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OneScience-Group/protenix

sourceHugging Faceapache-2.0updated 1mo agoView on Hugging Face

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Model Card

Protenix

Model Introduction

Protenix is an AlphaFold 3-like model for predicting the structures of biomolecular complexes comprising proteins, nucleic acids, ligands, and other molecules. It predicts three-dimensional structures from molecular inputs described in JSON and locally generated MSA features, producing CIF structure files together with associated confidence scores.

Model Description

Protenix uses a Pairformer representation network and an atom-level diffusion Transformer to predict the structures of proteins, nucleic acids, ligands, and their complexes within a unified framework.

This Hugging Face package is designed for immediate use after download, rapid local validation, and automated execution in OneCode. All code, configuration files, example inputs, and pretrained weights are included in the model package directory.

Use Cases

Scenario	Description
Complex structure prediction	Takes a Protenix JSON file and locally generated MSA features as input and outputs predicted structures in CIF format and confidence scores in JSON format.
Full Hugging Face package validation	Uses the included `configs/`, `models/`, `scripts/`, `examples/`, and `weight/` directories to run preflight checks and inference directly.
Fine-tuning pipeline validation	Starts the fine-tuning workflow using the included weights and `ft_datasets/finetune_subset.txt`.
Training pipeline setup	Supports single-GPU training once the complete Protenix dataset is provided.

Usage Guide

1. OneCode Usage

Try one-click AI4S development in the OneCode online environment:

Try one-click AI4S development

2. Manual Installation and Usage

Hardware Requirements

—GPU or DCU is recommended.
—A CPU can be used for basic connectivity checks, but execution will be slow.
—DCU users must install DTK in advance. DTK 25.04.2 or later is recommended, or a OneScience-recommended version matching the current cluster.

Software Requirements

DCU users who need additional information about the adaptation may contact liubiao@sugon.com.

Environment Check

—NVIDIA GPU:

bash

nvidia-smi

—Hygon DCU:

bash

hy-smi

Quick Start

1. Install the Runtime Environment

bash

conda create -n onescience311 python=3.11 -y
conda activate onescience311
pip install onescience[bio] -i http://mirrors.onescience.ai:3141/pypi/simple/  --trusted-host mirrors.onescience.ai

2. Download the Model Package and Dataset

bash

hf download OneScience-Group/protenix --local-dir ./protenix
hf download OneScience-Group/protenix_dataset --repo-type dataset --local-dir ./protenix_dataset
cd protenix

Model Weights

The trained model weights are included in the weights/ directory and can be used immediately after the model package is downloaded.

3. Run Preflight Checks

To check only the model package, weights, and local imports, run:

bash

python scripts/preflight.py --strict-weights --strict-imports

If the full dataset is already prepared:

bash

export DATA_ROOT_DIR=../protenix_dataset
python scripts/preflight.py --strict-weights --strict-imports --strict-data

4. Run Inference

bash

export DATA_ROOT_DIR=../protenix_dataset
bash scripts/inference_unified_demo.sh

Default output directory:

text

output_unified/7r6r/seed_101/predictions/

5. Training and Fine-Tuning

Training:

bash

export DATA_ROOT_DIR=../protenix_dataset
bash scripts/train_demo.sh

Fine-tuning:

bash

export DATA_ROOT_DIR=../protenix_dataset
bash scripts/finetune_demo.sh

OneScience Official Information

Platform	OneScience Main Repository	Skills Repository
Gitee	https://gitee.com/onescience-ai/onescience	https://gitee.com/onescience-ai/oneskills
GitHub	https://github.com/onescience-ai/OneScience	https://github.com/onescience-ai/oneskills

Citation & License

The Protenix project, including its code and model parameters, is available under the Apache License 2.0 and may be used free of charge for both academic research and commercial purposes.