Bio Substructure Search

Common SMARTS Patterns

Library Filtering

Goal: Filter a molecular library to retain only compounds containing (or lacking) a specific structural pattern.

Approach: Parse a SMARTS pattern and test each molecule for a substructure match, returning those that pass the inclusion or exclusion criterion.

from rdkit import Chem

def filter_by_substructure(molecules, smarts, exclude=False):
    '''
    Filter molecules by substructure presence/absence.

    Args:
        molecules: List of RDKit mol objects
        smarts: SMARTS pattern string
        exclude: If True, return molecules WITHOUT the pattern
    '''
    pattern = Chem.MolFromSmarts(smarts)
    if pattern is None:
        raise ValueError(f'Invalid SMARTS: {smarts}')

    filtered = []
    for mol in molecules:
        if mol is None:
            continue
        has_match = mol.HasSubstructMatch(pattern)
        if exclude:
            if not has_match:
                filtered.append(mol)
        else:
            if has_match:
                filtered.append(mol)

    return filtered

# Filter for amines
amines = filter_by_substructure(library, '[NX3;H2,H1,H0]')

# Exclude reactive groups
clean = filter_by_substructure(library, '[N+]([O-])=O', exclude=True)  # No nitro

Multiple Pattern Filtering

Goal: Apply multiple inclusion and exclusion substructure filters to narrow a compound set.

Approach: Sequentially apply SMARTS-based inclusion filters (must match all) then exclusion filters (must match none) to progressively narrow the library.

def filter_multiple_patterns(molecules, include_patterns=None, exclude_patterns=None):
    '''
    Filter by multiple inclusion and exclusion patterns.
    '''
    result = list(molecules)

    if include_patterns:
        for smarts in include_patterns:
            pattern = Chem.MolFromSmarts(smarts)
            result = [m for m in result if m and m.HasSubstructMatch(pattern)]

    if exclude_patterns:
        for smarts in exclude_patterns:
            pattern = Chem.MolFromSmarts(smarts)
            result = [m for m in result if m and not m.HasSubstructMatch(pattern)]

    return result

# Find compounds with both amine and carboxylic acid (amino acids)
amino_acids = filter_multiple_patterns(
    library,
    include_patterns=['[NX3;H2]', '[CX3](=O)[OX2H1]']
)

Atom Mapping

from rdkit import Chem

def get_substructure_atoms(mol, smarts):
    '''
    Get all atoms matching a pattern with their indices.
    '''
    pattern = Chem.MolFromSmarts(smarts)
    matches = mol.GetSubstructMatches(pattern)

    results = []
    for match in matches:
        atoms = [mol.GetAtomWithIdx(i) for i in match]
        results.append({
            'indices': match,
            'symbols': [a.GetSymbol() for a in atoms]
        })

    return results

# Find and characterize all aromatic rings
mol = Chem.MolFromSmiles('c1ccc2c(c1)cccc2')
rings = get_substructure_atoms(mol, 'c1ccccc1')
print(f'Found {len(rings)} aromatic 6-membered rings')

Recursive SMARTS

# Recursive SMARTS for complex patterns

# Phenyl attached to carbonyl
pattern = '[$(c1ccccc1C(=O))]'

# Ortho-substituted phenyl
ortho_pattern = '[$(c1ccc([*])cc1[*])]'

# Electron-withdrawing group on aromatic
ewg_aromatic = '[$(c[$(C(=O)),$(C#N),$(N(=O)=O)])]'

mol = Chem.MolFromSmiles('c1ccc(C(=O)O)cc1')
pattern = Chem.MolFromSmarts('[$(c1ccccc1C(=O))]')
print(mol.HasSubstructMatch(pattern))  # True

Visualization with Highlighting

from rdkit.Chem.Draw import rdMolDraw2D

def draw_with_highlights(mol, smarts, filename):
    '''Draw molecule with substructure highlighted.'''
    pattern = Chem.MolFromSmarts(smarts)
    match = mol.GetSubstructMatch(pattern)

    if not match:
        print('No match found')
        return

    drawer = rdMolDraw2D.MolDraw2DCairo(400, 300)
    drawer.DrawMolecule(mol, highlightAtoms=match)
    drawer.FinishDrawing()

    with open(filename, 'wb') as f:
        f.write(drawer.GetDrawingText())

# Highlight carboxylic acid
draw_with_highlights(mol, '[CX3](=O)[OX2H1]', 'highlighted.png')

Related Skills

• molecular-io - Load molecules for searching
• similarity-searching - Fingerprint-based searching
• admet-prediction - Filter before ADMET analysis