SiteAF3 is a universal molecular interaction analysis platform based on AlphaFold3, supporting structure prediction and analysis for various receptor-ligand type combinations.

SiteAF3 is published on PNAS!

🚨 v1.1.0 Update Supporting --use_pocket_masked_af3_msa_for_embedding, enhancing binding pocket locating. See the performance in Table 1.

Receptor Types:

• protein: Protein

Ligand Types:

• protein: Protein
• nucleic: Nucleic acid (DNA/RNA)
• small_molecule: Small molecule ligand / ions

1. Hotspot structure generation (generate_hotspot.py)
2. Pocket structure generation (generate_pocket.py)
3. Inference scripts (run_SiteAF3.py)

Replace /PATH/TO/alphafold3/src/alphafold3/model/model.py with ./AF3_code/model.py before intalling AlphaFold3.

# Installing AlphaFold3 dependencies
# Please follow the official AlphaFold3 documentation for installation
conda activate your_AF3_env_name
conda env update --file environment.yml

You can use the scripts here, or you can use PyMOL to build your own files.

python generate_hotspot.py \
    --input_pdb /PATH/TO/pdb_file \
    --receptor_type protein \
    --receptor_chains A B (OPTIONAL) \
    --ligand_type nucleic \
    --ligand_chains C (OPTIONAL) \

python generate_pocket.py \
    --input_pdb /PATH/TO/pdb_file \
    --receptor_type protein \
    --receptor_chains (OPTIONAL) \
    --ligand_type small_molecule \
    --ligand_chains (OPTIONAL) \
    --hotspot_cutoff 8.0 \
    --pocket_cutoff 10.0

python generate_hotspot.py --input_pdb input.pdb --list_chains

Hotspot files are required if enabling --use_hotspot_msa_for_embedding

python run_SiteAF3.py \
    --config_file /PATH/TO/JSON_file \
    --output_dir /PATH/TO/output_dir \
    --use_pocket_msa_for_embedding (OPTIONAL) \
    --use_hotspot_msa_for_embedding (OPTIONAL) \
    --use_af3_msa_for_embedding (OPTIONAL) \
    --use_pocket_masked_af3_msa_for_embedding  (v1.1.0 OPTIONAL) \
    --verbose

In the JSON configuration file for run_SiteAF3.py, See more JSON cases in test_input folder.

{
    "name": "my_complex",
    "receptor": [{
        "rec_struct_path": "input.pdb",
        "fixed_chain_id": ["A", "B"],
        "hotspot_path": "/PATH/TO/input_hotspot.pdb",
        "pocket_path": "/PATH/TO/input_pocket.pdb"
    }],
    "ligand": [{
        "type": "rna",
        "chain_id": "C",
        "sequence": "..."
    }],
    "modelSeeds": [42, 123, 456]
}

The program automatically detects chain types based on:

1. Atom check: Search for characteristic atoms (CA atoms→protein, P atoms→nucleic acid)
2. Residue ratio: Count ratio of standard amino acids and nucleic acid residues
3. Small molecule identification: Exclude water molecules and ions, identify ligand molecules
4. Atom count: Small molecules typically have 1-200 heavy atoms
5. Element composition: Molecules containing non-standard elements

Automatically selects based on molecule type:

• Protein: CA atoms
• Nucleic acid: P atoms or C4' atoms
• Small molecule: Geometric center

output_dir/
├── seed_{1}
│   ├── confidences.json
│   ├── summary_confidences.cif
│   ├── info.json
│   ├── model.cif
│   ├── ranking_scores.csv
│   ├── sample_{0}
│   │   │── model.cif
│   │   ├── confidences.json
│   │   ├── summary_confidences.cif
│   ├── sample_{1}           
│   └── ......      
├── seed_{2} 
└── ......  

Prediction accuracy across biomolecular complexes

Use the --verbose parameter to get detailed debugging information.

8/31/2025

• Fix the bug of baseline model in V1.1.0
• Support CCD when using multiple ligands as input

7/12/2025

Supporting --use_pocket_masked_af3_msa_for_embedding, enhancing binding pocket locating.

7/6/2025

Features:

• 🎯 Support for multiple receptor-ligand combinations
• 🔧 Compatible to Alphafold3
• 🧬 Intelligent molecular recognition system
• 📁 Flexible configuration system
• 🛠️ Parallel processing support

Issues and Pull Requests are welcome!

This project is licensed under the MIT License - see the LICENSE file for details.

If you use SiteAF3 in your research, please cite:

@article{doi:10.1073/pnas.2521048122,
journal = {Proceedings of the National Academy of Sciences},
doi = {10.1073/pnas.2521048122},
issn = {0027-8424},
number = {44},
publisher = {Proceedings of the National Academy of Sciences},
title = {Accurate site-specific folding via conditional diffusion based on AlphaFold3},
volume = {122},
author = {Tang, Haocheng and Wang, Junmei},
note = {[Online; accessed 2025-11-06]},
date = {2025-10-30},
year = {2025},
month = {10},
day = {30},
}

@software{siteaf3,
  title={SiteAF3: Universal Molecular Interaction Analysis Platform},
  author={Haocheng Tang},
  year={2025},
  url={https://github.com/HaCTang/SiteAF3}
}