chembl-database

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# 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

From Zaoqu-Liu/ScienceClaw, MIT.

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