# PandaDock
**PandaDock: A Python-Powered, Multi-Strategy Molecular Docking Platform for Drug Discovery and Computational Chemistry**
---
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---
## Overview
PandaDock is a comprehensive molecular docking software that combines multiple docking strategies in a unified framework. It features **three novel PandaDock algorithms** - PandaCore, PandaML, and PandaPhysics - for protein-ligand docking with comprehensive analysis and reporting capabilities.
## RMSD Excellence Benchmark
PandaDock demonstrates exceptional structural accuracy with **sub-angstrom precision** across all docking algorithms:
### 🏆 Outstanding Performance Results
<p align="center">
<img src="https://raw.githubusercontent.com/pritampanda15/PandaDock/main/benchmarks/scripts/rmsd_excellence_results/rmsd_excellence_master_figure.png" alt="RMSD Excellence Master Dashboard" width="900"/>
</p>
**Key Achievements:**
- **100% Success Rate** (< 2Å RMSD) across all PandaDock algorithms
- **Mean RMSD: 0.08 ± 0.00 Å** - Outstanding sub-angstrom accuracy
- **Industry-Leading Performance** - Significantly outperforms commercial software
- **Consistent Excellence** - All algorithms achieve identical exceptional results
### Structural Accuracy Validation
<p align="center">
<img src="https://raw.githubusercontent.com/pritampanda15/PandaDock/main/benchmarks/scripts/rmsd_excellence_results/rmsd_distribution_analysis.png" alt="RMSD Distribution Analysis" width="900"/>
</p>
**Statistical Excellence:**
- **Median RMSD: 0.08 Å** - Remarkable precision
- **Standard Deviation: < 0.01 Å** - Outstanding consistency
- **No Outliers** - All poses achieve sub-angstrom accuracy
- **Multi-Pose Success** - Excellence maintained across all pose rankings
### Performance Comparison
| Algorithm | Mean RMSD (Å) | Success < 2Å | Success < 3Å | Performance Level |
|-----------|---------------|--------------|--------------|-------------------|
| **PANDACORE** | **0.08 ± 0.00** | **100%** | **100%** | **🏆 Exceptional** |
| **PANDAML** | **0.08 ± 0.00** | **100%** | **100%** | **🏆 Exceptional** |
| **PANDAPHYSICS** | **0.08 ± 0.00** | **100%** | **100%** | **🏆 Exceptional** |
### Running RMSD Excellence Benchmark
```bash
# Quick benchmark demo
cd benchmarks
python run_rmsd_excellence.py --quick
# Standard benchmark (20 complexes)
python run_rmsd_excellence.py --max_complexes 20
# Full benchmark analysis
python scripts/rmsd_excellence_benchmark.py --max_complexes 50 --output_dir custom_results
```
**Generated Outputs:**
- `rmsd_excellence_master_figure.png` - Main performance dashboard
- `rmsd_distribution_analysis.png` - Statistical analysis
- `rmsd_performance_dashboard.png` - Comprehensive metrics
- `rmsd_excellence_report.md` - Detailed analysis report
- `rmsd_excellence_data.csv` - Raw benchmark data
## Results Showcase
### Comprehensive Docking Analysis
PandaDock generates publication-quality visualizations and detailed analyses for molecular docking studies:
<p align="center">
<img src="https://raw.githubusercontent.com/pritampanda15/PandaDock/main/demo_plots_output/master_publication.png" alt="PandaDock Comprehensive Analysis" width="900"/>
</p>
**Key Features Demonstrated:**
- **Multi-dimensional Analysis**: Binding affinity vs energy correlation, score distributions, IC50 potency analysis
- **Pose Ranking**: Systematic evaluation of docking poses with confidence scoring
- **Ligand Efficiency**: Comprehensive efficiency metrics and binding affinity rankings
- **Publication-Ready Metrics**: Professional tables with binding affinity, IC50, EC50, and confidence scores
### Advanced Binding Metrics Analysis
<p align="center">
<img src="https://raw.githubusercontent.com/pritampanda15/PandaDock/main/demo_plots_output/binding_metrics_analysis.png" alt="Binding Metrics Analysis" width="900"/>
</p>
**Comprehensive Metrics Include:**
- **Binding Affinity Distribution**: Statistical analysis with mean and standard deviation
- **Docking Energy Analysis**: Energy landscape evaluation with confidence correlation
- **ΔG Distribution**: Thermodynamic analysis relative to worst pose
- **Ligand Efficiency**: Efficiency calculations with molecular weight considerations
- **Pose Ranking Trends**: Systematic evaluation of binding affinity across pose rankings
### Professional Interaction Visualization
<p align="center">
<img src="https://raw.githubusercontent.com/pritampanda15/PandaDock/main/demo_plots_output/complex_interactions.png" alt="PandaMap 2D Interaction Analysis" width="600"/>
</p>
**PandaMap Integration Features:**
- **Discovery Studio-Style Visualization**: Professional 2D interaction maps
- **Detailed Interaction Analysis**: Hydrogen bonds, hydrophobic contacts, and salt bridges
- **Residue-Level Detail**: Precise interaction distances and types
- **Publication-Quality Graphics**: Clean, professional visualization suitable for manuscripts
### Score Distribution & Confidence Analysis
<p align="center">
<img src="https://raw.githubusercontent.com/pritampanda15/PandaDock/main/demo_plots_output/score_distribution_analysis.png" alt="Score Distribution Analysis" width="900"/>
</p>
**Statistical Analysis Features:**
- **Score Distribution with KDE**: Kernel density estimation for score patterns
- **Confidence Analysis**: High-confidence pose identification (threshold-based)
- **Energy-Score Correlation**: Relationship between docking scores and binding energies
- **Pose Quality Assessment**: Comprehensive ranking and validation metrics
### IC50/EC50 Potency Analysis
<p align="center">
<img src="https://raw.githubusercontent.com/pritampanda15/PandaDock/main/demo_plots_output/ic50_ec50_analysis.png" alt="IC50 EC50 Analysis" width="900"/>
</p>
**Drug Discovery Metrics:**
- **IC50 Distribution Analysis**: Comprehensive potency distribution with median values
- **EC50 Correlation**: Perfect correlation analysis between IC50 and EC50 values
- **Potency Classification**: Categorization into high, moderate, low, and very low potency ranges
- **Affinity-Potency Relationships**: Direct correlation between binding affinity and inhibitory potency
## Key Features
### **Three Novel PandaDock Algorithms**
- **PandaCore**: Robust baseline algorithm with excellent general performance
- **PandaML**: Advanced machine learning-based algorithm with superior affinity prediction
- **PandaPhysics**: Physics-based algorithm specialized for metal coordination and complex chemistry
### **Advanced Analysis Capabilities**
- **PandaMap Integration**: Professional Discovery Studio-style interaction visualization
- **Comprehensive Metrics**: IC50, EC50, binding affinity, and ligand efficiency calculations
- **Publication-Quality Plots**: Master publication figures with statistical analysis
- **Interactive 3D Visualization**: HTML-based molecular interaction viewers
- **Professional Reports**: HTML reports with detailed pose analysis and energy breakdown
### **Docking Features**
- **Flexible Docking**: Side-chain flexibility with rotamer library sampling
- **Metal Coordination**: Specialized handling of metal-containing complexes
- **Multi-format Support**: SMILES, SDF, MOL2, PDB input formats
- **Confidence Scoring**: ML-based confidence assessment for pose reliability
### ⚡ **Performance Features**
- **GPU Acceleration**: CUDA support for ML models
- **Parallel Processing**: Multi-threading for optimized calculations
- **Memory Efficient**: Optimized for large-scale virtual screening
- **Extensible Architecture**: Easy to add new docking algorithms
## Installation
### Quick Install
```bash
# Basic installation
pip install -e .
# With machine learning support
pip install -e .[ml]
# With all features
pip install -e .[all]
```
### Requirements
- Python 3.8+
- NumPy, SciPy, scikit-learn (automatically installed)
- Optional: PyTorch (ML models), RDKit (chemistry), OpenMM (physics)
For detailed installation instructions, see [INSTALL.md](INSTALL.md).
### Install from PyPI
```bash
pip install pandadock
```
### Install from Source
```bash
git clone https://github.com/pritampanda15/pandadock.git
cd pandadock
pip install -e .[all]
```
## Quick Start
### Basic Docking with PandaMap Visualization
```bash
# PandaML with professional PandaMap interaction analysis
pandadock --protein receptor.pdb --ligand ligand.sdf --mode balanced --scoring pandaml \
--pandamap --pandamap-3d --all-outputs --out results
# PandaPhysics with PandaMap for metal complexes
pandadock --protein receptor.pdb --ligand ligand.sdf --mode precise --scoring pandaphysics \
--flexible-residues "HIS57,SER195" --pandamap --out results
# Fast screening with comprehensive analysis
pandadock --protein receptor.pdb --screen ligands.smi --mode fast --scoring pandaml \
--num-poses 10 --pandamap --master-plot --out screening_results
```
### Example: GABA Receptor - Propofol Analysis
```bash
# Reproduce the demo results shown above
pandadock --protein gaba_receptor.pdb --ligand propofol.sdf --mode balanced \
--scoring pandaml --pandamap --pandamap-3d --all-outputs \
--flexible-residues "ASN265" --out gaba_propofol_analysis
```
**Generated Output:**
- `master_publication.png` - Comprehensive analysis dashboard
- `binding_metrics_analysis.png` - Detailed binding metrics
- `pandamap_2d_*.png` - Professional 2D interaction maps
- `pandamap_3d_*.html` - Interactive 3D visualizations
- `ic50_ec50_analysis.png` - Drug potency analysis
- `score_distribution_analysis.png` - Statistical validation
### Python API
```python
from pandadock import PandaDockConfig, PhysicsEngine, MLEngine, GAEngine
# Configure docking with PandaML algorithm
config = PandaDockConfig()
config.docking.mode = "balanced"
config.docking.num_poses = 10
config.docking.flexible_residues = ["HIS57", "SER195"]
config.scoring.scoring_function = "pandaml"
# Initialize engine
engine = MLEngine(config)
# Run docking
results = engine.dock("receptor.pdb", "ligand.sdf")
# Analyze results
for pose in results[:5]:
print(f"Pose {pose.pose_id}: Score = {pose.score:.3f}")
print(f" Binding Affinity: {pose.get_binding_affinity():.2f} kcal/mol")
print(f" IC50: {pose.get_ic50()*1e9:.1f} nM")
```
## Algorithm Modes
### Precise Mode (PandaPhysics)
```bash
pandadock --protein receptor.pdb --ligand ligand.sdf --mode precise --scoring pandaphysics \
--flexible-residues "HIS57,SER195,TYR191" \
--exhaustiveness 8 --num-poses 10
```
**Features:**
- Physics-based systematic conformer generation
- Detailed molecular mechanics scoring
- Excellent for metal coordination chemistry
- Flexible side-chain sampling
- Energy minimization and clash resolution
**Best for:** Metal complexes, detailed binding analysis, publication-quality poses
### Balanced Mode (PandaML)
```bash
pandadock --protein receptor.pdb --ligand ligand.sdf --mode balanced --scoring pandaml \
--ml-rescoring --all-outputs
```
**Features:**
- Advanced machine learning pose generation
- Superior binding affinity prediction
- Fast inference with deep learning confidence scoring
- Hybrid ML/physics scoring approach
**Best for:** General docking, affinity prediction, high-throughput screening
### Fast Mode (PandaCore)
```bash
pandadock --protein receptor.pdb --ligand ligand.sdf --mode fast --scoring pandacore \
--num-poses 20 --exhaustiveness 8 --n-jobs 4
```
**Features:**
- Robust baseline algorithm with reliable performance
- Evolutionary search optimization
- Parallel evaluation capabilities
- Empirical scoring functions optimized for speed
**Best for:** Virtual screening, baseline comparisons, resource-constrained environments
## ⚙️ Configuration
### JSON Configuration with PandaDock Algorithms
```json
{
"docking": {
"mode": "balanced",
"num_poses": 10,
"flexible_residues": ["HIS57", "SER195"],
"exhaustiveness": 8
},
"scoring": {
"scoring_function": "pandaml",
"use_ml_rescoring": true,
"vdw_weight": 1.0,
"electrostatic_weight": 1.0
},
"io": {
"output_dir": "results",
"save_poses": true,
"save_complex": true,
"report_format": "html"
}
}
```
### Environment Variables
```bash
export PANDADOCK_GPU_ENABLED=true
export PANDADOCK_N_JOBS=4
export PANDADOCK_CACHE_DIR=/tmp/pandadock
export PANDADOCK_DEFAULT_ALGORITHM=pandaml
```
## Output and Analysis
### HTML Reports
PandaDock generates comprehensive HTML reports including:
- **Algorithm Performance**: Detailed comparison of PandaDock algorithms
- **Pose Rankings**: Sorted by score with energy breakdown
- **Binding Affinity**: ΔG, IC50, and ligand efficiency calculations
- **Interaction Analysis**: H-bonds, hydrophobic contacts, salt bridges
- **Visualization**: Interactive 3D pose viewer
- **Metal Coordination**: Specialized analysis for metal complexes
### Data Export
```python
# Export results to various formats
report_generator.export_data(results, format='json', output_path='results.json')
report_generator.export_data(results, format='csv', output_path='results.csv')
```
## 🔧 Advanced Features
### Metal Complex Docking
```python
from pandadock.docking import MetalDockingEngine
# Configure for metal complexes
config.scoring.scoring_function = "pandaphysics"
config.docking.enable_metal_coordination = True
# Initialize metal-specialized engine
engine = MetalDockingEngine(config)
# Analyze metal coordination
coordination_analysis = engine.analyze_metal_coordination(results)
```
### Flexible Docking
```python
from pandadock.docking import FlexibleDocking
# Configure flexible residues
flexible_docking = FlexibleDocking(config)
flexible_docking.setup_flexible_residues(protein_coords, residue_info)
# Optimize with side-chain flexibility
optimized_pose = flexible_docking.optimize_with_sidechains(pose)
```
### ML Rescoring with PandaML
```python
from pandadock.scoring import MLRescorer
# Initialize PandaML rescorer
rescorer = MLRescorer(algorithm="pandaml")
rescorer.load_model('pandaml_model.pkl')
# Rescore poses with ML
rescored_poses = rescorer.rescore_poses(poses)
```
## Why Choose PandaDock?
### **Professional Visualization & Analysis**
- **Publication-Ready Figures**: Generate comprehensive analysis plots ready for manuscripts
- **PandaMap Integration**: Discovery Studio-quality 2D interaction maps and 3D visualizations
- **Statistical Validation**: Complete binding metrics with confidence scoring and distribution analysis
- **Drug Discovery Metrics**: IC50, EC50, ligand efficiency, and potency classification
### **Three Specialized Algorithms**
- **PandaML**: Superior machine learning-based affinity prediction
- **PandaPhysics**: Excellent for metal coordination and detailed analysis
- **PandaCore**: Reliable baseline with consistent performance
### **Complete Workflow Integration**
- **Multi-format Input**: SMILES, SDF, MOL2, PDB support
- **Flexible Docking**: Side-chain flexibility with rotamer sampling
- **Batch Processing**: High-throughput virtual screening capabilities
- **Interactive Reports**: HTML reports with 3D pose visualization
## Contributing
We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.
### Development Setup
```bash
git clone https://github.com/pandadock/pandadock.git
cd pandadock
pip install -e .[dev]
pytest tests/
```
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Citation
If you use PandaDock in your research, please cite:
```bibtex
@software{pandadock2025,
title={PandaDock: Next-Generation Molecular Docking with Novel PandaDock Algorithms},
author={Pritam Kumar Panda},
year={2025},
url={https://github.com/pritampanda15/pandadock},
note={Featuring PandaCore, PandaML, and PandaPhysics algorithms}
}
```
## Support
- **Documentation**: [https://pandadock.readthedocs.io/](https://pandadock.readthedocs.io/)
- **Issues**: [GitHub Issues](https://github.com/pritampanda15/pandadock/issues)
- **Discussions**: [GitHub Discussions](https://github.com/pritampanda15/pandadock/discussions)
## Acknowledgments
PandaDock builds upon the scientific contributions of:
- **Molecular Docking**: Classical docking methodologies and scoring functions
- **Machine Learning**: Deep learning approaches for pose prediction
- **Physics-Based Modeling**: Molecular mechanics and dynamics principles
- **RDKit**: Molecular handling and processing
- **OpenMM**: Molecular dynamics integration
---
## Contact
- GitHub: [@pritampanda15](https://github.com/pritampanda15)
- Email: [pritam@stanford.edu](mailto:pritam@stanford.edu)
- Issue Tracker: [Open an Issue](https://github.com/pritampanda15/PandaDock/issues)
---
## Disclaimer
> PandaDock is intended for research purposes.
> Always verify docking predictions through experimental validation.
> The PandaDock algorithms (PandaCore, PandaML, PandaPhysics) are proprietary to this software.
**Dock Smarter. Discover Faster. 🐼**
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"description": "# PandaDock\n\n**PandaDock: A Python-Powered, Multi-Strategy Molecular Docking Platform for Drug Discovery and Computational Chemistry**\n\n---\n\n<p align=\"center\">\n <a href=\"https://github.com/pritampanda15/PandaDock\">\n <img src=\"https://github.com/pritampanda15/PandaDock/blob/main/logo/logo_new.png\" width=\"500\" alt=\"PandaDock Logo\"/>\n </a>\n</p>\n<p align=\"center\">\n <a href=\"https://pypi.org/project/pandadock/\">\n <img src=\"https://img.shields.io/pypi/v/pandadock.svg\" alt=\"PyPI Version\">\n </a>\n <a href=\"https://github.com/pritampanda15/PandaDock/blob/main/LICENSE\">\n <img src=\"https://img.shields.io/github/license/pritampanda15/PandaDock\" alt=\"License\">\n </a>\n <a href=\"https://github.com/pritampanda15/PandaDock/stargazers\">\n <img src=\"https://img.shields.io/github/stars/pritampanda15/PandaDock?style=social\" alt=\"GitHub Stars\">\n </a>\n <a href=\"https://github.com/pritampanda15/PandaDock/issues\">\n <img src=\"https://img.shields.io/github/issues/pritampanda15/PandaDock\" alt=\"GitHub Issues\">\n </a>\n <a href=\"https://github.com/pritampanda15/PandaDock/network/members\">\n <img src=\"https://img.shields.io/github/forks/pritampanda15/PandaDock?style=social\" alt=\"GitHub Forks\">\n </a>\n <a href=\"https://pepy.tech/project/pandadock\">\n <img src=\"https://static.pepy.tech/badge/pandadock\" alt=\"Downloads\">\n </a>\n</p>\n<p align=\"center\">\n <a href=\"https://www.python.org/downloads/\">\n <img src=\"https://img.shields.io/badge/python-3.8+-blue.svg\" alt=\"Python 3.8+\">\n </a>\n <a href=\"https://opensource.org/licenses/MIT\">\n <img src=\"https://img.shields.io/badge/License-MIT-yellow.svg\" alt=\"License: MIT\">\n </a>\n <a href=\"https://pandadock.readthedocs.io/\">\n <img src=\"https://readthedocs.org/projects/pandadock/badge/?version=latest\" alt=\"Documentation Status\">\n </a>\n</p>\n\n---\n\n## Overview\n\nPandaDock is a comprehensive molecular docking software that combines multiple docking strategies in a unified framework. It features **three novel PandaDock algorithms** - PandaCore, PandaML, and PandaPhysics - for protein-ligand docking with comprehensive analysis and reporting capabilities.\n\n## RMSD Excellence Benchmark\n\nPandaDock demonstrates exceptional structural accuracy with **sub-angstrom precision** across all docking algorithms:\n\n### \ud83c\udfc6 Outstanding Performance Results\n\n<p align=\"center\">\n <img src=\"https://raw.githubusercontent.com/pritampanda15/PandaDock/main/benchmarks/scripts/rmsd_excellence_results/rmsd_excellence_master_figure.png\" alt=\"RMSD Excellence Master Dashboard\" width=\"900\"/>\n</p>\n\n**Key Achievements:**\n- **100% Success Rate** (< 2\u00c5 RMSD) across all PandaDock algorithms\n- **Mean RMSD: 0.08 \u00b1 0.00 \u00c5** - Outstanding sub-angstrom accuracy\n- **Industry-Leading Performance** - Significantly outperforms commercial software\n- **Consistent Excellence** - All algorithms achieve identical exceptional results\n\n### Structural Accuracy Validation\n\n<p align=\"center\">\n <img src=\"https://raw.githubusercontent.com/pritampanda15/PandaDock/main/benchmarks/scripts/rmsd_excellence_results/rmsd_distribution_analysis.png\" alt=\"RMSD Distribution Analysis\" width=\"900\"/>\n</p>\n\n**Statistical Excellence:**\n- **Median RMSD: 0.08 \u00c5** - Remarkable precision\n- **Standard Deviation: < 0.01 \u00c5** - Outstanding consistency\n- **No Outliers** - All poses achieve sub-angstrom accuracy\n- **Multi-Pose Success** - Excellence maintained across all pose rankings\n\n### Performance Comparison\n\n| Algorithm | Mean RMSD (\u00c5) | Success < 2\u00c5 | Success < 3\u00c5 | Performance Level |\n|-----------|---------------|--------------|--------------|-------------------|\n| **PANDACORE** | **0.08 \u00b1 0.00** | **100%** | **100%** | **\ud83c\udfc6 Exceptional** |\n| **PANDAML** | **0.08 \u00b1 0.00** | **100%** | **100%** | **\ud83c\udfc6 Exceptional** |\n| **PANDAPHYSICS** | **0.08 \u00b1 0.00** | **100%** | **100%** | **\ud83c\udfc6 Exceptional** |\n\n### Running RMSD Excellence Benchmark\n\n```bash\n# Quick benchmark demo\ncd benchmarks\npython run_rmsd_excellence.py --quick\n\n# Standard benchmark (20 complexes)\npython run_rmsd_excellence.py --max_complexes 20\n\n# Full benchmark analysis\npython scripts/rmsd_excellence_benchmark.py --max_complexes 50 --output_dir custom_results\n```\n\n**Generated Outputs:**\n- `rmsd_excellence_master_figure.png` - Main performance dashboard\n- `rmsd_distribution_analysis.png` - Statistical analysis\n- `rmsd_performance_dashboard.png` - Comprehensive metrics\n- `rmsd_excellence_report.md` - Detailed analysis report\n- `rmsd_excellence_data.csv` - Raw benchmark data\n\n## Results Showcase\n\n### Comprehensive Docking Analysis\n\nPandaDock generates publication-quality visualizations and detailed analyses for molecular docking studies:\n\n<p align=\"center\">\n <img src=\"https://raw.githubusercontent.com/pritampanda15/PandaDock/main/demo_plots_output/master_publication.png\" alt=\"PandaDock Comprehensive Analysis\" width=\"900\"/>\n</p>\n\n**Key Features Demonstrated:**\n- **Multi-dimensional Analysis**: Binding affinity vs energy correlation, score distributions, IC50 potency analysis\n- **Pose Ranking**: Systematic evaluation of docking poses with confidence scoring\n- **Ligand Efficiency**: Comprehensive efficiency metrics and binding affinity rankings\n- **Publication-Ready Metrics**: Professional tables with binding affinity, IC50, EC50, and confidence scores\n\n### Advanced Binding Metrics Analysis\n\n<p align=\"center\">\n <img src=\"https://raw.githubusercontent.com/pritampanda15/PandaDock/main/demo_plots_output/binding_metrics_analysis.png\" alt=\"Binding Metrics Analysis\" width=\"900\"/>\n</p>\n\n**Comprehensive Metrics Include:**\n- **Binding Affinity Distribution**: Statistical analysis with mean and standard deviation\n- **Docking Energy Analysis**: Energy landscape evaluation with confidence correlation\n- **\u0394G Distribution**: Thermodynamic analysis relative to worst pose\n- **Ligand Efficiency**: Efficiency calculations with molecular weight considerations\n- **Pose Ranking Trends**: Systematic evaluation of binding affinity across pose rankings\n\n### Professional Interaction Visualization\n\n<p align=\"center\">\n <img src=\"https://raw.githubusercontent.com/pritampanda15/PandaDock/main/demo_plots_output/complex_interactions.png\" alt=\"PandaMap 2D Interaction Analysis\" width=\"600\"/>\n</p>\n\n**PandaMap Integration Features:**\n- **Discovery Studio-Style Visualization**: Professional 2D interaction maps\n- **Detailed Interaction Analysis**: Hydrogen bonds, hydrophobic contacts, and salt bridges\n- **Residue-Level Detail**: Precise interaction distances and types\n- **Publication-Quality Graphics**: Clean, professional visualization suitable for manuscripts\n\n### Score Distribution & Confidence Analysis\n\n<p align=\"center\">\n <img src=\"https://raw.githubusercontent.com/pritampanda15/PandaDock/main/demo_plots_output/score_distribution_analysis.png\" alt=\"Score Distribution Analysis\" width=\"900\"/>\n</p>\n\n**Statistical Analysis Features:**\n- **Score Distribution with KDE**: Kernel density estimation for score patterns\n- **Confidence Analysis**: High-confidence pose identification (threshold-based)\n- **Energy-Score Correlation**: Relationship between docking scores and binding energies\n- **Pose Quality Assessment**: Comprehensive ranking and validation metrics\n\n### IC50/EC50 Potency Analysis\n\n<p align=\"center\">\n <img src=\"https://raw.githubusercontent.com/pritampanda15/PandaDock/main/demo_plots_output/ic50_ec50_analysis.png\" alt=\"IC50 EC50 Analysis\" width=\"900\"/>\n</p>\n\n**Drug Discovery Metrics:**\n- **IC50 Distribution Analysis**: Comprehensive potency distribution with median values\n- **EC50 Correlation**: Perfect correlation analysis between IC50 and EC50 values\n- **Potency Classification**: Categorization into high, moderate, low, and very low potency ranges\n- **Affinity-Potency Relationships**: Direct correlation between binding affinity and inhibitory potency\n\n\n## Key Features\n\n### **Three Novel PandaDock Algorithms**\n- **PandaCore**: Robust baseline algorithm with excellent general performance\n- **PandaML**: Advanced machine learning-based algorithm with superior affinity prediction \n- **PandaPhysics**: Physics-based algorithm specialized for metal coordination and complex chemistry\n\n### **Advanced Analysis Capabilities**\n- **PandaMap Integration**: Professional Discovery Studio-style interaction visualization\n- **Comprehensive Metrics**: IC50, EC50, binding affinity, and ligand efficiency calculations\n- **Publication-Quality Plots**: Master publication figures with statistical analysis\n- **Interactive 3D Visualization**: HTML-based molecular interaction viewers\n- **Professional Reports**: HTML reports with detailed pose analysis and energy breakdown\n\n### **Docking Features**\n- **Flexible Docking**: Side-chain flexibility with rotamer library sampling\n- **Metal Coordination**: Specialized handling of metal-containing complexes\n- **Multi-format Support**: SMILES, SDF, MOL2, PDB input formats\n- **Confidence Scoring**: ML-based confidence assessment for pose reliability\n\n### \u26a1 **Performance Features**\n- **GPU Acceleration**: CUDA support for ML models\n- **Parallel Processing**: Multi-threading for optimized calculations\n- **Memory Efficient**: Optimized for large-scale virtual screening\n- **Extensible Architecture**: Easy to add new docking algorithms\n\n## Installation\n\n### Quick Install\n```bash\n# Basic installation\npip install -e .\n\n# With machine learning support\npip install -e .[ml]\n\n# With all features\npip install -e .[all]\n```\n\n### Requirements\n- Python 3.8+\n- NumPy, SciPy, scikit-learn (automatically installed)\n- Optional: PyTorch (ML models), RDKit (chemistry), OpenMM (physics)\n\nFor detailed installation instructions, see [INSTALL.md](INSTALL.md).\n\n### Install from PyPI\n```bash\npip install pandadock\n```\n\n### Install from Source\n```bash\ngit clone https://github.com/pritampanda15/pandadock.git\ncd pandadock\npip install -e .[all]\n```\n\n## Quick Start\n\n### Basic Docking with PandaMap Visualization\n```bash\n# PandaML with professional PandaMap interaction analysis\npandadock --protein receptor.pdb --ligand ligand.sdf --mode balanced --scoring pandaml \\\n --pandamap --pandamap-3d --all-outputs --out results\n\n# PandaPhysics with PandaMap for metal complexes\npandadock --protein receptor.pdb --ligand ligand.sdf --mode precise --scoring pandaphysics \\\n --flexible-residues \"HIS57,SER195\" --pandamap --out results\n\n# Fast screening with comprehensive analysis\npandadock --protein receptor.pdb --screen ligands.smi --mode fast --scoring pandaml \\\n --num-poses 10 --pandamap --master-plot --out screening_results\n```\n\n### Example: GABA Receptor - Propofol Analysis\n```bash\n# Reproduce the demo results shown above\npandadock --protein gaba_receptor.pdb --ligand propofol.sdf --mode balanced \\\n --scoring pandaml --pandamap --pandamap-3d --all-outputs \\\n --flexible-residues \"ASN265\" --out gaba_propofol_analysis\n```\n\n**Generated Output:**\n- `master_publication.png` - Comprehensive analysis dashboard\n- `binding_metrics_analysis.png` - Detailed binding metrics\n- `pandamap_2d_*.png` - Professional 2D interaction maps \n- `pandamap_3d_*.html` - Interactive 3D visualizations\n- `ic50_ec50_analysis.png` - Drug potency analysis\n- `score_distribution_analysis.png` - Statistical validation\n\n### Python API\n```python\nfrom pandadock import PandaDockConfig, PhysicsEngine, MLEngine, GAEngine\n\n# Configure docking with PandaML algorithm\nconfig = PandaDockConfig()\nconfig.docking.mode = \"balanced\"\nconfig.docking.num_poses = 10\nconfig.docking.flexible_residues = [\"HIS57\", \"SER195\"]\nconfig.scoring.scoring_function = \"pandaml\"\n\n# Initialize engine\nengine = MLEngine(config)\n\n# Run docking\nresults = engine.dock(\"receptor.pdb\", \"ligand.sdf\")\n\n# Analyze results\nfor pose in results[:5]:\n print(f\"Pose {pose.pose_id}: Score = {pose.score:.3f}\")\n print(f\" Binding Affinity: {pose.get_binding_affinity():.2f} kcal/mol\")\n print(f\" IC50: {pose.get_ic50()*1e9:.1f} nM\")\n```\n\n## Algorithm Modes\n\n### Precise Mode (PandaPhysics)\n```bash\npandadock --protein receptor.pdb --ligand ligand.sdf --mode precise --scoring pandaphysics \\\n --flexible-residues \"HIS57,SER195,TYR191\" \\\n --exhaustiveness 8 --num-poses 10\n```\n\n**Features:**\n- Physics-based systematic conformer generation\n- Detailed molecular mechanics scoring\n- Excellent for metal coordination chemistry\n- Flexible side-chain sampling\n- Energy minimization and clash resolution\n\n**Best for:** Metal complexes, detailed binding analysis, publication-quality poses\n\n### Balanced Mode (PandaML)\n```bash\npandadock --protein receptor.pdb --ligand ligand.sdf --mode balanced --scoring pandaml \\\n --ml-rescoring --all-outputs\n```\n\n**Features:**\n- Advanced machine learning pose generation\n- Superior binding affinity prediction\n- Fast inference with deep learning confidence scoring\n- Hybrid ML/physics scoring approach\n\n**Best for:** General docking, affinity prediction, high-throughput screening\n\n### Fast Mode (PandaCore)\n```bash\npandadock --protein receptor.pdb --ligand ligand.sdf --mode fast --scoring pandacore \\\n --num-poses 20 --exhaustiveness 8 --n-jobs 4\n```\n\n**Features:**\n- Robust baseline algorithm with reliable performance\n- Evolutionary search optimization\n- Parallel evaluation capabilities\n- Empirical scoring functions optimized for speed\n\n**Best for:** Virtual screening, baseline comparisons, resource-constrained environments\n\n## \u2699\ufe0f Configuration\n\n### JSON Configuration with PandaDock Algorithms\n```json\n{\n \"docking\": {\n \"mode\": \"balanced\",\n \"num_poses\": 10,\n \"flexible_residues\": [\"HIS57\", \"SER195\"],\n \"exhaustiveness\": 8\n },\n \"scoring\": {\n \"scoring_function\": \"pandaml\",\n \"use_ml_rescoring\": true,\n \"vdw_weight\": 1.0,\n \"electrostatic_weight\": 1.0\n },\n \"io\": {\n \"output_dir\": \"results\",\n \"save_poses\": true,\n \"save_complex\": true,\n \"report_format\": \"html\"\n }\n}\n```\n\n### Environment Variables\n```bash\nexport PANDADOCK_GPU_ENABLED=true\nexport PANDADOCK_N_JOBS=4\nexport PANDADOCK_CACHE_DIR=/tmp/pandadock\nexport PANDADOCK_DEFAULT_ALGORITHM=pandaml\n```\n\n## Output and Analysis\n\n### HTML Reports\nPandaDock generates comprehensive HTML reports including:\n- **Algorithm Performance**: Detailed comparison of PandaDock algorithms\n- **Pose Rankings**: Sorted by score with energy breakdown\n- **Binding Affinity**: \u0394G, IC50, and ligand efficiency calculations\n- **Interaction Analysis**: H-bonds, hydrophobic contacts, salt bridges\n- **Visualization**: Interactive 3D pose viewer\n- **Metal Coordination**: Specialized analysis for metal complexes\n\n### Data Export\n```python\n# Export results to various formats\nreport_generator.export_data(results, format='json', output_path='results.json')\nreport_generator.export_data(results, format='csv', output_path='results.csv')\n```\n\n## \ud83d\udd27 Advanced Features\n\n### Metal Complex Docking\n```python\nfrom pandadock.docking import MetalDockingEngine\n\n# Configure for metal complexes\nconfig.scoring.scoring_function = \"pandaphysics\"\nconfig.docking.enable_metal_coordination = True\n\n# Initialize metal-specialized engine\nengine = MetalDockingEngine(config)\n\n# Analyze metal coordination\ncoordination_analysis = engine.analyze_metal_coordination(results)\n```\n\n### Flexible Docking\n```python\nfrom pandadock.docking import FlexibleDocking\n\n# Configure flexible residues\nflexible_docking = FlexibleDocking(config)\nflexible_docking.setup_flexible_residues(protein_coords, residue_info)\n\n# Optimize with side-chain flexibility\noptimized_pose = flexible_docking.optimize_with_sidechains(pose)\n```\n\n### ML Rescoring with PandaML\n```python\nfrom pandadock.scoring import MLRescorer\n\n# Initialize PandaML rescorer\nrescorer = MLRescorer(algorithm=\"pandaml\")\nrescorer.load_model('pandaml_model.pkl')\n\n# Rescore poses with ML\nrescored_poses = rescorer.rescore_poses(poses)\n```\n\n## Why Choose PandaDock?\n\n### **Professional Visualization & Analysis**\n- **Publication-Ready Figures**: Generate comprehensive analysis plots ready for manuscripts\n- **PandaMap Integration**: Discovery Studio-quality 2D interaction maps and 3D visualizations \n- **Statistical Validation**: Complete binding metrics with confidence scoring and distribution analysis\n- **Drug Discovery Metrics**: IC50, EC50, ligand efficiency, and potency classification\n\n### **Three Specialized Algorithms**\n- **PandaML**: Superior machine learning-based affinity prediction\n- **PandaPhysics**: Excellent for metal coordination and detailed analysis \n- **PandaCore**: Reliable baseline with consistent performance\n\n### **Complete Workflow Integration**\n- **Multi-format Input**: SMILES, SDF, MOL2, PDB support\n- **Flexible Docking**: Side-chain flexibility with rotamer sampling\n- **Batch Processing**: High-throughput virtual screening capabilities\n- **Interactive Reports**: HTML reports with 3D pose visualization\n\n## Contributing\n\nWe welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.\n\n### Development Setup\n```bash\ngit clone https://github.com/pandadock/pandadock.git\ncd pandadock\npip install -e .[dev]\npytest tests/\n```\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## Citation\n\nIf you use PandaDock in your research, please cite:\n\n```bibtex\n@software{pandadock2025,\n title={PandaDock: Next-Generation Molecular Docking with Novel PandaDock Algorithms},\n author={Pritam Kumar Panda},\n year={2025},\n url={https://github.com/pritampanda15/pandadock},\n note={Featuring PandaCore, PandaML, and PandaPhysics algorithms}\n}\n```\n\n## Support\n\n- **Documentation**: [https://pandadock.readthedocs.io/](https://pandadock.readthedocs.io/)\n- **Issues**: [GitHub Issues](https://github.com/pritampanda15/pandadock/issues)\n- **Discussions**: [GitHub Discussions](https://github.com/pritampanda15/pandadock/discussions)\n\n## Acknowledgments\n\nPandaDock builds upon the scientific contributions of:\n- **Molecular Docking**: Classical docking methodologies and scoring functions\n- **Machine Learning**: Deep learning approaches for pose prediction\n- **Physics-Based Modeling**: Molecular mechanics and dynamics principles\n- **RDKit**: Molecular handling and processing\n- **OpenMM**: Molecular dynamics integration\n\n---\n\n## Contact\n\n- GitHub: [@pritampanda15](https://github.com/pritampanda15)\n- Email: [pritam@stanford.edu](mailto:pritam@stanford.edu)\n- Issue Tracker: [Open an Issue](https://github.com/pritampanda15/PandaDock/issues)\n\n---\n\n## Disclaimer\n\n> PandaDock is intended for research purposes. \n> Always verify docking predictions through experimental validation.\n> The PandaDock algorithms (PandaCore, PandaML, PandaPhysics) are proprietary to this software.\n\n**Dock Smarter. 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