gdock 0.1.0

Information-driven protein-protein docking using a genetic algorithm - Python bindings

gdock-py

Minimal Python bindings for gdock, an information-driven protein-protein docking engine using a genetic algorithm.

Installation

pip install gdock

Usage

import gdock

receptor_pdb = open("receptor.pdb").read()
ligand_pdb = open("ligand.pdb").read()

# Score a receptor-ligand complex
scores = gdock.score(receptor_pdb, ligand_pdb)
print(scores)  # {"vdw": ..., "elec": ..., "desolv": ..., "total": ...}

# Run the full GA docking pipeline
result = gdock.dock(
    receptor_pdb,
    ligand_pdb,
    restraints=[(10, 20), (15, 25)],  # (receptor_resseq, ligand_resseq) pairs
    max_generations=250,
    seed=42,
)
print(f"Generations run: {result['generationsRun']}")
print(f"Best fitness: {result['poses'][0]['fitness']}")
best_pdb = result["poses"][0]["pdb"]

API

score(receptor_pdb, ligand_pdb, w_vdw=0.4, w_elec=0.05, w_desolv=3.4) -> dict

Computes VDW, electrostatic and desolvation energy terms for a receptor-ligand pose. Returns {"vdw", "elec", "desolv", "total"}.

dock(receptor_pdb, ligand_pdb, restraints=None, max_generations=250, seed=42) -> dict

Runs the genetic algorithm docking pipeline and returns ranked poses:

{
  "generationsRun": int,
  "poses": [
    {"rank", "fitness", "vdw", "elec", "desolv", "air", "pdb"},
    ...
  ]
}

Development

uv venv --python 3.13 .venv
source .venv/bin/activate
uv pip install maturin pytest
maturin develop
pytest tests/

License

0BSD