chembl-database

# 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:

```bash
uv pip install chembl_webresource_client
```

### Basic Usage Pattern

```python
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:**
```python
molecule = new_client.molecule
aspirin = molecule.get('CHEMBL25')
```

**Search by name:**
```python
results = molecule.filter(pref_name__icontains='aspirin')
```

**Filter by properties:**
```python
# 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:**
```python
target = new_client.target
egfr = target.get('CHEMBL203')
```

**Search for specific target types:**
```python
# Find all kinase targets
kinases = target.filter(
    target_type='SINGLE PROTEIN',
    pref_name__icontains='kinase'
)
```

### 3. Bioactivity Data

**Query activities for a target:**
```python
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:**
```python
compound_activities = activity.filter(
    molecule_chembl_id='CHEMBL25',
    pchembl_value__isnull=False
)
```

### 4. Structure-Based Searches

**Similarity search:**
```python
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:**
```python
substructure = new_client.substructure
# Find compounds containing benzene ring
results = substructure.filter(smiles='c1ccccc1')
```

### 5. Drug Information

**Retrieve drug data:**
```python
drug = new_client.drug
drug_info = drug.get('CHEMBL25')
```

**Get mechanisms of action:**
```python
mechanism = new_client.mechanism
mechanisms = mechanism.filter(molecule_chembl_id='CHEMBL25')
```

**Query drug indications:**
```python
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** by searching by name:
   ```python
   targets = new_client.target.filter(pref_name__icontains='EGFR')
   target_id = targets[0]['target_chembl_id']
   ```

2. **Query bioactivity data** for that target:
   ```python
   activities = new_client.activity.filter(
       target_chembl_id=target_id,
       standard_type='IC50',
       standard_value__lte=100
   )
   ```

3. **Extract compound IDs** and retrieve details:
   ```python
   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**:
   ```python
   drug_info = new_client.drug.get('CHEMBL1234')
   ```

2. **Retrieve mechanisms**:
   ```python
   mechanisms = new_client.mechanism.filter(molecule_chembl_id='CHEMBL1234')
   ```

3. **Find all bioactivities**:
   ```python
   activities = new_client.activity.filter(molecule_chembl_id='CHEMBL1234')
   ```

### Workflow 3: Structure-Activity Relationship (SAR) Study

1. **Find similar compounds**:
   ```python
   similar = new_client.similarity.filter(smiles='query_smiles', similarity=80)
   ```

2. **Get activities for each compound**:
   ```python
   for compound in similar:
       activities = new_client.activity.filter(
           molecule_chembl_id=compound['molecule_chembl_id']
       )
   ```

3. **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:

```python
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:

```python
from chembl_webresource_client.settings import