Welcome to ChemGraphBuilder

The chemgraphbuilder package is a specialized tool designed for researchers in bioinformatics, cheminformatics, and computational biology. It enables the construction of detailed knowledge graphs that map the complex interactions between chemical compounds, genes, proteins, and bioassays. These knowledge graphs are critical for visualizing and understanding the multifaceted relationships in biochemical networks.

At its core, chemgraphbuilder integrates data from PubChem and utilize Neo4j, creating nodes that represent key entities such as compounds, genes, proteins, and bioassays. It further enriches these graphs by incorporating various relationships, including:

• BioAssay-Compound Relationships: Capturing the interactions where bioassays evaluate the effects of specific compounds. This is crucial for understanding the efficacy and mechanism of action of pharmaceuticals.
• BioAssay-Gene Relationships: Documenting instances where bioassays study specific genes, providing valuable data on gene function and regulation.
• Gene-Protein Relationships: Mapping the connections where genes encode proteins, providing insights into genetic regulation and protein function.
• Compound-Gene Interactions: Detailing how compounds interact with genes, categorized into various interaction types such as Inhibitors, Inactivators, Activators, Inducers, Substrates, Ligands, and Inactive compounds. This classification aids in understanding gene regulation, drug mechanisms, and potential therapeutic applications.
• Compound Similarities: Highlighting chemical similarities between compounds, which can suggest similar biological activities or shared molecular properties.
• Compound Co-occurrence: Identifying instances where compounds or genes co-occur in scientific literature, indicating potential interactions or co-regulation.

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The chemgraphbuilder package is versatile and can be utilized both in Python code and via the command line interface. For practical examples and use cases, please refer to the "Usage Examples" section available in the menu on the left sidebar. For comprehensive documentation of the main classes and their functionalities, visit the Documentation Page.

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Neo4j Requirements

chemgraphbuilder requires a running Neo4j database that is accessible via Bolt URI, username, and password.

You can run Neo4j: - Locally (Neo4j Desktop or Docker) - Remotely (Neo4j Aura Cloud)

Default Bolt port: 7687
Default Web UI port: 7474

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🚀 Quick Start

Follow these steps to get up and running with chemgraphbuilder and Neo4j in under 5 minutes.

1️⃣ Install and Run Neo4j

Option A – Docker (fastest)

docker run \
  --name neo4j \
  -p 7474:7474 -p 7687:7687 \
  -e NEO4J_AUTH=neo4j/testpassword \
  neo4j:5.14

• Bolt URI: bolt://localhost:7687
• Username: neo4j
• Password: testpassword
• Web UI: http://localhost:7474

Option B – Neo4j Desktop

1. Download from: https://neo4j.com/download/
2. Create a new project and database.
3. Note the Bolt URI, username, and password.

2️⃣ Install chemgraphbuilder

pip install chemgraphbuilder

Or visit the PyPI Project Page for the latest release.

3️⃣ Connect to Neo4j in Python

from chemgraphbuilder import Neo4jBase

# Connection details
NEO4J_URI = "bolt://localhost:7687"
NEO4J_USER = "neo4j"
NEO4J_PASSWORD = "testpassword"

# Connect and test
db = Neo4jBase(uri=NEO4J_URI, user=NEO4J_USER, password=NEO4J_PASSWORD)
db.test_connection()

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4️⃣ First Example

# Create a simple test node
db.run_query("CREATE (:Test {name: 'Hello Neo4j'})")
print("Node created!")

Check in the Neo4j Browser:

MATCH (n) RETURN n;

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Project layout

chemgraphbuilder/                 # Main source code directory for the chemgraphbuilder package.
build/
    lib/chemgraphbuilder/         # Build artifacts and main package files.
chemgraphbuilder.egg-info/        # Package metadata and distribution information.
dist/                             # Distribution packages (.tar.gz, .whl).
docs/                             # Documentation files for the project.
    index.md                      # Documentation homepage.
    ...                           # Other markdown pages, images, and files.
examples/                         # Example scripts and usage demonstrations.
__pycache__/                      # Compiled Python bytecode files.
.gitignore                        # Specifies files and directories to be ignored by Git.
LICENSE                           # License file detailing the terms of use.
README.md                         # Project overview and instructions.
mkdocs.yml                        # Configuration file for MkDocs.
requirements.txt                  # List of Python dependencies for the project.
setup.py                          # Setup script for packaging and distribution.

Gallery

Welcome to the gallery. Here are some screenshots from the Knowledge Graph built using this package:

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