alterlab-chembl

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

Scenario	Use Instead
Measured binding constants (Ki/Kd) from BindingDB, or its full TSV dump	`alterlab-bindingdb`
General compound lookup, name↔CID/SMILES conversion, PubChem BioAssay	`alterlab-pubchem`
Drug–drug interactions, drug labels, detailed pharmacology records	`alterlab-drugbank`
Purchasable compounds / docking-ready 3D libraries	`alterlab-zinc-db`
Computing descriptors or fingerprints locally from structures	`alterlab-rdkit`

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

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.

Query bioactivity data for that target:

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

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

Get drug information:

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

Retrieve mechanisms:

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

Find all bioactivities:

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

Workflow 3: Structure-Activity Relationship (SAR) Study

Find similar compounds:

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

Get activities for each compound:

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

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