot. To run this example, use: ```bash bash inference_demo.sh ``` with `infer_model_name=odesign_base_ligand_rigid` and `input_json_path=./examples/ligand_design/prot_binding_lig/odesign_input.json`. ### Nucleic Acid Generation #### Backbone Generation

RNA Backbone

ODesign can generate nucleic acid backbone structures of specified length. This is useful for designing NA molecules from scratch without requiring a reference structure. You only need to specify the desired RNA chain length. Note: This example demonstrates RNA generation. To generate DNA instead, modify the `chain_type` field in the JSON input file to `"dnaChain"` and set `design_modality=dna`. To run this example, use: ```bash bash inference_demo.sh ``` with `infer_model_name=odesign_base_na_rigid`, `design_modality=rna`, and `input_json_path=./examples/na_design/rna_bb/odesign_input.json`. #### Protein-binding Nucleic Acid

Protein-binding RNA

ODesign can generate nucleic acid molecules that bind to specific protein targets. You need to provide a reference structure containing the target protein and specify the hotspot residues that define the binding interface. The model will generate a new NA chain that interacts with the target at the specified hotspot. Note: This example demonstrates RNA generation. To generate DNA instead, modify the `chain_type` field in the JSON input file (e.g., change `"rnaChain"` to `"dnaChain"`) and set `design_modality=dna`. To run this example, use: ```bash bash inference_demo.sh ``` with `infer_model_name=odesign_base_na_rigid`, `design_modality=rna`, and `input_json_path=./examples/na_design/prot_binding_rna/odesign_input.json`. ### Partial Diffusion ODesign can partially modify existing binding molecules to potentially enhance stability, modulate specificity, or improve expressibility. You need to provide a reference structure containing the target molecule, and specify the `partial_diff` field in the input JSON file to indicate the regions that require modifications. Please refer to **Section B.3 Partial Diffusion** in our [Supplementary Information](https://odesign1.github.io/static/pdfs/technical_report.pdf) for details. To run this example, use: ```bash bash inference_demo.sh ``` with `infer_model_name=odesign_base_prot_rigid`, `input_json_path=./examples/protein_design/prot_binding_prot_partial_diff/odesign_input.json` and `enable_partial_diff=true`. # Training **Step 1 β€” Download Required Training Data** Before training ODesign, please download the `odesign_full_data.tar.gz` from [Google Drive](https://drive.google.com/drive/folders/1wPmwIrC3G52q1JFY0RXY95tjKDl7YEln?usp=drive_link), and unzip the file using the following command. About 850 GB of disk space is required to keep the unzipped files. ```bash tar -xzvf [data_root_dir]/odesign_train_data.tar.gz -C [data_root_dir] ``` **Step 2 β€” Run the Training Demo** After data preparation, launch the training process using: ```bash bash train_demo.sh ``` Please note that the `ckpt_root_dir` in `train_demo.sh` should contain the pre-trained folding model checkpoint for ODesign initialization if you are not training from scratch. Our default training setting employs [protenix_base_default_v0.5.0](https://af3-dev.tos-cn-beijing.volces.com/release_model/protenix_base_default_v0.5.0.pt). # 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 code draws in part upon [Protenix](https://github.com/bytedance/Protenix) and [OpenFold](https://github.com/aqlaboratory/openfold), and is supported under the Apache 2.0 License. Thanks for their great work and code. This project is supported by Lingang Laboratory, Zhejiang University, and The Chinese University of Hong Kong. We are actively seeking highly motivated and talented PhD students to join our team. **We offer PhD training opportunities in Pharmacy / AI at Zhejiang University, or Computer Science at The Chinese University of Hong Kong.** If you are interested, please contact **odinz@link.cuhk.edu.hk** # License Both source code and model parameters of ODesign are released under the [Apache 2.0 License](https://github.com/The-Institute-for-AI-Molecular-Design/ODesign/blob/main/LICENSE).