boltzina 1.0.1

Efficient and accurate virtual screening via docking-guided binding prediction with Boltz-2

Boltzina

Boltzina is a pipeline that combines AutoDock Vina (or Uni-Dock2) docking with Boltz-2 structure prediction and affinity scoring for virtual screening.

Quick Start

Installation

# From PyPI
pip install boltzina

# From source (development)
uv sync

Tool setup (Vina, MAXIT, Boltz-2 model weights)

boltzina setup --all

For Uni-Dock2 (GPU-accelerated docking, requires pixi and CUDA 12):

# Clone Uni-Dock2 and build using the provided pixi.toml
git clone https://github.com/dptech-corp/Uni-Dock2 /path/to/Uni-Dock2
cp pixi.toml /path/to/Uni-Dock2/
mkdir -p /path/to/Uni-Dock2/.pixi && cp pixi-config.toml /path/to/Uni-Dock2/.pixi/config.toml
cd /path/to/Uni-Dock2 && pixi install --all && pixi run build
boltzina setup --register-unidock2 /path/to/Uni-Dock2

---

Usage

With Boltz-2 structure prediction (sequence → dock → score)

Provide a protein sequence and a SMILES/SDF file. Boltzina will:

1. Run Boltz-2 structure + affinity prediction (complex with first/reference ligand)
2. Determine the docking grid automatically from the predicted binding pose
3. Run AutoDock Vina docking
4. Score all poses with Boltz-2

# From a FASTA file (CDK2 example)
boltzina run sample/CDK2/ligands.smi \
  --sequence-file sample/CDK2/cdk2.fasta \
  --output-dir ./results

# From a sequence string directly
boltzina run sample/CDK2/ligands.smi \
  --sequence "MENFQKVEKIGEGTYGVVYKARNKLTGEVVALKKIRLDTETEGVPSTAIREISLLKELNHPNIVKLLDVIHTENKLYLVFEFLHQDLKKFMDASALTGIPLPLIKSYLFQLLQGLAFCHSHRVLHRDLKPQNLLINTEGAIKLADFGLARAFGVPVRTYTHEVVTLWYRAPEILLGCKYYSTAVDIWSLGCIFAEMVTRRALFPGDSEIDQLFRIFRTLGTPDEVVWPGVTSMPDYKPSFPKWARQDFSKVVPPLDEDGRSLLSQMLHYDPNKRISAKAALAHPFFQDVTKPVPHLRL" \
  --output-dir ./results

# Multi-chain protein: colon-separated sequences
boltzina run sample/CDK2/ligands.smi \
  --sequence "MENFQKVEKIGEGTYGVVYK...:AKLSILPWGHC..." \
  --output-dir ./results

# Multi-chain protein: multi-entry FASTA
boltzina run sample/CDK2/ligands.smi \
  --sequence-file complex.fasta \   # >chain1 / seq / >chain2 / seq
  --output-dir ./results

# Use a specific reference ligand for prediction and grid center
boltzina run sample/CDK2/ligands.smi \
  --sequence-file sample/CDK2/cdk2.fasta \
  --reference-ligand "CC(C)[C@H](CO)Nc1nc(Nc2ccc(C(=O)O)c(Cl)c2)c2ncn(C(C)C)c2n1" \
  --output-dir ./results

# With more diffusion samples for better accuracy
boltzina run sample/CDK2/ligands.smi \
  --sequence-file sample/CDK2/cdk2.fasta \
  --use-msa-server \
  --diffusion-samples 5 \
  --output-dir ./results

With a Boltz-2 YAML input

For full control over multi-chain proteins, ligand definitions, and Boltz-2 settings, use a boltz-compatible YAML file (see sample/CDK2/1ckp_cdk2.yaml for an example):

boltzina run sample/CDK2/ligands.smi \
  --yaml sample/CDK2/1ckp_cdk2.yaml \
  --output-dir ./results

The YAML format:

version: 1
sequences:
  - protein:
      id: A
      sequence: MENFQKVEKIGEGTYGVVYK...  # CDK2 sequence
  - ligand:
      id: B
      smiles: 'CC(C)[C@H](CO)Nc1nc(Nc2ccc(C(=O)O)c(Cl)c2)c2ncn(C(C)C)c2n1'
properties:
  - affinity:
      binder: B

Multiple protein chains are supported (add more - protein: entries). The properties.affinity.binder identifies the reference ligand for grid center determination.

From precomputed Boltz-2 results

If you have already run boltz predict, pass the output directory directly:

boltzina run sample/CDK2/ligands.smi \
  --work-dir sample/CDK2/boltz_results_base \
  --output-dir ./results

The grid center is determined automatically from the Boltz-2 predicted ligand position. You can override it explicitly:

boltzina run sample/CDK2/ligands.smi \
  --work-dir sample/CDK2/boltz_results_base \
  --grid-center "7.0,-4.9,7.5" \
  --output-dir ./results

---

CLI Reference

boltzina run <INPUT> [OPTIONS]

INPUT can be a .smi/.txt file (SMILES list), .sdf file, or a directory.

Protein input (choose one; required):

Option	Description
--sequence / -s	Protein sequence (single chain, or SEQ1:SEQ2 for multi-chain)
--sequence-file	FASTA file (one >entry per chain for multi-chain)
--yaml	Boltz-2 compatible YAML (protein + ligand + affinity)
--work-dir	Existing Boltz-2 output directory (docking + scoring only)

Structure prediction options (with --sequence / --sequence-file):

Option	Default	Description
--reference-ligand	first in INPUT	SMILES string or SDF file for Boltz-2 complex prediction and grid center

Docking:

Option	Default	Description
--grid-center	auto	Docking box center x,y,z
--grid-size	20.0	Docking box size (Å)
--ligand-chain-id	B	Ligand chain in Boltz-2 prediction (rescore mode)
--docking-engine	vina	vina or unidock2
--num-workers	1	Parallel Vina workers
--skip-docking	off	Score existing poses only
--regenerate-conformer	off	Force 3D conformer regeneration for SDF

Boltz-2 prediction:

Option	Default	Description
--use-msa-server	off	Use online MMseqs2 MSA server
--recycling-steps	3	Boltz-2 recycling steps
--sampling-steps	200	Boltz-2 sampling steps
--diffusion-samples	1	Boltz-2 diffusion samples
--use-potentials	off	Boltz-2 inference-time potentials
--subsample-msa	off	Subsample MSA sequences
--no-kernels	off	Disable trifast kernels (older GPUs)
--affinity-mw-correction	off	MW correction to affinity

Output:

Option	Default	Description
--output-dir / -o	./boltzina_results	Output directory
--batch-size	1	Boltz-2 scoring batch size
--seed	—	Random seed
--vina-override	off	Rerun Vina even if results exist
--boltz-override	off	Rerun Boltz-2 scoring even if results exist
--keep-intermediate-files	off	Keep intermediate docking files

boltzina prepare <INPUT> [OPTIONS]

Convert SMILES/SDF to PDB + prepared_mols.pkl for use with run.py.

boltzina prepare ligands.smi --output-dir ./prepared
boltzina prepare ligands.sdf --output-dir ./prepared --regenerate-conformer

boltzina grid <STRUCTURE_FILE> [OPTIONS]

Compute the docking grid center from a ligand or complex file.

boltzina grid ligand.pdb --output vina_config.txt
boltzina grid complex.cif --chain B --output vina_config.txt

boltzina setup [OPTIONS]

Install and register external tools.

boltzina setup --all                          # Vina + MAXIT + Boltz-2 weights
boltzina setup --install-vina                 # Vina only
boltzina setup --install-maxit                # MAXIT only
boltzina setup --register-unidock2 /path/to/Uni-Dock2
boltzina setup --show                         # Show current config

---

Legacy usage (run.py)

The original run.py interface is fully supported:

python run.py sample/CDK2/config.json
python run.py sample/CDK2/config.json --use_kernels --num_workers 4

See sample/CDK2/config.json for the configuration file format.

---

Benchmark Dataset

The MF-PCBA benchmark dataset used in the paper is included in mf-pcba_test.zip. See the paper for details on the evaluation protocol.

---

Running Tests

# Unit tests (no GPU required)
uv run pytest tests/ --ignore=tests/test_integration.py -v

# Integration tests (requires GPU + Boltz-2 weights)
uv run pytest tests/test_integration.py -m gpu -v

---

Reference

Furui, K, & Ohue, M. Boltzina: Efficient and Accurate Virtual Screening via Docking-Guided Binding Prediction with Boltz-2. AI for Accelerated Materials Design - NeurIPS 2025. https://openreview.net/forum?id=OwtEQsd2hN