bindingdb

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• SKILL.md
• affinity_queries.md · references/

SKILL.md (first lines)

# 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/rwd/bind/BindingDBRESTfulAPI.jsp
- Downloads: https://www.bindingdb.org/rwd/bind/chemsearch/marvin/SDFdownload.jsp?all_download=yes
- GitHub: https://github.com/dhimmel/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/rwd/bind/BindingDBRESTfulAPI.jsp`

```python
import requests

BASE_URL = "https://www.bindingdb.org/rwd/bind/BindingDBRESTfulAPI.jsp"

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/rwd/bind/chemsearch/marvin/SDFdownload.jsp?all_download=yes
    """
    # Key columns
    usecols = [
        "BindingDB Reactant_set_id",
        "Ligand SMILES",
        "Ligand InChI",
        "Ligand InChI Key",
        "BindingDB Target Chain  Sequence",

From LUNARTECH-X/superpowers, MIT.

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