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Examples

This section provides practical examples of using AugChem for molecular data augmentation. Choose the appropriate section based on your data type and augmentation needs.

🧪 Quick Overview

AugChem supports two main types of molecular data augmentation:

  • 🔤 SMILES Augmentation: String-based molecular representation augmentation
  • 🔗 Graph Augmentation: Graph neural network-ready molecular graph augmentation

📋 Available Example Collections

SMILES Examples

Comprehensive examples for SMILES-based molecular augmentation including: - Basic SMILES manipulation techniques - Dataset-level augmentation strategies
- Quality control and validation - Real-world drug discovery applications - Integration with cheminformatics workflows

Graph Examples

Detailed examples for graph-based molecular augmentation including: - PyTorch Geometric integration - Individual augmentation techniques - Machine learning pipeline integration - Comparative analysis and visualization - Advanced pharmaceutical applications

🚀 Getting Started

If you're new to AugChem, we recommend:

  1. Start with Prerequisites: Install required packages
  2. Choose Your Data Type: SMILES strings or molecular graphs
  3. Follow Relevant Examples: Pick examples that match your use case
  4. Experiment: Modify parameters to suit your specific needs

Prerequisites

pip install augchem torch torch-geometric rdkit pandas matplotlib

Basic Usage Pattern

from augchem import Augmentator

# Initialize with reproducible seed
augmentator = Augmentator(seed=42)

# For SMILES data
smiles_result = augmentator.SMILES.augment_data(
    dataset="your_data.csv",
    augmentation_methods=["fusion", "enumeration"],
    augment_percentage=0.5
)

# For Graph data (when available)
# graph_result = augmentator.Graph.augment_dataset(...)

🎯 Example Categories

Beginner Examples

  • Basic augmentation setup
  • Single molecule processing
  • Simple dataset expansion

Intermediate Examples

  • Parameter optimization
  • Quality control implementation
  • Integration with ML pipelines

Advanced Examples

  • Custom augmentation strategies
  • Large-scale processing
  • Research-grade applications

💡 Tips for Using Examples

  1. Modify Parameters: Adjust augmentation rates based on your data
  2. Validate Results: Always check output quality
  3. Set Seeds: Use random seeds for reproducible experiments
  4. Start Small: Test with small datasets first
  5. Monitor Performance: Track augmentation impact on model performance

🔬 Real-World Applications

Our examples cover scenarios from: - Academic Research: Dataset expansion for publications - Drug Discovery: Virtual compound generation - Chemical Informatics: Property prediction enhancement - Materials Science: Novel structure exploration

📖 Additional Resources

Ready to augment your molecular data? Choose your examples and start exploring! 🧬✨