bindingdb-database

# BindingDB Database

## Overview

BindingDB (https://www.bindingdb.org/) is the primary public database of measured drug-protein binding affinities. It contains over 3 million binding data records for ~1.4 million compounds tested against ~9,200 protein targets, curated from scientific literature and patent literature. BindingDB stores quantitative binding measurements (Ki, Kd, IC50, EC50) essential for drug discovery, pharmacology, and computational chemistry research.

**Key resources:**
- BindingDB website: https://www.bindingdb.org/
- REST API: https://www.bindingdb.org/axis2/services/BDBService
- Downloads: https://www.bindingdb.org/bind/chemsearch/marvin/Download.jsp
- GitHub: https://github.com/drugilsberg/bindingdb

## When to Use This Skill

Use BindingDB when:

- **Target-based drug discovery**: What known compounds bind to a target protein? What are their affinities?
- **SAR analysis**: How do structural modifications affect binding affinity for a series of analogs?
- **Lead compound profiling**: What targets does a compound bind (selectivity/polypharmacology)?
- **Benchmark datasets**: Obtain curated protein-ligand affinity data for ML model training
- **Repurposing analysis**: Does an approved drug bind to an unintended target?
- **Competitive analysis**: What is the best reported affinity for a target class?
- **Fragment screening**: Find validated binding data for fragments against a target

## Core Capabilities

### 1. BindingDB REST API

Base URL: `https://www.bindingdb.org/axis2/services/BDBService`

```python
import requests

BASE_URL = "https://www.bindingdb.org/axis2/services/BDBService"

def bindingdb_query(method, params):
    """Query the BindingDB REST API."""
    url = f"{BASE_URL}/{method}"
    response = requests.get(url, params=params, headers={"Accept": "application/json"})
    response.raise_for_status()
    return response.json()
```

### 2. Query by Target (UniProt ID)

```python
def get_ligands_for_target(uniprot_id, affinity_type="Ki", cutoff=10000, unit="nM"):
    """
    Get all ligands with measured affinity for a UniProt target.

    Args:
        uniprot_id: UniProt accession (e.g., "P00519" for ABL1)
        affinity_type: "Ki", "Kd", "IC50", "EC50"
        cutoff: Maximum affinity value to return (in nM)
        unit: "nM" or "uM"
    """
    params = {
        "uniprot_id": uniprot_id,
        "affinity_type": affinity_type,
        "affinity_cutoff": cutoff,
        "response": "json"
    }
    return bindingdb_query("getLigandsByUniprotID", params)

# Example: Get all compounds binding ABL1 (imatinib target)
ligands = get_ligands_for_target("P00519", affinity_type="Ki", cutoff=100)
```

### 3. Query by Compound Name or SMILES

```python
def search_by_name(compound_name, limit=100):
    """Search BindingDB for compounds by name."""
    params = {
        "compound_name": compound_name,
        "response": "json",
        "max_results": limit
    }
    return bindingdb_query("getAffinitiesByCompoundName", params)

def search_by_smiles(smiles, similarity=100, limit=50):
    """
    Search BindingDB by SMILES string.

    Args:
        smiles: SMILES string of the compound
        similarity: Tanimoto similarity threshold (1-100, 100 = exact)
    """
    params = {
        "SMILES": smiles,
        "similarity": similarity,
        "response": "json",
        "max_results": limit
    }
    return bindingdb_query("getAffinitiesByBEI", params)

# Example: Search for imatinib binding data
result = search_by_name("imatinib")
```

### 4. Download-Based Analysis (Recommended for Large Queries)

For comprehensive analyses, download BindingDB data directly:

```python
import pandas as pd

def load_bindingdb(filepath="BindingDB_All.tsv"):
    """
    Load BindingDB TSV file.
    Download from: https://www.bindingdb.org/bind/chemsearch/marvin/Download.jsp
    """
    # Key columns
    usecols = [
        "BindingDB Reactant_set_id",
        "Ligand SMILES",
        "Ligand InChI",
        "Ligand InChI Key",
        "BindingDB Target Chain  Sequence",
        "PDB ID(s) for Ligand-Target Complex",
        "UniProt (SwissProt) Entry Name of Target Chain",
        "UniProt (SwissProt) Primary ID of Target Chain",
        "UniProt (TrEMBL) Primary ID of Target Chain",
        "Ki (nM)",
        "IC50 (nM)",
        "Kd (nM)",
        "EC50 (nM)",
        "kon (M-1-s-1)",
        "koff (s-1)",
        "Target Name",
        "Target Source Organism According to Curator or DataSource",
        "Number of Protein Chains in Target (>1 implies a multichain complex)",
        "PubChem CID",
        "PubChem SID",
        "ChEMBL ID of Ligand",
        "DrugBank ID of Ligand",
    ]

    df = pd.read_csv(filepath, sep="\t", usecols=[c for c in usecols if c],
                     low_memory=False, on_bad_lines='skip')

    # Convert affinity columns to numeric
    for col in ["Ki (nM)", "IC50 (nM)", "Kd (nM)", "EC50 (nM)"]:
        if col in df.columns:
            df[col] = pd.to_numeric(df[col], errors='coerce')

    return df

def query_target_affinity(df, uniprot_id, affinity_types=None, max_nm=10000):
    """Query loaded BindingDB for a specific target."""
    if affinity_types is None:
        affinity_types = ["Ki (nM)", "IC50 (nM)", "Kd (nM)"]

    # Filter by UniProt ID
    mask = df["UniProt (SwissProt) Primary ID of Target Chain"] == uniprot_id
    target_df = df[mask].copy()

    # Filter by affinity cutoff
    has_affinity = pd.Series(False, index=target_df.index)
    for col in affinity_types:
        if col in target_df.columns:
            has_affinity |= target_df[col] <= max_nm

    result = target_df[has_affinity][["Ligand SMILES"] + affinity_types +
                                      ["PubChem CID", "ChEMBL ID of Ligand"]].dropna(how='all')
    return result.sort_values(affinity_types[0])
```

### 5. SAR Analysis

```python
import pandas as pd

def sar_analysis(df, target_uniprot, affinity_col="IC50 (nM)"):
    """
    Structure-activity relati