🐼 PandaDock

A Python-based GPU/CPU-accelerated molecular docking platform for computational drug discovery and bioinformatics.

🚀 Overview

PandaDock is a modern, modular molecular docking toolkit that combines cutting-edge algorithms with high-performance computing capabilities. Built for drug discovery, computational chemistry, and bioinformatics workflows.

✨ Key Features

🎯 Multiple Docking Algorithms: Genetic Algorithm, Monte Carlo, PANDADOCK, Random Search, Metal-based docking
⚡ Hardware Acceleration: Native GPU (PyTorch/CUDA) and multi-core CPU parallelization
📊 Advanced Scoring: Physics-based scoring with MM-GBSA inspired energy decomposition
🧬 Flexible Residues: Automatic and manual flexible residue detection
📈 Virtual Screening: High-throughput screening with batch processing
🔬 Comprehensive Analysis: Binding affinity calculations, pose clustering, interaction analysis
🎨 Rich Visualization: Interactive HTML reports with energy breakdowns and plots
🛠️ Modular Architecture: Clean, extensible Python API for custom workflows

📦 Installation

Quick Install

# Install PandaDock
pip install pandadock

# For GPU acceleration (optional)
pip install pandadock[gpu]

# For RDKit integration (recommended)
pip install pandadock[rdkit]

# Install all optional dependencies
pip install pandadock[gpu,rdkit,dev]

Development Install

# Clone the repository
git clone https://github.com/pritampanda15/PandaDock.git
cd PandaDock

# Install in development mode
pip install -e .

System Requirements

Python: 3.8+
OS: Linux, macOS, Windows
Memory: 4GB+ RAM (8GB+ recommended)
GPU: CUDA-compatible GPU (optional, for acceleration)

🔥 Quick Start

Basic Docking

# Simple protein-ligand docking
pandadock -p protein.pdb -l ligand.sdf -o results

# With binding site specification
pandadock -p protein.pdb -l ligand.sdf -o results -s -15.7 -17.7 8.1 -r 10.0

# Enhanced docking with GPU acceleration
pandadock -p protein.pdb -l ligand.sdf -o results --enhanced --use-gpu

Physics-Based Docking

# High-accuracy physics-based scoring
pandadock -p protein.pdb -l ligand.sdf -o results --physics-based

# PANDADOCK algorithm with simulated annealing
pandadock -p protein.pdb -l ligand.sdf -o results -a pandadock --physics-based

# With flexible residues
pandadock -p protein.pdb -l ligand.sdf -o results --physics-based --auto-flex

Virtual Screening

# High-throughput screening
pandadock -p protein.pdb -l ligand_library/ -o screening_results --enhanced

# Batch processing with parallel execution
pandadock -p protein.pdb -l compounds.sdf -o batch_results --cpu-workers 8

🐍 Python API

Basic Usage

from pandadock.core import DockingEngine
from pandadock.molecules import LigandHandler, ProteinHandler

# Load molecules
protein_handler = ProteinHandler()
ligand_handler = LigandHandler()

protein = protein_handler.load_protein("protein.pdb")
ligand = ligand_handler.load_ligand("ligand.sdf")

# Configure docking
config = {
    'algorithm': 'genetic',
    'scoring': 'enhanced',
    'site_center': [10.0, 20.0, 30.0],
    'site_radius': 12.0,
    'iterations': 100
}

# Run docking
engine = DockingEngine(config)
results = engine.dock(protein, ligand)

# Analyze results
for pose in results['poses']:
    print(f"Pose {pose['rank']}: Score = {pose['score']:.2f}")

Advanced Workflow

from pandadock.scoring import ScoringFunctionFactory
from pandadock.algorithms import AlgorithmFactory
from pandadock.analysis import BindingAffinityCalculator

# Create custom scoring function
scoring_factory = ScoringFunctionFactory()
scorer = scoring_factory.create_scoring_function(
    type='physics-based',
    use_gpu=True
)

# Use specialized algorithm
algorithm_factory = AlgorithmFactory()
algorithm = algorithm_factory.create_algorithm(
    type='pandadock',
    population_size=200,
    temperature=500
)

# Calculate binding affinities
affinity_calc = BindingAffinityCalculator()
affinities = affinity_calc.calculate_batch_affinities(results['poses'])

for affinity in affinities:
    print(f"ΔG: {affinity.delta_g:.2f} kcal/mol, Kd: {affinity.kd:.2e} M")

📊 Output & Analysis

Each docking run generates comprehensive results:

results/
├── poses/                          # Individual pose PDB files
│   ├── pose_1_score_-14.25.pdb
│   └── pose_2_score_-14.07.pdb
├── complexes/                      # Protein-ligand complexes
│   └── complex_pose_1_score_-14.25.pdb
├── reports/                        # Analysis reports
│   ├── docking_report.html         # Interactive HTML report
│   ├── binding_affinity_report.csv
│   └── detailed_analysis.txt
├── plots/                          # Visualization plots
│   ├── score_distribution.png
│   ├── energy_breakdown.png
│   └── binding_modes.png
└── docking_results.json           # Machine-readable results

Sample Analysis Report

🔬 DOCKING ANALYSIS REPORT
═══════════════════════════════════════

ALGORITHM: Genetic Algorithm (Enhanced)
SCORING: Physics-based with MM-GBSA
POSES GENERATED: 20
BEST SCORE: -14.25 kcal/mol

📊 BINDING AFFINITY ANALYSIS
─────────────────────────────
Pose 1: ΔG = -14.25 kcal/mol, Kd = 3.60e-11 M, IC50 = 7.20e-11 M
Pose 2: ΔG = -14.07 kcal/mol, Kd = 4.87e-11 M, IC50 = 9.75e-11 M
Pose 3: ΔG = -13.94 kcal/mol, Kd = 6.02e-11 M, IC50 = 1.20e-10 M

🧬 INTERACTION ANALYSIS
─────────────────────────
Hydrogen Bonds: 3
Hydrophobic Contacts: 12
π-π Stacking: 1
Salt Bridges: 0

🧠 Algorithms

Available Algorithms

Algorithm	Description	Best For
Genetic	Evolutionary optimization	General docking, large ligands
Monte Carlo	Metropolis sampling	Conformational sampling
PANDADOCK	Simulated annealing + MD	High accuracy, flexible ligands
Random Search	Stochastic exploration	Quick screening, benchmarking
Metal Docking	Coordination constraints	Metalloproteins, metal cofactors

Scoring Functions

Basic: VDW + Hydrogen bonding
Enhanced: + Electrostatics + Desolvation + Hydrophobic
Physics-based: Full MM-GBSA with entropy estimation
Metal-aware: Coordination geometry constraints

⚙️ Configuration

Command Line Options

# Essential options
-p, --protein PROTEIN       Protein PDB file
-l, --ligand LIGAND         Ligand MOL/SDF file
-o, --output OUTPUT         Output directory
-s, --site X Y Z            Binding site center
-r, --radius RADIUS         Binding site radius (Å)

# Algorithm selection
-a, --algorithm {genetic,monte-carlo,pandadock,random}
-i, --iterations N          Number of generations/steps
--exhaustiveness N          Independent runs

# Hardware acceleration
--use-gpu                   Enable GPU acceleration
--cpu-workers N             Number of CPU cores

# Scoring options
--enhanced-scoring          Enhanced scoring function
--physics-based            Physics-based scoring (MM-GBSA)
--local-opt                 Local optimization

# Flexibility
--auto-flex                 Automatic flexible residues
--flex-residues RES1 RES2   Manual flexible residues

# Output formats
--report-format {html,csv,json,all}
--detailed-energy           Energy component breakdown

🏗️ Architecture

PandaDock features a clean, modular architecture:

pandadock/
├── core/                   # Core docking engine
├── algorithms/             # Docking algorithms
├── scoring/               # Scoring functions
├── molecules/             # Molecule handling
├── analysis/              # Post-processing & analysis
├── hardware/              # GPU/CPU abstraction
├── screening/             # Virtual screening
├── cli/                   # Command-line interface
└── utils/                 # Utilities & helpers

🧪 Testing

# Run tests
pytest

# Run with coverage
pytest --cov=pandadock

# Run specific test categories
pytest tests/test_basic.py
pytest tests/test_scoring.py

📚 Documentation

GitHub Wiki: Comprehensive guides and tutorials
API Reference: Full Python API documentation
Examples: Sample scripts and workflows
Paper: [Cite PandaDock] (if published)

🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines.

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

📄 License

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

📞 Support

GitHub Issues: Report bugs or request features
Discussions: Community Q&A
Email: [email protected]

📖 Citation

If you use PandaDock in your research, please cite:

@software{pandadock2025,
  title={PandaDock: A Python-based Molecular Docking Platform},
  author={Panda, Pritam Kumar},
  year={2025},
  url={https://github.com/pritampanda15/PandaDock},
  version={3.0.0}
}

🙏 Acknowledgments

RDKit community for cheminformatics tools
PyTorch team for GPU acceleration framework
AutoDock Vina for inspiration
Scientific Python ecosystem

⚗️ Dock Smarter. Discover Faster. Build Better.

Name		Name	Last commit message	Last commit date
Latest commit History 240 Commits
.github/workflows		.github/workflows
debug_scripts		debug_scripts
example_outputs/plots		example_outputs/plots
logo		logo
pandadock		pandadock
refactored_summaries		refactored_summaries
tests		tests
.DS_Store		.DS_Store
.gitignore		.gitignore
ARCHITECTURE.md		ARCHITECTURE.md
CHANGELOG.md		CHANGELOG.md
LICENSE		LICENSE
MANIFEST.in		MANIFEST.in
README.md		README.md
conftest.py		conftest.py
contributing.md		contributing.md
pyproject.toml		pyproject.toml
requirements.txt		requirements.txt

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pritampanda15/PandaDock

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