README.md

metadata

title: Crystallization Component Predictor
emoji: 🔬
colorFrom: blue
colorTo: purple
sdk: streamlit
sdk_version: 1.29.0
app_file: app.py
pinned: false
license: mit

🔬 Crystallization Component Predictor

An interactive machine learning application for predicting optimal protein crystallization components based on experimental parameters.

🎯 What Does This App Do?

This tool predicts three critical crystallization parameters:

1. Component Name: The chemical compound most likely to produce crystals
2. Concentration: The optimal molarity for the component
3. pH: The ideal acidity/basicity level for crystallization

🚀 Quick Start

1. Select a model (Advanced Baseline recommended)
2. Input your crystallization parameters:
   • Crystallization method
   • Temperature
   • pH
   • Matthews coefficient
   • Solvent content
3. Click "Predict Components"
4. Review predictions and download results

📊 Model Performance

Model	Name Accuracy	Conc R²	pH R²
Simple Baseline	61.12%	N/A	95.58%
Advanced Baseline ⭐	64.18%	47.33%	99.34%
Transformer	53.85%	18.72%	99.27%

Recommended: Advanced Baseline for best overall performance

🔬 Features

• Two Model Approaches: Choose between Simple and Advanced Baseline
• Interactive UI: Easy-to-use sliders and dropdowns
• Top-5 Predictions: View confidence scores for multiple candidates
• Visual pH Scale: Intuitive pH visualization
• Downloadable Results: Export predictions as CSV
• Performance Charts: Compare model accuracies

🛠️ Technical Details

Simple Baseline

• Random Forest for component classification
• XGBoost for pH regression
• 4 numerical features + TF-IDF of crystallization method

Advanced Baseline (Recommended)

• Ensemble of Random Forest, XGBoost, LightGBM, and CatBoost
• 8 engineered features including interaction terms
• Separate models for name, concentration, and pH
• Log-transformed concentration predictions

Models Included

• simple_baseline/: Simple baseline models

  • model_component_name.pkl: Component classifier
  • model_component_ph.pkl: pH regressor
  • label_encoder_name.pkl: Label encoder
  • scaler.pkl: Feature scaler
  • tfidf.pkl: TF-IDF vectorizer

• advanced_baseline/: Advanced baseline models

  • model_component_name.pkl: Enhanced component classifier
  • model_component_conc.pkl: Concentration regressor
  • model_component_ph.pkl: Enhanced pH regressor
  • label_encoder_name.pkl: Label encoder
  • scaler.pkl: Feature scaler
  • tfidf.pkl: TF-IDF vectorizer

📦 Dependencies

• Python 3.9+
• Streamlit
• Scikit-learn
• XGBoost
• LightGBM
• CatBoost
• Pandas
• NumPy
• Joblib

🎓 Use Cases

• Structural Biology: Plan crystallization experiments
• Drug Discovery: Optimize protein crystal conditions
• Research: Explore crystallization parameter space
• Education: Learn about protein crystallization

📖 Background

Protein crystallization is essential for determining 3D protein structures via X-ray crystallography. This tool uses machine learning trained on historical crystallization data from the Protein Data Bank (PDB) to predict optimal conditions.

Input Parameters Explained

• Crystallization Method: Technique used (vapor diffusion, batch, etc.)
• Temperature: Affects protein stability and crystal growth (typically 277-298K)
• pH: Critical for protein solubility and crystal formation (0-14 scale)
• Matthews Coefficient: Unit cell volume to protein molecular weight ratio (Ų/Da)
• Solvent Content: Percentage of solvent in crystal lattice (typically 30-70%)

⚠️ Important Notes

• Validation Required: Always validate predictions experimentally
• Research Tool: For research and educational purposes
• Starting Point: Use predictions as a guide, not absolute truth
• Protein-Specific: Results may vary based on your specific protein

🤝 Contributing

This is a research project. Feedback and suggestions are welcome!

📄 License

MIT License - Free to use for research and educational purposes

🙏 Acknowledgments

• Training data derived from Protein Data Bank (PDB)
• Built with Streamlit and ensemble ML models
• Inspired by advances in computational structural biology

📞 Contact & Support

For questions or issues, please open an issue on the repository.

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Note: This tool provides predictions based on historical data. Always conduct proper experimental validation. Crystallization is a complex process influenced by many factors not captured by these models alone.