A powerful GUI application for molecular clustering and visualization using BitBirch clustering algorithm with PCA dimensionality reduction. Provides a tool for chemists to visualize and analyze large chemical libraries.
- Interactive Visualization:
- Overview of all clusters with size-based filtering
- Detailed cluster exploration with molecular structure display
- Interactive zoom, pan, and hover functionality
- Data Persistence: Save and load clustering results for later analysis
- Options:
- User can specify Similarity Threshold, Branching Factor, FP Radius and Bits for Morgan Fingerprints
- Range of cluster sizes to view by the number of molecules
| Overview Mode | Detail Mode |
|---|---|
 |
 |
| Shows cluster centroids containing molecules in the specified range. | Shows molecules within a cluster, allows for detailed exploration. |
gitclone https://github.com/mqcomplab/NAMI.git
cd NAMI# Create a virtual environment (recommended)
python -m venv NAMI_env
source NAMI_env/bin/activate # On Windows: NAMI_env\Scripts\activate
# Install dependencies
pip install tkinter pandas numpy scikit-learn rdkit matplotlib tqdm scipy mplcursors pillowInstructions for installation of BitBIRCH at: https://github.com/mqcomplab/bitbirch.
python NAMI/main.py-
Load Data: Click "Load SMILES CSV" to load your molecular dataset
- Supported formats: CSV files with SMILES column
-
Configure Parameters:
- BB Threshold: BitBirch clustering threshold (0.0-1.0)
- Branching Factor: Maximum number of subclusters per node
- FP Radius: Morgan fingerprint radius
- FP Bits: Number of bits in fingerprint
- Min/Max Large Cluster: Size range for clusters shown in overview
-
Process & Cluster: Click to generate fingerprints and perform clustering
-
Explore Results:
- Overview: See all clusters, click to explore details
- Detail View: Hover over molecules to see structures and properties
- Use mouse wheel to zoom, drag to pan
-
Save/Load: Save clustering results for later analysis
Paper: