⚠️ Note: AlphaFold3 setup not included. Install separately following official instructions
The setup script will automatically:
✅ Create conda environment with Python 3.10
✅ Install all required dependencies
✅ Set up Jupyter kernel for notebooks
✅ Download Boltz model weights
✅ Configure LigandMPNN and ProteinMPNN
✅ Optionally install PyRosetta
❌ Need to install AF3 separately
Run Code End-to-End
Run the complete pipeline from BoltzDesign to LigandMPNN/ProteinMPNN redesign and AlphaFold3 cross-validation.
Examle for small molecule:
python boltzdesign.py --target_name 7v11 --target_type small_molecule --target_mols OQO --gpu_id 0 --design_samples 2 --suffix 1
Example for DNA/RNA PDB design:
python boltzdesign.py --target_name 5zmc --target_type dna --pdb_target_ids C,D --gpu_id 0 --design_samples 5 --suffix 1
If you want to use your custom PDB file:
python boltzdesign.py --target_name 7v11 --pdb_path your_pdb_path --target_type small_molecule --target_mols OQO --gpu_id 0 --design_samples 2 --suffix own
⚠️ Warning: To run the AlphaFold3 cross-validation pipeline, you need to specify your AlphaFold3 directory, Docker name, database settings, and conda environment in the configuration. These can be set using the following arguments:
--alphafold_dir: Path to your AlphaFold3 installation (default: ~/alphafold3)
--af3_docker_name: Name of your AlphaFold3 Docker container
--af3_database_settings: Path to AlphaFold3 database
--af3_hmmer_path: Path to HMMER
If you want to disable af3 cross validation add flag --run_alphafold False
🔧 Additionally, you may need to optimize parameters for your binder/target:
If binder does not form a highly compact structure, increase num_intra_contacts e.g. (default) 2 -> 4
If target does not form interaction with binder, increase num_inter_contacts e.g. (default) 2 -> 4
If generated binders have all alpha helices and you want to design beta sheets, change e.g. helix_loss_max 0.0, helix_loss_min = -0.3 to helix_loss_max -0.3, helix_loss_min = -0.6
If interaction features are not obtained through recycling=0, increase recycling_steps to 1 or more
If you want to enable visualization of the trajectory, you need to set --save_trajectory True. However, be cautious that if you are just optimizing with distogram (--distogram_only True), it will take more time since it also runs the diffusion modules to get actual xyz coordinates.
⚙️ Design Configuration
Configure your molecular design parameters:
python
1config ={2# Optimization parameters3'mutation_rate':1,4'learning_rate_pre':0.2,## Pre_iteration stage5'learning_rate':0.1,## Soft, temp, hard stages6# Iteration stages7'pre_iteration':30,# Initial logits optimization8'soft_iteration':75,# Logits to Softmax optimization9'temp_iteration':45,# Softmax Temperature annealing10'hard_iteration':5,# Final hard encoding optimization 11'semi_greedy_steps':0,# MCMC based on iPTM score12# Algorithm settings13'design_algorithm':'3stages',14}
Enhanced Scoring: Currently uses Rosetta scores (from [BindCraft])
📄 License & Citation
License: MIT License - See LICENSE file for details
Citation: If you use BoltzDesign1 in your research, please cite:
@article{cho2025boltzdesign1,
title={Boltzdesign1: Inverting all-atom structure prediction model for generalized biomolecular binder design},
author={Cho, Yehlin and Pacesa, Martin and Zhang, Zhidian and Correia, Bruno E and Ovchinnikov, Sergey},
journal={bioRxiv},
pages={2025--04},
year={2025},
publisher={Cold Spring Harbor Laboratory}
}
Issues: Please report bugs and feature requests via GitHub Issues
⚠️ Important Disclaimer
EXPERIMENTAL SOFTWARE: This pipeline is under active development and has NOT been experimentally validated in laboratory settings. We release this code to enable community contributions and collaborative development. Use at your own discretion and validate results independently.
