Molecular Analysis

The Molecular Analysis module in ChemXploreML provides comprehensive tools for analyzing and visualizing molecular properties and relationships.

Overview

The Molecular Analysis interface provides:

1. Property Analysis

   • Molecular weight distribution
   • LogP analysis
   • H-bond donor/acceptor patterns
   • Rotatable bond analysis
   • Aromatic ring statistics

2. Structural Analysis

   • Substructure patterns
   • Functional group analysis
   • Ring system analysis
   • Stereochemistry assessment

3. Similarity Analysis

   • Molecular similarity calculations
   • Cluster analysis
   • Diversity assessment
   • Scaffold analysis

Key Features

Property Analysis

• Calculate and visualize key molecular properties
• Generate property distributions
• Identify property outliers
• Compare property patterns across datasets

Structural Analysis

• Identify common structural motifs
• Analyze molecular complexity
• Assess structural diversity
• Evaluate structural alerts

Similarity Analysis

• Calculate molecular similarity using various metrics
• Perform cluster analysis
• Visualize similarity networks
• Identify representative compounds

Usage Guidelines

1. Starting Analysis

   • Load your molecular dataset
   • Select the analysis type
   • Configure analysis parameters
   • Run the analysis

2. Interpreting Results

   • Review generated plots and statistics
   • Export analysis results
   • Save analysis configurations
   • Generate reports

Best Practices

1. Data Preparation

   • Ensure data quality
   • Standardize molecular representations
   • Remove duplicates
   • Validate structures

2. Analysis Selection

   • Choose appropriate analysis type
   • Consider dataset size
   • Account for computational resources
   • Plan for result interpretation

3. Result Management

   • Save analysis configurations
   • Export results in multiple formats
   • Document analysis parameters
   • Track analysis history

Next Steps

After completing molecular analysis, you can:

1. Proceed to Dimensionality Reduction for further data processing
2. Use the results for ML Training
3. Generate Molecular Embeddings

For more detailed information about specific analysis types, please refer to the respective documentation sections.