Vectorize Molecules
The Vectorize Molecules module in ChemXploreML provides tools for converting molecular structures into numerical representations (embeddings) that can be used for machine learning and analysis.
Overview
The Vectorize Molecules interface offers:
Multiple Embedding Methods
- Molecular fingerprints
- Graph-based embeddings
- Learned representations
- Custom descriptors
Configuration Options
- Embedding type selection
- Parameter tuning
- Feature selection
- Output format options
Process Flow
The vectorization process includes:
Input Processing
- Molecular structure validation
- Feature extraction
- Data standardization
- Batch processing
Embedding Generation
- Fingerprint calculation
- Graph representation
- Feature vector creation
- Quality checks
Key Features
Embedding Methods
Molecular Fingerprints
- ECFP (Extended Connectivity Fingerprints)
- MACCS keys
- Atom pair fingerprints
- Topological fingerprints
Graph-based Embeddings
- Graph neural networks
- Message passing networks
- Graph convolutional networks
- Custom graph representations
Learned Representations
- Pre-trained models
- Transfer learning
- Fine-tuning options
- Custom training
Configuration Options
- Embedding dimension selection
- Feature importance analysis
- Parameter optimization
- Output format selection
Best Practices
Method Selection
- Consider data characteristics
- Account for downstream tasks
- Balance accuracy and speed
- Validate results
Parameter Tuning
- Optimize embedding dimensions
- Adjust algorithm parameters
- Validate performance
- Document settings
Quality Control
- Validate embeddings
- Check for information loss
- Monitor performance
- Track changes
Next Steps
After generating molecular embeddings, you can:
- Apply Dimensionality Reduction for visualization
- Use the embeddings for ML Training
- Perform Molecular Analysis
For more detailed information about specific embedding methods or configuration options, please refer to the respective documentation sections.