molecularnetwork 0.4.6
Helper Library to create Molecular Networks
License: MIT
Requires Python: <4.0,>=3.9

# MolecularNetwork
![banner](https://github.com/Manas02/molecularnetwork/raw/main/banner.png?raw=True)

`MolecularNetwork` is a Python package that facilitates the creation of molecular networks based on molecular similarities. It leverages RDKit for molecular operations, and NetworkX for graph operations.

## Features

- **Molecular Descriptors:** Calculate molecular fingerprints using descriptor types (e.g., Morgan fingerprints, MACCS keys, AtomPairs).

- **Similarity Metrics:** Choose from a variety of similarity metrics (e.g., Tanimoto, Cosine, Dice) to quantify molecular similarities.

- **Modularity:** The code is organized into modular components, promoting easy extension and customization.

## Installation

To install the MolecularNetwork package, you can use `pip`. Ensure you have Python and pip installed on your system.

```bash
pip install molecularnetwork
```

## Usage
Here's a simple example of how to use the MolecularNetwork package:

``` python
from molecularnetwork import MolecularNetwork

# Define SMILES strings and classes
smiles_list = ["CCO", "CCN", "CCC", "CCF"]

# By default `0` is the categorical_label unless specified like following
classes = ["alcohol", "amine", "alkane", "fluoride"] 

# Create MolecularNetwork instance
network = MolecularNetwork(descriptor="morgan2", sim_metric="tanimoto", sim_threshold=0.25)

# Generate the molecular network graph
graph = network.create_graph(smiles_list, classes) # network.get_graph() also returns graph

# Graph `Node` Attributes
graph.nodes[0]['fp'] # Returns ECFP4 fingerprint for node 0 ["CCO"].
graph.nodes[0]['smiles'] # Returns SMILES for node 0 ["CCO"].
graph.nodes[0]['categorical_label'] # Returns `alcohol`

# Graph `Edge` Attributes
graph[0][1]['similarity'] # Returns the edge weight attribute which is the similarity between node 0 and 1
# Returns 0.3333333333333333

# Save the graph to a file
network.save_graph("test_molecular_network.joblib")

# Read graph from a file
graph = network.read_graph("test_molecular_network.joblib")
```

### Plot Molecular Network
```py
def draw_graph_with_attributes(G, node_attribute='categorical_label', edge_attribute='similarity'):
    """
    Draws a molecular network graph with node colors based on categorical labels and edge widths based on similarity.

    Args:
      G: NetworkX graph representing the molecular network.
      node_attribute: Name of the node attribute containing categorical labels (default: 'categorical_label').
      edge_attribute: Name of the edge attribute containing similarity (default: 'similarity').
    """

    # Extract unique categorical labels
    unique_labels = set(nx.get_node_attributes(G, node_attribute).values())
    num_labels = len(unique_labels)

    # Define a colormap
    colormap = plt.cm.get_cmap('nipy_spectral', num_labels)

    # Create a dictionary mapping labels to colors and a list of label names for legend
    color_map = {label: colormap(i) for i, label in enumerate(unique_labels)}
    label_names = list(color_map.keys())

    # Extract node colors based on categorical labels
    node_colors = [color_map[G.nodes[n][node_attribute]] for n in G.nodes]

    # Extract edge widths based on similarity
    edge_widths = [G[u][v][edge_attribute] for u, v in G.edges]

    # Draw the graph
    plt.figure(figsize=(8, 6))
    pos = nx.spring_layout(G, k=0.2)  # you can choose different layout algorithms

    # Draw nodes
    nx.draw_networkx_nodes(G, pos, node_color=node_colors, node_size=20, label=False)

    # Draw edges with widths corresponding to their weights
    for (u, v), width in zip(G.edges, edge_widths):
        nx.draw_networkx_edges(G, pos, edgelist=[(u, v)], width=width, edge_color='red', label=False)

    # Create legend entries with colored circles and labels
    legend_handles = [matplotlib.patches.Circle((0, 0), radius=0.4, color=color_map[label]) for label in label_names]
    plt.legend(legend_handles, label_names, loc='upper right', title='Class')  # Legend in upper right with title

    plt.title('Molecular Network')
    plt.show()
```

```py
>>> draw_graph_with_attributes(graph)
```
![net](https://github.com/Manas02/molecularnetwork/raw/main/net.png?raw=True)

## Contributing
If you find any issues or have suggestions for improvements, feel free to open an issue or submit a pull request. I welcome contributions from the community.

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