GPL - Get Pharma Literature

A command-line tool to search PubMed for research papers authored by pharmaceutical and biotech company researchers. This tool filters out academic papers and focuses specifically on industry-affiliated research.

Features

• Search PubMed using natural language queries
• Filter papers to show only those with pharmaceutical/biotech company affiliations
• Display results in formatted tables or save to CSV
• Rate-limited API calls to respect PubMed usage guidelines
• Debug mode for troubleshooting
• Built with type annotations and modular architecture

Installation

Prerequisites

• Python 3.8 or higher
• Poetry (for dependency management)

Setup

1. Clone the repository:

git clone https://github.com/yourusername/gpl.git
cd gpl

2. Install dependencies using Poetry:

poetry install

3. Activate the virtual environment:

poetry shell

Usage

Basic Commands

Search for papers and display in terminal:

gpl "cancer therapy"

Save results to CSV file:

gpl "diabetes treatment" --file results.csv

Enable debug logging:

gpl "immunotherapy" --debug

Show help:

gpl --help

Command Options

• QUERY: Search term (required) - e.g., "cancer therapy", "gene editing"
• --file, -f: Save results to specified CSV file
• --debug, -d: Enable verbose debugging output
• --help, -h: Show help message and usage examples

Output Format

The tool provides the following information for each paper:

• PubMed ID: Unique identifier in PubMed database
• Title: Full paper title
• Publication Date: Formatted as "Jan 15 2024"
• Non-academic Author(s): Authors affiliated with companies
• Company Affiliation(s): Pharmaceutical/biotech company names
• Corresponding Author Email: Contact email (or "Email Unable to Retrieve")

How It Works

Architecture

The project is structured into two main modules:

1. Core Module (gpl/core.py):

   • PubMedSearcher: Main class for API interactions
   • PubMedPaper: Data structure for paper information
   • PubMedAPIError: Custom exception handling

2. CLI Module (gpl/cli.py):

   • Command-line interface using Click
   • Table formatting with tabulate
   • CSV export functionality
   • Progress indicators and error handling

Company Detection Logic

The tool uses keyword-based heuristics to identify company affiliations:

Company Keywords (case-insensitive):

• pharma, pharmaceutical, biotech, biotechnology
• inc., ltd., llc, corp., corporation
• therapeutics, biopharm, medicines, drugs
• laboratories, technologies, gmbh, plc

Excluded Academic Keywords:

• university, college, institute, school
• department, faculty, hospital, clinic
• medical center, foundation, society

API Integration

• Uses PubMed E-utilities API (free, no authentication required)
• Rate limited to 3 requests/second per NCBI guidelines
• Searches up to 50 most relevant papers per query
• Handles XML parsing for detailed paper information

Project Structure

gpl/
├── gpl/
│   ├── __init__.py          # Package initialization
│   ├── core.py              # Core PubMed API and filtering logic
│   └── cli.py               # Command-line interface
├── tests/                   # Test files (future)
├── pyproject.toml           # Poetry configuration and dependencies
├── README.md                # This file
└── LICENSE                  # MIT License

Development

Setting Up Development Environment

# Install development dependencies
poetry install --with dev

# Run type checking
poetry run mypy gpl/

# Run code formatting
poetry run black gpl/

# Run linting
poetry run flake8 gpl/

Testing

# Run tests (when implemented)
poetry run pytest

Publishing to TestPyPI

The project is structured to support publishing to TestPyPI:

1. Build the package:

poetry build

2. Configure TestPyPI (one-time setup):

poetry config repositories.testpypi https://test.pypi.org/legacy/
poetry config pypi-token.testpypi YOUR_TESTPYPI_TOKEN

3. Publish to TestPyPI:

poetry publish --repository testpypi

Dependencies

Production Dependencies

• requests: HTTP library for API calls
• click: Command-line interface framework
• tabulate: Table formatting for console output

Development Dependencies

• pytest: Testing framework
• black: Code formatting
• flake8: Code linting
• mypy: Static type checking
• types-requests, types-tabulate: Type stubs

Limitations and Future Improvements

Current Limitations

• Simplified XML parsing (production version should use proper XML parser)
• Basic keyword matching for company detection
• Limited to 50 papers per search
• Email extraction may not capture all corresponding authors

Planned Improvements

• More sophisticated affiliation parsing using NLP
• Configurable company keyword lists
• Better XML parsing with lxml or xml.etree.ElementTree
• Pagination support for larger result sets
• Caching to reduce API calls
• More comprehensive email extraction

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

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

Acknowledgments

• Uses NCBI's PubMed E-utilities API
• Built with assistance from Claude AI for initial structure and implementation
• Inspired by the need for industry-focused biomedical literature searches