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skills/chembl-database/scripts/example_queries.py

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+#!/usr/bin/env python3
+"""
+ChEMBL Database Query Examples
+
+This script demonstrates common query patterns for the ChEMBL database
+using the chembl_webresource_client Python library.
+
+Requirements:
+    pip install chembl_webresource_client
+    pip install pandas (optional, for data manipulation)
+"""
+
+from chembl_webresource_client.new_client import new_client
+
+
+def get_molecule_info(chembl_id):
+    """
+    Retrieve detailed information about a molecule by ChEMBL ID.
+
+    Args:
+        chembl_id: ChEMBL identifier (e.g., 'CHEMBL25')
+
+    Returns:
+        Dictionary containing molecule information
+    """
+    molecule = new_client.molecule
+    return molecule.get(chembl_id)
+
+
+def search_molecules_by_name(name_pattern):
+    """
+    Search for molecules by name pattern.
+
+    Args:
+        name_pattern: Name or pattern to search for
+
+    Returns:
+        List of matching molecules
+    """
+    molecule = new_client.molecule
+    results = molecule.filter(pref_name__icontains=name_pattern)
+    return list(results)
+
+
+def find_molecules_by_properties(max_mw=500, min_logp=None, max_logp=None):
+    """
+    Find molecules based on physicochemical properties.
+
+    Args:
+        max_mw: Maximum molecular weight
+        min_logp: Minimum LogP value
+        max_logp: Maximum LogP value
+
+    Returns:
+        List of matching molecules
+    """
+    molecule = new_client.molecule
+
+    filters = {
+        'molecule_properties__mw_freebase__lte': max_mw
+    }
+
+    if min_logp is not None:
+        filters['molecule_properties__alogp__gte'] = min_logp
+    if max_logp is not None:
+        filters['molecule_properties__alogp__lte'] = max_logp
+
+    results = molecule.filter(**filters)
+    return list(results)
+
+
+def get_target_info(target_chembl_id):
+    """
+    Retrieve information about a biological target.
+
+    Args:
+        target_chembl_id: ChEMBL target identifier (e.g., 'CHEMBL240')
+
+    Returns:
+        Dictionary containing target information
+    """
+    target = new_client.target
+    return target.get(target_chembl_id)
+
+
+def search_targets_by_name(target_name):
+    """
+    Search for targets by name or keyword.
+
+    Args:
+        target_name: Target name or keyword (e.g., 'kinase', 'EGFR')
+
+    Returns:
+        List of matching targets
+    """
+    target = new_client.target
+    results = target.filter(
+        target_type='SINGLE PROTEIN',
+        pref_name__icontains=target_name
+    )
+    return list(results)
+
+
+def get_bioactivity_data(target_chembl_id, activity_type='IC50', max_value=100):
+    """
+    Retrieve bioactivity data for a specific target.
+
+    Args:
+        target_chembl_id: ChEMBL target identifier
+        activity_type: Type of activity (IC50, Ki, EC50, etc.)
+        max_value: Maximum activity value in nM
+
+    Returns:
+        List of activity records
+    """
+    activity = new_client.activity
+    results = activity.filter(
+        target_chembl_id=target_chembl_id,
+        standard_type=activity_type,
+        standard_value__lte=max_value,
+        standard_units='nM'
+    )
+    return list(results)
+
+
+def find_similar_compounds(smiles, similarity_threshold=85):
+    """
+    Find compounds similar to a query structure.
+
+    Args:
+        smiles: SMILES string of query molecule
+        similarity_threshold: Minimum similarity percentage (0-100)
+
+    Returns:
+        List of similar compounds
+    """
+    similarity = new_client.similarity
+    results = similarity.filter(
+        smiles=smiles,
+        similarity=similarity_threshold
+    )
+    return list(results)
+
+
+def substructure_search(smiles):
+    """
+    Search for compounds containing a specific substructure.
+
+    Args:
+        smiles: SMILES string of substructure
+
+    Returns:
+        List of compounds containing the substructure
+    """
+    substructure = new_client.substructure
+    results = substructure.filter(smiles=smiles)
+    return list(results)
+
+
+def get_drug_info(molecule_chembl_id):
+    """
+    Retrieve drug information including indications and mechanisms.
+
+    Args:
+        molecule_chembl_id: ChEMBL molecule identifier
+
+    Returns:
+        Tuple of (drug_info, mechanisms, indications)
+    """
+    drug = new_client.drug
+    mechanism = new_client.mechanism
+    drug_indication = new_client.drug_indication
+
+    try:
+        drug_info = drug.get(molecule_chembl_id)
+    except:
+        drug_info = None
+
+    mechanisms = list(mechanism.filter(molecule_chembl_id=molecule_chembl_id))
+    indications = list(drug_indication.filter(molecule_chembl_id=molecule_chembl_id))
+
+    return drug_info, mechanisms, indications
+
+
+def find_kinase_inhibitors(max_ic50=100):
+    """
+    Find potent kinase inhibitors.
+
+    Args:
+        max_ic50: Maximum IC50 value in nM
+
+    Returns:
+        List of kinase inhibitor activities
+    """
+    target = new_client.target
+    activity = new_client.activity
+
+    # Find kinase targets
+    kinase_targets = target.filter(
+        target_type='SINGLE PROTEIN',
+        pref_name__icontains='kinase'
+    )
+
+    # Get target IDs
+    target_ids = [t['target_chembl_id'] for t in kinase_targets[:10]]  # Limit to first 10
+
+    # Find activities
+    results = activity.filter(
+        target_chembl_id__in=target_ids,
+        standard_type='IC50',
+        standard_value__lte=max_ic50,
+        standard_units='nM'
+    )
+
+    return list(results)
+
+
+def get_compound_bioactivities(molecule_chembl_id):
+    """
+    Get all bioactivity data for a specific compound.
+
+    Args:
+        molecule_chembl_id: ChEMBL molecule identifier
+
+    Returns:
+        List of all activity records for the compound
+    """
+    activity = new_client.activity
+    results = activity.filter(
+        molecule_chembl_id=molecule_chembl_id,
+        pchembl_value__isnull=False
+    )
+    return list(results)
+
+
+def export_to_dataframe(data):
+    """
+    Convert ChEMBL data to pandas DataFrame (requires pandas).
+
+    Args:
+        data: List of ChEMBL records
+
+    Returns:
+        pandas DataFrame
+    """
+    try:
+        import pandas as pd
+        return pd.DataFrame(data)
+    except ImportError:
+        print("pandas not installed. Install with: pip install pandas")
+        return None
+
+
+# Example usage
+if __name__ == "__main__":
+    print("ChEMBL Database Query Examples")
+    print("=" * 50)
+
+    # Example 1: Get information about aspirin
+    print("\n1. Getting information about aspirin (CHEMBL25)...")
+    aspirin = get_molecule_info('CHEMBL25')
+    print(f"Name: {aspirin.get('pref_name')}")
+    print(f"Formula: {aspirin.get('molecule_properties', {}).get('full_molformula')}")
+
+    # Example 2: Search for EGFR inhibitors
+    print("\n2. Searching for EGFR targets...")
+    egfr_targets = search_targets_by_name('EGFR')
+    if egfr_targets:
+        print(f"Found {len(egfr_targets)} EGFR-related targets")
+        print(f"First target: {egfr_targets[0]['pref_name']}")
+
+    # Example 3: Find potent activities for a target
+    print("\n3. Finding potent compounds for EGFR (CHEMBL203)...")
+    activities = get_bioactivity_data('CHEMBL203', 'IC50', max_value=10)
+    print(f"Found {len(activities)} compounds with IC50 <= 10 nM")
+
+    print("\n" + "=" * 50)
+    print("Examples completed successfully!")