ChEMBL Database

Overview

ChEMBL is a manually curated database of bioactive molecules maintained by the European Bioinformatics Institute (EBI). Release ChEMBL 37 (May 2026) holds ~2.9 million distinct compounds, ~24.5 million bioactivity measurements, ~18,500 targets, and data on approved drugs and clinical candidates (live counts: https://www.ebi.ac.uk/chembl/api/data/status.json). Access and query this data programmatically using the ChEMBL Python client for drug discovery and medicinal chemistry research.

When to Use This Skill

This skill should be used when:

• Compound searches: Finding molecules by name, structure, or properties
• Target information: Retrieving data about proteins, enzymes, or biological targets
• Bioactivity data: Querying IC50, Ki, EC50, or other activity measurements
• Drug information: Looking up approved drugs, mechanisms, or indications
• Structure searches: Performing similarity or substructure searches
• Cheminformatics: Analyzing molecular properties and drug-likeness
• Target-ligand relationships: Exploring compound-target interactions
• Drug discovery: Identifying inhibitors, agonists, or bioactive molecules

Does NOT Trigger

Installation and Setup

Python Client

The ChEMBL Python client is required for programmatic access:

uv pip install chembl_webresource_client

Basic Usage Pattern

from chembl_webresource_client.new_client import new_client

# Access different endpoints
molecule = new_client.molecule
target = new_client.target
activity = new_client.activity
drug = new_client.drug

Core Capabilities

1. Molecule Queries

Retrieve by ChEMBL ID:

molecule = new_client.molecule
aspirin = molecule.get('CHEMBL25')

Search by name:

results = molecule.filter(pref_name__icontains='aspirin')

Filter by properties:

# Find small molecules (MW <= 500) with favorable LogP
results = molecule.filter(
    molecule_properties__mw_freebase__lte=500,
    molecule_properties__alogp__lte=5
)

2. Target Queries

Retrieve target information:

target = new_client.target
egfr = target.get('CHEMBL203')

Search for specific target types:

# Find all kinase targets
kinases = target.filter(
    target_type='SINGLE PROTEIN',
    pref_name__icontains='kinase'
)

3. Bioactivity Data

Query activities for a target:

activity = new_client.activity
# Find potent EGFR inhibitors
results = activity.filter(
    target_chembl_id='CHEMBL203',
    standard_type='IC50',
    standard_value__lte=100,
    standard_units='nM'
)

Get all activities for a compound:

compound_activities = activity.filter(
    molecule_chembl_id='CHEMBL25',
    pchembl_value__isnull=False
)

4. Structure-Based Searches

Similarity search:

similarity = new_client.similarity
# Find compounds similar to aspirin
similar = similarity.filter(
    smiles='CC(=O)Oc1ccccc1C(=O)O',
    similarity=85  # 85% similarity threshold
)

Substructure search:

substructure = new_client.substructure
# Find compounds containing benzene ring
results = substructure.filter(smiles='c1ccccc1')

5. Drug Information

Retrieve drug data:

drug = new_client.drug
drug_info = drug.get('CHEMBL25')

Get mechanisms of action:

mechanism = new_client.mechanism
mechanisms = mechanism.filter(molecule_chembl_id='CHEMBL25')

Query drug indications:

drug_indication = new_client.drug_indication
indications = drug_indication.filter(molecule_chembl_id='CHEMBL25')

Query Workflow

Workflow 1: Finding Inhibitors for a Target

1. Identify the target — most reliably by UniProt accession, because name

search is fuzzy (pref_name__icontains='EGFR' returns chimeras and protein–protein-interaction targets first, and CHEMBL203's pref_name is "Epidermal growth factor receptor", so gene symbols only match synonyms):

   targets = new_client.target.filter(
       target_components__accession='P00533',  # human EGFR
       target_type='SINGLE PROTEIN',
   )
   target_id = targets[0]['target_chembl_id']  # CHEMBL203
   # By symbol instead: target_synonym__icontains='EGFR' plus organism='Homo sapiens',
   # then check pref_name — substring matches also hit VEGFR1/2/3.

1. Query bioactivity data for that target:

   activities = new_client.activity.filter(
       target_chembl_id=target_id,
       standard_type='IC50',
       standard_value__lte=100
   )

1. Extract compound IDs and retrieve details:

   compound_ids = [act['molecule_chembl_id'] for act in activities]
   compounds = [new_client.molecule.get(cid) for cid in compound_ids]

Workflow 2: Analyzing a Known Drug

1. Get drug information:

   drug_info = new_client.drug.get('CHEMBL1234')

1. Retrieve mechanisms:

   mechanisms = new_client.mechanism.filter(molecule_chembl_id='CHEMBL1234')

1. Find all bioactivities:

   activities = new_client.activity.filter(molecule_chembl_id='CHEMBL1234')

Workflow 3: Structure-Activity Relationship (SAR) Study

1. Find similar compounds:

   similar = new_client.similarity.filter(smiles='query_smiles', similarity=80)

1. Get activities for each compound:

   for compound in similar:
       activities = new_client.activity.filter(
           molecule_chembl_id=compound['molecule_chembl_id']
       )

1. Analyze property-activity relationships using molecular properties from results.

Filter Operators

ChEMBL supports Django-style query filters:

• __exact - Exact match
• __iexact - Case-insensitive exact match
• __contains / __icontains - Substring matching
• __startswith / __endswith - Prefix/suffix matching
• __gt, __gte, __lt, __lte - Numeric comparisons
• __range - Value in range
• __in - Value in list
• __isnull - Null/not null check

Data Export and Analysis

Convert results to pandas DataFrame for analysis:

import pandas as pd

activities = new_client.activity.filter(target_chembl_id='CHEMBL203')
df = pd.DataFrame(list(activities))

# Analyze results
print(df['standard_value'].describe())
print(df.groupby('standard_type').size())

Performance Optimization

Caching

The client automatically caches results for 24 hours. Configure caching:

from chembl_webresource_client.settings import Settings

# Disable caching
Settings.Instance().CACHING = False

# Adjust cache expiration (seconds)
Settings.Instance().CACHE_EXPIRE = 86400

Lazy Evaluation

Queries execute only when data is accessed. Convert to list to force execution:

# Query is not executed yet
results = molecule.filter(pref_name__icontains='aspirin')

# Force execution
results_list = list(results)

Pagination

Results are paginated automatically. Iterate through all results:

for activity in new_client.activity.filter(target_chembl_id='CHEMBL203'):
    # Process each activity
    print(activity['molecule_chembl_id'])

Common Use Cases

Find Kinase Inhibitors

# Identify kinase targets
kinases = new_client.target.filter(
    target_type='SINGLE PROTEIN',
    pref_name__icontains='kinase'
)

# Get potent inhibitors
for kinase in kinases[:5]:  # First 5 kinases
    activities = new_client.activity.filter(
        target_chembl_id=kinase['target_chembl_id'],
        standard_type='IC50',
        standard_value__lte=50
    )

Explore Drug Repurposing

# Get approved drugs (the drug endpoint also lists clinical candidates,
# so filter on max_phase=4; ~3,000 approved of ~16,000 drug records)
drugs = new_client.drug.filter(max_phase=4)

# For each drug, find all targets
for drug in drugs[:10]:
    mechanisms = new_client.mechanism.filter(
        molecule_chembl_id=drug['molecule_chembl_id']
    )

Virtual Screening

# Find compounds with desired properties
candidates = new_client.molecule.filter(
    molecule_properties__mw_freebase__range=[300, 500],
    molecule_properties__alogp__lte=5,
    molecule_properties__hba__lte=10,
    molecule_properties__hbd__lte=5
)

Resources

scripts/example_queries.py

Ready-to-use Python functions demonstrating common ChEMBL query patterns:

• get_molecule_info() - Retrieve molecule details by ID
• search_molecules_by_name() - Name-based molecule search
• find_molecules_by_properties() - Property-based filtering
• get_bioactivity_data() - Query bioactivities for targets
• find_similar_compounds() - Similarity searching
• substructure_search() - Substructure matching
• get_drug_info() - Retrieve drug information
• find_kinase_inhibitors() - Specialized kinase inhibitor search
• export_to_dataframe() - Convert results to pandas DataFrame

Consult this script for implementation details and usage examples.

references/api_reference.md

Comprehensive API documentation including:

• Complete endpoint listing (molecule, target, activity, assay, drug, etc.)
• All filter operators and query patterns
• Molecular properties and bioactivity fields
• Advanced query examples
• Configuration and performance tuning
• Error handling and rate limiting

Refer to this document when detailed API information is needed or when troubleshooting queries.

Important Notes

Data Reliability

• ChEMBL data is manually curated but may contain inconsistencies
• Always check data_validity_comment field in activity records
• Be aware of potential_duplicate flags

Units and Standards

• Bioactivity values use standard units (nM, uM, etc.)
• pchembl_value provides normalized activity (-log scale)
• Check standard_type to understand measurement type (IC50, Ki, EC50, etc.)

Rate Limiting

• Respect ChEMBL's fair usage policies
• Use caching to minimize repeated requests
• Consider bulk downloads for large datasets
• Avoid hammering the API with rapid consecutive requests

Chemical Structure Formats

• SMILES strings are the primary structure format
• InChI keys available for compounds
• SVG images can be generated via the image endpoint

Additional Resources

• ChEMBL website: https://www.ebi.ac.uk/chembl/
• API documentation: https://www.ebi.ac.uk/chembl/api/data/docs
• Python client GitHub: https://github.com/chembl/chembl_webresource_client
• Interface documentation: https://chembl.gitbook.io/chembl-interface-documentation/
• Example notebooks: https://github.com/chembl/notebooks