Chemical Structure Analysis Example

This example demonstrates how to use Kwasa-Kwasa’s chemistry extension to analyze molecular structures, perform chemical operations, and compare molecular similarities. It shows how the framework’s pattern-based approach can be applied to chemical structures.

Source Code

// Example of chemical molecule analysis using Turbulance

// Import the chemistry module
import chemistry

// Create molecules from SMILES strings
item ethanol = chemistry.Molecule.from_smiles("CCO", "ethanol").with_name("Ethanol")
item aspirin = chemistry.Molecule.from_smiles("CC(=O)OC1=CC=CC=C1C(=O)O", "aspirin").with_name("Aspirin")
item caffeine = chemistry.Molecule.from_smiles("CN1C=NC2=C1C(=O)N(C(=O)N2C)C", "caffeine").with_name("Caffeine")

// Function to analyze a molecule
funxn analyze_molecule(molecule):
    print("Analyzing molecule: {}".format(molecule.display()))
    print("SMILES: {}".format(molecule.smiles()))
    print("Formula: {}".format(molecule.formula()))
    print("Molecular weight: {:.2f}".format(molecule.molecular_weight()))
    
    // Display atom count
    print("Number of atoms: {}".format(len(molecule.atoms())))
    
    // Display bond count
    print("Number of bonds: {}".format(len(molecule.bonds())))
    
    // Calculate functional groups
    item functional_groups = identify_functional_groups(molecule)
    print("Functional groups:")
    for each group, count in functional_groups:
        print("  - {}: {}".format(group, count))

Code Explanation

1. Molecule Creation and Basic Analysis

item ethanol = chemistry.Molecule.from_smiles("CCO", "ethanol").with_name("Ethanol")

The example shows how to:

• Create molecules from SMILES notation
• Add metadata (names, IDs)
• Access basic molecular properties
• Calculate structural features

2. Functional Group Analysis

funxn identify_functional_groups(molecule):
    item groups = {}
    item smiles = molecule.smiles()
    
    // Check for common functional groups
    if smiles.contains("OH"):
        groups["hydroxyl"] = smiles.count("OH")
    // ... more group checks

Features:

• Identifies common functional groups
• Counts group occurrences
• Pattern-based structure analysis
• SMILES pattern matching

3. Mathematical Operations on Molecules

// Division: split by functional groups
item ethanol_parts = molecule_ops.divide(ethanol, "functional_group")

// Multiplication: chemical reaction
item reaction_product = molecule_ops.multiply(ethanol, aspirin)

// Addition: combine molecules
item mixture = molecule_ops.add(caffeine, aspirin)

// Subtraction: remove a functional group
item dehydroxylated = molecule_ops.subtract(ethanol, chemistry.Molecule.from_smiles("O", "hydroxyl"))

Demonstrates:

• Splitting molecules by groups
• Simulating chemical reactions
• Combining molecular structures
• Removing structural elements

4. Chemical Analysis Propositions

proposition ChemicalAnalysis:
    motion DrugLikeness("Molecules with specific properties can act as drugs")
    motion SolubilityPrediction("Polar functional groups increase water solubility")
    
    within caffeine:
        given contains_substructure("CN1C"):
            print("Contains N-methylated nitrogen - common in psychoactive compounds")

Shows how to:

• Define chemical properties
• Check structural features
• Make chemical predictions
• Analyze drug-like properties

5. Molecular Complexity and Similarity

funxn molecular_complexity(molecule):
    item complexity = 0
    // Calculate complexity based on structure
    
funxn find_similar(molecule, candidates, threshold=0.7):
    // Compare molecular similarities

Features:

• Complexity scoring
• Structural similarity comparison
• Functional group matching
• Threshold-based filtering

Running the Example

1. Save the code in a file with .turb extension
2. Run using the Kwasa-Kwasa interpreter:

   kwasa run chemistry_analysis.turb
   

Expected Output

Analyzing molecule: Ethanol
SMILES: CCO
Formula: C2H6O
Molecular weight: 46.07
Number of atoms: 9
Number of bonds: 8
Functional groups:
  - hydroxyl: 1

Analyzing molecule: Aspirin
SMILES: CC(=O)OC1=CC=CC=C1C(=O)O
Formula: C9H8O4
Molecular weight: 180.16
Functional groups:
  - carboxyl: 1
  - carbonyl: 2
  - aromatic: 1

Dividing molecules by functional groups:
Ethanol divided into 2 functional groups
Aspirin divided into 4 functional groups

Molecular complexity comparison:
Ethanol: 4.5
Aspirin: 28.5
Caffeine: 35.0

Molecules similar to aspirin:
Acetaminophen: 0.85 similarity
Ibuprofen: 0.78 similarity

Key Concepts Demonstrated

1. Molecular Representation:
   • SMILES notation
   • Structural formulas
   • Molecular properties
   • Chemical metadata
2. Chemical Analysis:
   • Functional group identification
   • Structure decomposition
   • Complexity calculation
   • Similarity comparison
3. Chemical Operations:
   • Molecular division
   • Chemical reactions
   • Structure combination
   • Group removal
4. Pattern Recognition:
   • Substructure matching
   • Functional group patterns
   • Structural motifs
   • Chemical fingerprints

Applications

1. Drug Discovery:
   • Structure-activity relationships
   • Drug-like property analysis
   • Molecular similarity search
   • Lead compound identification
2. Chemical Informatics:
   • Structure database searching
   • Molecular property prediction
   • Reaction pathway analysis
   • Structure visualization
3. Material Science:
   • Polymer analysis
   • Structure-property relationships
   • Material compatibility
   • Composite design