| --- |
| tags: |
| - Biology |
| - Protein |
| - Protein Design |
| license: agpl-3.0 |
| library_name: ofoldx |
| pipeline_tag: other |
| --- |
| |
| # PXDesign |
|
|
| Pre-trained model for protein backbone generation and binder evaluation through Preview and Extended workflows. |
|
|
| ## Disclaimer |
|
|
| This is an UNOFFICIAL implementation of [PXDesign](https://github.com/bytedance/PXDesign) by ByteDance Research. |
|
|
| The OFFICIAL repository of PXDesign is at [bytedance/PXDesign](https://github.com/bytedance/PXDesign). |
|
|
| **The team releasing PXDesign did not write this model card for this model so this model card has been written by the OFoldX team.** |
|
|
| ## Model Details |
|
|
| PXDesign-d is an all-atom diffusion model for de novo and target-conditioned protein backbone generation. The complete |
| workflow assigns a ProteinMPNN sequence to each backbone, evaluates the complex and unbound binder with AlphaFold 2, |
| and filters and ranks the candidates. |
|
|
| Extended evaluation additionally uses Protenix base or the structure-conditioned Protenix mini model, following the |
| released [PXDesignBench](https://github.com/bytedance/PXDesignBench) composition. Please refer to the |
| [Training Details](#training-details) section for more information on the training process. |
|
|
| ### Variants |
|
|
| - **PXDesign backbone**: Independently callable PXDesign-d coordinate generation through `PxDesignBackbonePipeline`. |
| - **PXDesign Preview**: Designs 100 backbones by default, assigns sequences with ProteinMPNN, and evaluates candidates with AlphaFold 2. |
| - **PXDesign Extended**: Designs 500 backbones by default and adds Protenix evaluation and screening. |
|
|
| ### Model Specification |
|
|
| | Single Size | Pair Size | Token Denoiser Blocks | Atom Encoder Blocks | Atom Decoder Blocks | Num Parameters (M) | |
| | ----------- | --------- | --------------------- | ------------------- | ------------------- | ------------------ | |
| | 384 | 128 | 16 | 4 | 4 | 139.028 | |
|
|
| The parameter count is for PXDesign-d. |
|
|
| ### Links |
|
|
| - **Code**: [ofoldx.models.pxdesign](https://github.com/OTeam-AI4S/OFoldX/tree/main/ofoldx/models/pxdesign) |
| - **Weights**: [oteam/pxdesign](https://huggingface.co/oteam/pxdesign) |
| - **Developed by**: ByteDance Research |
| - **Model type**: Target-conditioned all-atom protein backbone diffusion model |
| - **Original Repository**: [bytedance/PXDesign](https://github.com/bytedance/PXDesign) |
|
|
| ## Usage |
|
|
| The model file depends on the [`ofoldx`](https://ofoldx.valtech.ai/) library. You can install it using pip: |
|
|
| ```bash |
| pip install ofoldx |
| ``` |
|
|
| ### Direct Use |
|
|
| #### Preview Design |
|
|
| You can use this model directly with a pipeline for 100-residue binder design with Preview evaluation: |
|
|
| ```python |
| import ofoldx |
| from ofoldx.io import read_structure |
| from ofoldx.models.pxdesign import from_binder |
| |
| structure, _ = read_structure("target.cif") |
| pipeline = ofoldx.pipeline("design", model="oteam/pxdesign") |
| result = pipeline(structure, placement=from_binder(100, [("target", [(0, 150)])]), seed=7) |
| ``` |
|
|
| The result contains the generated candidates and ranked selections. `from_binder` specifies the target instances and |
| zero-based component spans. |
|
|
| #### Extended Design |
|
|
| You can use this model directly with a pipeline for binder design with Extended evaluation and full-target MSA evidence: |
|
|
| ```python |
| import ofoldx |
| from ofoldx.io import read_structure |
| from ofoldx.models.pxdesign import from_binder |
| from ofoldx.msa import read_msa |
| |
| structure, _ = read_structure("target.cif") |
| msa = {"target": read_msa("target.a3m", entity_id="1")} |
| pipeline = ofoldx.pipeline("design", model="oteam/pxdesign") |
| result = pipeline( |
| structure, |
| placement=from_binder(100, [("target", [(0, 150)])]), |
| msa=msa, |
| mode="extended", |
| seed=7, |
| ) |
| best = result.ranked_candidates[0] |
| ``` |
|
|
| Here `target` is the target instance ID and `1` is its entity ID; use the IDs in your input structure. |
|
|
| ### Downstream Use |
|
|
| #### Backbone Generation |
|
|
| Use `PxDesignBackbonePipeline` to generate backbones without sequence assignment or evaluation. See the |
| [pipeline guide](https://github.com/OTeam-AI4S/OFoldX/blob/main/ofoldx/pipelines/README.md) and [`PxDesignPipeline`][ofoldx.models.pxdesign.PxDesignPipeline] for component loading and workflow options. |
|
|
| ## Training Details |
|
|
| PXDesign-d learns protein backbone generation with an EDM coordinate-denoising objective. |
|
|
| ### Training Data |
|
|
| PXDesign-d is trained on [PDB](https://www.rcsb.org/) structures up to 1 May 2021 and predicted structures from |
| [AlphaFold DB](https://alphafold.ebi.ac.uk/) and [MGnify](https://www.ebi.ac.uk/metagenomics/). |
|
|
| ### Training Procedure |
|
|
| PXDesign-d is trained in two stages, first emphasizing monomer structures and then target-conditioned complexes. |
|
|
| ## Citation |
|
|
| Please cite the PXDesign work and repository described by the [upstream project](https://github.com/bytedance/PXDesign). |
|
|
| > [!NOTE] |
| > The artifacts distributed in this repository are part of the OFoldX project. |
| > If OFoldX supports your research, please cite the OFoldX project as follows: |
|
|
| ```bibtex |
| @software{chen2026ofoldx, |
| author = {Chen, Zhiyuan}, |
| title = {OFoldX}, |
| year = {2026}, |
| url = {https://github.com/OTeam-AI4S/OFoldX}, |
| } |
| ``` |
|
|
| ## Contact |
|
|
| Please use GitHub issues of [OFoldX](https://github.com/OTeam-AI4S/OFoldX/issues) for any questions or comments on the model implementation or model card. |
|
|
| Please contact the authors of the [PXDesign project](https://github.com/bytedance/PXDesign) for questions or comments on the paper/model. |
|
|
| ## License |
|
|
| The model weights, implementation, configuration, and documentation distributed by OFoldX are licensed under the |
| [GNU Affero General Public License](https://github.com/OTeam-AI4S/OFoldX/blob/main/license.md). |
|
|
| For additional terms and clarifications, please refer to our |
| [License FAQ](https://github.com/OTeam-AI4S/OFoldX/blob/main/license-faq.md). |
|
|
| ```spdx |
| SPDX-License-Identifier: AGPL-3.0-or-later |
| ``` |
|
|
| [ofoldx.models.pxdesign.PxDesignPipeline]: https://ofoldx.valtech.ai/models/pxdesign/#ofoldx.models.pxdesign.PxDesignPipeline |
|
|
