🧠 TopoIndex

TopoIndex is a Python library for computing topological indices of molecular and graph structures using NetworkX, RDKit and Pandas. It supports input via SMILES strings and is designed for mathematical chemistry, cheminformatics, and graph theory applications.

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📦 Features

• ✅ Wiener Index
• ✅ Zagreb Indices (First & Second)
• ✅ Hyper-Wiener Index
• ✅ Randic Index
• ✅ Balaban Index
• ✅ Eccentric Connectivity Index
• ✅ Graph Invariants
  • Node & edge count, diameter, radius, girth
  • Spectral radius, Estrada index, Graph energy
  • Optional distance matrix output
• ✅ Command-Line Interface (CLI)
• 📘 More indices coming soon!

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🚀 Installation

Once released on PyPI:

pip install topoindex

For manual installation:

git clone https://github.com/avimallick/topoindex.git
cd topoindex
pip install -e .

⚠️ Requires networkx, rdkit and pandas.

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🧪 Example Usage (Python)

from topoindex import (
    wiener_index,
    first_zagreb_index,
    second_zagreb_index,
    hyper_wiener_index,
    randic_index,
    balaban_index,
    eccentric_connectivity_index,
)
from topoindex.graphs.invariants import compute_invariants

smiles = "CCO"  # Ethanol

print("Wiener:", wiener_index(smiles))
print("Zagreb-1:", first_zagreb_index(smiles))
print("Zagreb-2:", second_zagreb_index(smiles))
print("Hyper-Wiener:", hyper_wiener_index(smiles))
print("Randic:", randic_index(smiles))
print("Balaban:", balaban_index(smiles))
print("Eccentric Connectivity:", eccentric_connectivity_index(smiles))

invariants = compute_invariants(smiles, include_matrix=True, matrix_format="pandas")
print("Graph Invariants:", invariants)

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🖥️ CLI Usage

TopoIndex also provides a command-line interface:

topoindex --smiles "C1=CC=CC=C1" --matrix --matrix-format pandas

Example Output (JSON):

{
  "num_nodes": 6,
  "num_edges": 6,
  "diameter": 3,
  "radius": 3,
  "girth": 6,
  "spectral_radius": 2.0,
  "estrada_index": 9.38,
  "graph_energy": 8.47,
  "distance_matrix": [[0, 1, 2, 1, 2, 3], [1, 0, 1, 2, 3, 2], ...]
}

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🧠 What are Topological Indices?

Topological indices are numerical descriptors of graph structure widely used in:

• Molecular property prediction
• Graph similarity comparison
• Cheminformatics and QSPR/QSAR
• Network analysis

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📝 License

Licensed under the MIT License.

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🤝 Contributing

Pull requests are welcome. If you'd like to contribute a new index or optimization, please open an issue first.

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👨‍💻 Author

Developed by Avinash Mallick.