HBDB Compound Search

A Flask web application for searching and analyzing compound relationships in the HBDB database.

Prerequisites

• Python 3.10
• Docker and Docker Compose
• MySQL 8.0

Quick Start with Docker

1. Clone the repository

2. Create a .env file in the root directory with the following variables:

MYSQL_HOST=mysql
MYSQL_PORT=3306
MYSQL_USER=root
MYSQL_ROOT_PASSWORD=your_password
MYSQL_DATABASE=hbdb2

3. Place your hbdb2.sql file in the root directory

4. Build and run the containers:

docker-compose up --build

The application will be available at http://localhost:5020

Manual Setup

1. Create a Python virtual environment:

python -m venv venv
source venv/bin/activate  # Linux/Mac
venv\Scripts\activate     # Windows

2. Install dependencies:

pip install -r requirements.txt

3. Configure MySQL:

• Install MySQL 8.0
• Create a database named hbdb2
• Import the hbdb2.sql file

4. Set up environment variables:

• Create a .env file with the same variables as shown in the Docker setup
• Adjust MYSQL_HOST to localhost for local development

5. Run the application:

python app.py

Usage

1. Access the web interface at http://localhost:5020
2. Enter a compound ID in the search form
3. View the results showing:
   • Compound name
   • Related terms
   • Context information
   • Category classifications

Downloading Results

The application generates JSON files for each compound-concept relationship. These files are stored in the following structure:

{compound_name}/
    ├── concept_abnormality_metadata/
    ├── concept_chemical_metadata/
    ├── concept_molecular function_metadata/
    ├── concept_gene_metadata/
    ├── concept_location_metadata/
    └── concept_animal model_metadata/

Each JSON file follows the naming format:

{compound_name}_{term_B}_{paragraph}.json

To download the JSON files:

1. After searching for a compound, the files will be generated in the appropriate directories
2. Access the files directly from the project directory
3. Each JSON file contains:

   {
       "term_A": "compound name",
       "term_B": "related concept",
       "context": "surrounding text including the target sentence",
       "category": "concept category"
   }

Project Structure

• app.py: Main Flask application
• templates/: HTML templates
• docker-compose.yml: Docker services configuration
• Dockerfile: Web application container configuration
• requirements.txt: Python dependencies
• .env: Environment variables (create this)
• hbdb2.sql: Database schema and data (provide this)

Environment Variables

• MYSQL_HOST: MySQL server hostname
• MYSQL_PORT: MySQL server port
• MYSQL_USER: MySQL username
• MYSQL_ROOT_PASSWORD: MySQL root password
• MYSQL_DATABASE: Database name (hbdb2)