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Command-line interface for computational biology and drug discovery

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Project description

🌿 ivybloom CLI

Command-line interface for ivy biosciences' computational biology and drug discovery platform

PyPI version Python 3.8+ License: MIT Documentation

Accelerate your computational biology research with powerful command-line tools for protein structure prediction, drug discovery, ADMET analysis, and workflow automation.

🚀 Quick Start

Installation

pip install ivybloom

Authentication

# Browser-based login (recommended)
ivybloom auth login --browser

Your First Job

# Predict protein structure
ivybloom run esmfold protein_sequence=MKLLVLGLVGFGVGFGVGFGVGFGVGFGVGFG

# Monitor progress
ivybloom jobs list

✨ Key Features

🧬 Computational Biology Tools

  • Protein Structure Prediction: ESMFold, AlphaFold integration
  • Drug Discovery: REINVENT, fragment-based design
  • ADMET Analysis: Comprehensive property prediction
  • Molecular Analysis: Solubility, toxicity, bioavailability

🔗 Advanced Workflows

  • Job Chaining: Link multiple analyses seamlessly
  • Parallel Execution: Run multiple optimizations simultaneously
  • Parameter Passing: Results flow between pipeline stages
  • YAML Workflows: Define complex multi-step processes

🎨 Beautiful Interface

  • Earth-Tone Design: Professional, biology-inspired color scheme
  • Rich Formatting: Progress bars, tables, status indicators
  • Multiple Formats: JSON, YAML, CSV, table output
  • Real-Time Monitoring: Live job progress tracking

🔐 Enterprise Authentication

  • Browser OAuth: "Click here to login" experience
  • Device Flow: Perfect for headless environments
  • API Keys: Traditional authentication for automation
  • Secure Storage: System keyring integration

📊 Platform Integration

The IvyBloom CLI seamlessly integrates with your existing workflow:

  • Shared Database: Jobs appear in both CLI and web interface
  • Project Access: Full project management capabilities
  • Account Management: Usage tracking and limits
  • Cross-Platform: macOS, Linux, Windows support

🔬 Research Use Cases

Drug Discovery Pipeline

ivybloom workflows run protein_to_drug_pipeline.yaml \
    --input protein_sequence=MKLLVL... \
    --project-id drug-discovery-project

Fragment-Based Design

ivybloom workflows run fragment_based_discovery.yaml \
    --input target_protein=structure.pdb \
    --parallel

High-Throughput Screening

ivybloom workflows run virtual_screening.yaml \
    --input compound_library=compounds.sdf \
    --batch-size 10000

📚 Documentation

🛠 Development

Local Installation

git clone https://github.com/ivybiosciences/ivybloom-cli.git
cd ivybloom-cli
pip install -e .

Testing

pip install -e ".[dev]"
pytest tests/

🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

📄 License

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

🆘 Support


🌿 Computational Biology & Drug Discovery at Your Fingertips

Built with ❤️ by Ivy Biosciences

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