🧬 DockInsight

DockInsight is an automated molecular docking and drug-likeness analysis pipeline developed using Python. It integrates protein retrieval, ligand retrieval, molecular descriptor calculation, Lipinski's Rule of Five evaluation, GNINA-based molecular docking, docking analysis, visualization, and automated PDF report generation into a single workflow.

The pipeline accepts any valid Protein Data Bank (PDB) ID and ligand name, making it flexible for analyzing different protein–ligand systems.

• Protein download from the Protein Data Bank (PDB)
• Automatic crystal ligand detection
• Ligand retrieval from PubChem
• Protein preparation for docking
• Molecular descriptor calculation using RDKit
• Lipinski's Rule of Five analysis
• Molecular docking using GNINA
• Binding affinity analysis
• Affinity visualization
• CSV export of docking results
• Automated PDF report generation

• Python
• Google Colab
• GNINA
• RDKit
• PubChemPy
• Biopython
• Open Babel
• Pandas
• Matplotlib
• ReportLab

User Input
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Protein Download
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Crystal Ligand Detection
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Ligand Download
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Protein Preparation
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Drug-Likeness Analysis
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Molecular Descriptor Calculation
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GNINA Molecular Docking
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Docking Analysis
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Affinity Plot
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PDF Report Generation

DockInsight/
│
├── DockInsight.ipynb
├── README.md
├── requirements.txt
├── LICENSE
│
├── Reports/
│   └── Sample_Docking_Report.pdf
│
├── Results/
│   ├── results.csv
│   ├── affinity_plot.png
│   └── docking.log
│
└── Images/
    └── ligand2D.png

Clone the repository:

git clone https://github.com/Saummyaa/DockInsight.git

Install the required Python packages:

pip install -r requirements.txt

1. Open the notebook in Google Colab or Jupyter Notebook.
2. Enter a valid Protein Data Bank (PDB) ID.
3. Enter the ligand name.
4. Run all notebook cells sequentially.
5. View the generated docking report and results.

The workflow automatically generates:

• Docking results table
• Binding affinity plot
• Molecular descriptor analysis
• Lipinski's Rule of Five evaluation
• PDF docking report
• CSV results file

• Batch docking of multiple ligands
• Molecular Dynamics simulation integration
• ADMET prediction
• Web application deployment
• AI-assisted binding affinity prediction