README.md

---
license: cc-by-nc-sa-4.0
tasks:
  - protein-complex-structure-prediction
frameworks:
  - jax
language:
  - en
  - zh
tags:
  - OneScience
  - Life Sciences
  - Protein Structure Prediction
  - Biomolecular Interactions
  - Complex Structure Prediction
  - AlphaFold3
datasets:
  - OneScience-Sugon/AlphaFold3_dataset
---

<p align="center">
  <strong>
    <span style="font-size: 30px;">AlphaFold3</span>
  </strong>
</p>

# Model Introduction

AlphaFold 3 is a biomolecular structure prediction model developed by Google DeepMind and Isomorphic Labs. It predicts the three-dimensional structures and interactions of proteins, DNA, RNA, small-molecule ligands, and their complexes.

Paper: Accurate structure prediction of biomolecular interactions with AlphaFold 3  
https://www.nature.com/articles/s41586-024-07487-w

# Model Description

AlphaFold 3 uses a Pairformer and a diffusion model to predict biomolecular complex structures. This model package provides a JAX / Flax inference implementation and database-search scripts, together with the accompanying Hugging Face dataset `OneScience-Sugon/AlphaFold3_dataset`.

# Use Cases

| Scenario | Description |
| :---: | :--- |
| Direct inference from existing features | Takes an AlphaFold 3 JSON file containing precomputed features such as MSAs and templates as input and produces structure predictions |
| Protein structure prediction | Takes a protein sequence as input, generates features by searching databases, and predicts its structure |
| Biomolecular complex modeling | Takes multicomponent systems comprising proteins, DNA, RNA, ligands, and other molecules as input and predicts their 3D structures |
| Database-search pipeline validation | Uses Jackhmmer / Nhmmer or MMseqs pipelines to verify database paths and the availability of search tools |
| Hugging Face / OneCode execution | After downloading the model project and complete dataset, quickly verifies that the scripts run correctly in a life-sciences runtime environment |



# Usage Instructions

## 1. OneCode

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

[Try one-click AI4S development](https://web-2069360198568017922-iaaj.ksai.scnet.cn:58043/home)

## 2. Manual Installation

**Hardware Requirements**

- GPU or DCU is recommended.
- A CPU can be used for import checks and lightweight configuration tests; full training and inference 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.





**Environment Check**

- NVIDIA GPU:

```bash
nvidia-smi
```

- Hygon DCU:

```bash
hy-smi
```

### Download the Model Package

```bash
hf download --model OneScience-Sugon/AlphaFold3 --local-dir ./AlphaFold3
cd AlphaFold3
```

### Install the Runtime Environment

**DCU Environment**

```bash
# Activate DTK and CONDA first
conda create -n onescience311 python=3.11 -y
conda activate onescience311
# uv installation supported
pip install onescience[bio-dcu] -i http://mirrors.onescience.ai:3141/pypi/simple/  --trusted-host mirrors.onescience.ai
```

After installation, return to the model package directory:

```bash
cd ./AlphaFold3
```

If the AlphaFold 3 C++ extension and runtime data files have not yet been built in the current environment, run:

```bash
python -m onescience.flax_model.alphafold3.build_extension
python -m onescience.flax_models.alphafold3.build_data
```

### Training and Inference Data Overview

The OneScience community has uploaded the complete data required for AlphaFold 3 inference and database searches to Hugging Face: [OneScience-Sugon/AlphaFold3_dataset](https://huggingface.co/datasets/OneScience-Sugon/AlphaFold3_dataset). This model package does not include a training entry point; the dataset is primarily used to construct MSA / template features and perform database searches before inference.

```bash
hf download --dataset OneScience-Sugon/AlphaFold3_dataset --local-dir ./data/alphafold3
```
### Model Weights

Model weights will be available soon.

### Preparing Weights

Place the AlphaFold 3 model weights in the following directory, or specify them via environment variables:

```text
weight/
  AlphaFold3/
    ...
```

Default lookup order:

- `ALPHAFOLD3_MODEL_DIR`
- `${ONESCIENCE_MODELS_DIR}/AlphaFold3`
- `weight/AlphaFold3`

Example:

```bash
export ALPHAFOLD3_MODEL_DIR=/path/to/AlphaFold3
```

### Direct Inference

When the input JSON already contains features such as MSA and templates, you can run inference directly:

```bash
bash scripts/infer.sh
```

Equivalent Python command example:

```bash
python scripts/run_alphafold.py \
  --json_path inputs/7r6r_data.json \
  --model_dir weight/AlphaFold3 \
  --output_dir outputs \
  --run_data_pipeline=false \
  --flash_attention_implementation=triton
```

Output is written to `outputs/` and includes the top-ranked structure, structures generated from different seed / sample combinations, a CSV file containing ranking scores, and a copy of the input JSON.

### Jackhmmer / Nhmmer Database Search

When the input JSON contains only sequences and requires a local database search, use:

```bash
bash scripts/infer_jackhmmer.sh
```

Common environment variables:

```bash
export ALPHAFOLD3_DATASET_ROOT=/path/to/alphafold3
export ALPHAFOLD3_MODEL_DIR=/path/to/AlphaFold3
export ALPHAFOLD3_JSON_PATH=inputs/t1119_search.json
export ALPHAFOLD3_OUTPUT_DIR=outputs
export ALPHAFOLD3_RUN_INFERENCE=false
```

`ALPHAFOLD3_DATASET_ROOT` is expected to contain directories such as `public_databases/`, `jackhmmer_split/`, and `mmseqsDB/`.

### MMseqs Database Search

If the runtime environment provides the MMseqs executable and the required MMseqs databases, use:

```bash
bash scripts/infer_mmseqs.sh
```

Common environment variables:

```bash
export ALPHAFOLD3_MMSEQS_HOME=/path/to/mmseqs
export ALPHAFOLD3_DATASET_ROOT=/path/to/alphafold3
export ALPHAFOLD3_MMSEQS_DB_DIR=/path/to/alphafold3/mmseqsDB
export ALPHAFOLD3_RUN_INFERENCE=false
```

To proceed with inference after the database search, set `ALPHAFOLD3_RUN_INFERENCE` to `true` and ensure that the weights directory is available.

# Data Format

AlphaFold 3 inputs are provided in JSON format with the following basic structure:

```json
{
  "dialect": "alphafold3",
  "version": 1,
  "name": "example",
  "sequences": [
    {
      "protein": {
        "id": "A",
        "sequence": "..."
      }
    }
  ],
  "modelSeeds": [100],
  "bondedAtomPairs": null,
  "userCCD": null
}
```

This repository provides two examples:

- `inputs/7r6r_data.json`: contains sequence, MSA, and template information; suitable for direct inference.
- `inputs/t1119_search.json`: contains only sequences; suitable for database-search pipeline validation.

It is recommended to download the full Hugging Face dataset `OneScience-Sugon/AlphaFold3_dataset` to `data/alphafold3/` under the model package. The relative directory structure expected by the database-search pipeline is as follows:

```text
data/
  alphafold3/
    public_databases/
      mmcif_files/
      pdb_seqres_2022_09_28.fasta
      ...
    jackhmmer_split/
      bfd-first_non_consensus_sequences.fasta@64
      mgy_clusters_2022_05.fa@512
      uniprot_cluster_annot_2021_04.fa@256
      uniref90_2022_05.fa@128
    mmseqsDB/
      small_bfd_db
      mgnify_db
      uniprot_cluster_annot_db
      uniref90_db
```

# Verification

Static import check:

```bash
python tests/check_import_boundaries.py
```

# 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

- This repository is adapted from the open-source AlphaFold 3 model to support DCUs.
- The AlphaFold 3 source code is licensed under CC BY-NC-SA 4.0; model parameters are subject to separate usage terms.
- For scientific use, please cite the original paper: [Accurate structure prediction of biomolecular interactions with AlphaFold 3](https://www.nature.com/articles/s41586-024-07487-w).