chembl-database（化学数据库）

ChEMBL Database

ChEMBL is the European Bioinformatics Institute's repository of bioactive compound data, containing over 2 million compounds, 19 million bioactivity measurements, and 13,000+ drug targets.

Use Cases

• Find potent inhibitors for a protein target
• Search for compounds similar to a known drug
• Retrieve drug mechanism of action data
• Filter compounds by molecular properties (Lipinski, etc.)
• Export bioactivity data for ML or analysis

Installation

uv pip install chembl_webresource_client

Basic Usage

from chembl_webresource_client.new_client import new_client

# Fetch compound by identifier
mol = new_client.molecule.get('CHEMBL192')

# Retrieve target data
tgt = new_client.target.get('CHEMBL203')

# Query activity measurements
acts = new_client.activity.filter(
    target_chembl_id='CHEMBL203',
    standard_type='IC50',
    standard_value__lte=50
)

Available Endpoints

Query Operators

The client uses Django-style filtering:

Common Workflows

Find Target Inhibitors

from chembl_webresource_client.new_client import new_client

activity = new_client.activity

# Get potent BRAF inhibitors (IC50 < 100 nM)
braf_hits = activity.filter(
    target_chembl_id='CHEMBL5145',
    standard_type='IC50',
    standard_value__lte=100,
    standard_units='nM'
)

for hit in braf_hits:
    print(f"{hit['molecule_chembl_id']}: {hit['standard_value']} nM")

Search by Target Name

from chembl_webresource_client.new_client import new_client

target = new_client.target
activity = new_client.activity

# Find CDK targets
cdk_targets = target.filter(
    pref_name__icontains='cyclin-dependent kinase',
    target_type='SINGLE PROTEIN'
)

target_ids = [t['target_chembl_id'] for t in cdk_targets]

# Get activities for these targets
cdk_activities = activity.filter(
    target_chembl_id__in=target_ids[:5],
    standard_type='IC50',
    standard_value__lte=100,
    standard_units='nM'
)

Structure Similarity Search

from chembl_webresource_client.new_client import new_client

sim = new_client.similarity

# Find molecules 80% similar to ibuprofen
ibuprofen_smiles = 'CC(C)Cc1ccc(cc1)C(C)C(=O)O'
matches = sim.filter(smiles=ibuprofen_smiles, similarity=80)

for m in matches:
    print(f"{m['molecule_chembl_id']}: {m['similarity']}%")

Substructure Search

from chembl_webresource_client.new_client import new_client

sub = new_client.substructure

# Find compounds with benzimidazole core
benzimidazole = 'c1ccc2[nH]cnc2c1'
compounds = sub.filter(smiles=benzimidazole)

Filter by Molecular Properties

from chembl_webresource_client.new_client import new_client

mol = new_client.molecule

# Lipinski-compliant fragments
fragments = mol.filter(
    molecule_properties__mw_freebase__lte=300,
    molecule_properties__alogp__lte=3,
    molecule_properties__hbd__lte=3,
    molecule_properties__hba__lte=3
)

Drug Mechanisms of Action

from chembl_webresource_client.new_client import new_client

mech = new_client.mechanism
drug_ind = new_client.drug_indication

# Get mechanism of metformin
metformin_id = 'CHEMBL1431'
mechanisms = mech.filter(molecule_chembl_id=metformin_id)

for m in mechanisms:
    print(f"Target: {m['target_chembl_id']}")
    print(f"Action: {m['action_type']}")

# Get approved indications
indications = drug_ind.filter(molecule_chembl_id=metformin_id)

Generate Molecule Images

from chembl_webresource_client.new_client import new_client

img = new_client.image

# Get SVG of caffeine
caffeine_svg = img.get('CHEMBL113')

with open('caffeine.svg', 'w') as f:
    f.write(caffeine_svg)

Key Response Fields

Molecule Properties

Activity Fields

Target Fields

Mechanism Fields

Export to DataFrame

import pandas as pd
from chembl_webresource_client.new_client import new_client

activity = new_client.activity

results = activity.filter(
    target_chembl_id='CHEMBL279',
    standard_type='Ki',
    pchembl_value__isnull=False
)

df = pd.DataFrame(list(results))
df.to_csv('dopamine_d2_ligands.csv', index=False)

Configuration

from chembl_webresource_client.settings import Settings

cfg = Settings.Instance()

cfg.CACHING = True           # Enable response caching
cfg.CACHE_EXPIRE = 43200     # Cache TTL (12 hours)
cfg.TIMEOUT = 60             # Request timeout
cfg.TOTAL_RETRIES = 5        # Retry attempts

Data Quality Notes

• ChEMBL data is manually curated but verify data_validity_comment fields
• Check potential_duplicate flags when aggregating results
• Use pchembl_value for normalized comparisons across assay types
• Activity values without standard_units should be used cautiously

Best Practices

1. Use caching - Reduces API load and improves performance
2. Filter early - Apply filters to reduce data transfer
3. Limit results - Use [:n] slicing for testing
4. Check validity - Inspect data_validity_comment fields
5. Use pchembl_value - Normalized values enable cross-assay comparison
6. Batch queries - Use __in operator for multiple IDs

Error Handling

from chembl_webresource_client.new_client import new_client

mol = new_client.molecule

try:
    result = mol.get('INVALID_ID')
except Exception as e:
    if '404' in str(e):
        print("Compound not found")
    elif '503' in str(e):
        print("Service unavailable - retry later")
    else:
        raise

External Links

• ChEMBL: https://www.ebi.ac.uk/chembl/
• API Documentation: https://chembl.gitbook.io/chembl-interface-documentation
• Python Client: https://github.com/chembl/chembl_webresource_client