Getting Started

Quick start

Install and run ProDock

Create an isolated environment, then install ProDock with Conda, Pip, or Source.

◆ Conda · easiest Py Pip · PyPI Git Source · editable V Env first

1

🧪 Create env Fresh conda environment

→

2

📦 Install Conda, Pip, or Source

→

3

🚀 Run prodock(...)

Installation

◆

Conda

Recommended.

conda create -n prodock python=3.11
conda activate prodock
conda install -c tieulongphan prodock

Use: quickest install

Py

Pip

Install dependencies first.

conda create -n prodock python=3.11
conda activate prodock
conda install -c conda-forge openmm=8.3.1 pdbfixer
pip install prodock

Use: PyPI install

Git

Source

Editable install.

git clone https://github.com/Medicine-Artificial-Intelligence/ProDock
cd ProDock
conda env create -f prodock-env.yml

Use: development

Quick example

from prodock import prodock

PROJECT = "Demo"

RECEPTORS = [
    {
        "pdb_id": "4WKQ",
        "receptor_name": "EGFR_4WKQ",
        "ligand_code": "IRE",
        "chains": ["A"],
        "cofactors": [],
    },
]

LIGANDS = [
    {
        "id": "erlotinib",
        "smiles": "COCCOc1cc2c(ncnc2cc1OCCOC)Nc1cccc(c1)C#C",
    },
    {
        "id": "gefitinib",
        "smiles": "COc1cc2ncnc(c2cc1OCCCN1CCOCC1)Nc1ccc(c(c1)Cl)F",
    },
]

result = prodock(
    PROJECT,
    receptors=RECEPTORS,
    ligands=LIGANDS,
    engines=["qvina", "qvina-w"],
    extract_interaction=True,
    db_name="test.db",
)

print(result.campaign_json)
print(result.db_path)
print(result.merged_df.head())

📁 Output paths

result.campaign_json is the path to the generated campaign config, and result.db_path is the path to the SQLite database.

📊 Output tables

result.merged_df stores pose-level rows such as receptor, ligand, engine, rank, affinity, and RDKit molecule objects and pose id for downstream analysis.