ChEMBL Database

Overview

ChEMBL is a manually curated database of bioactive molecules maintained by the European Bioinformatics Institute (EBI), containing over 2 million compounds, 19 million bioactivity measurements, 13,000+ drug targets, and data on approved drugs and clinical candidates. 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

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 by searching by name:

targets = new_client.target.filter(pref_name__icontains='EGFR') target_id = targets[0]['target_chembl_id']

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

drugs = new_client.drug.filter()

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