Query BindingDB for measured drug-target binding affinities (Ki, Kd, IC50, EC50). Search by target (UniProt ID), compound (SMILES/name), or pathogen. Essential for drug discovery, lead optimization, polypharmacology analysis, and structure-activity relationship (SAR) studies.
SKILL.md
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.
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
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:
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
import pandas as pd
def sar_analysis(df, target_uniprot, affinity_col="IC50 (nM)"):
"""
Structure-activity relationship analysis for a target.
Retrieves all compounds with affinity data and ranks by potency.
"""
target_data = query_target_affinity(df, target_uniprot, [affinity_col])
if target_data.empty:
return target_data
# Add pIC50 (negative log of IC50 in molar)
if affinity_col in target_data.columns:
target_data = target_data[target_data[affinity_col].notna()].copy()
target_data["pAffinity"] = -((target_data[affinity_col] * 1e-9).apply(
lambda x: __import__('math').log10(x)
))
target_data = target_data.sort_values("pAffinity", ascending=False)
return target_data
# Most potent compounds against EGFR (P00533)
# sar = sar_analysis(df, "P00533", "IC50 (nM)")
# print(sar.head(20))
6. Polypharmacology Profile
def polypharmacology_profile(df, ligand_smiles_or_name, affinity_cutoff_nM=1000):
"""
Find all targets a compound binds to.
Uses PubChem CID or SMILES for matching.
"""
# Search by ligand SMILES (exact match)
mask = df["Ligand SMILES"] == ligand_smiles_or_name
ligand_data = df[mask].copy()
# Filter by affinity
aff_cols = ["Ki (nM)", "IC50 (nM)", "Kd (nM)"]
has_aff = pd.Series(False, index=ligand_data.index)
for col in aff_cols:
if col in ligand_data.columns:
has_aff |= ligand_data[col] <= affinity_cutoff_nM
result = ligand_data[has_aff][
["Target Name", "UniProt (SwissProt) Primary ID of Target Chain"] + aff_cols
].dropna(how='all')
return result.sort_values("Ki (nM)")
Query Workflows
Workflow 1: Find Best Inhibitors for a Target
import pandas as pd
def find_best_inhibitors(uniprot_id, affinity_type="IC50 (nM)", top_n=20):
"""Find the most potent inhibitors for a target in BindingDB."""
df = load_bindingdb("BindingDB_All.tsv") # Load once and reuse
result = query_target_affinity(df, uniprot_id, [affinity_type])
if result.empty:
print(f"No data found for {uniprot_id}")
return result
result = result.sort_values(affinity_type).head(top_n)
print(f"Top {top_n} inhibitors for {uniprot_id} by {affinity_type}:")
for _, row in result.iterrows():
print(f" {row['PubChem CID']}: {row[affinity_type]:.1f} nM | SMILES: {row['Ligand SMILES'][:40]}...")
return result
Workflow 2: Selectivity Profiling
Get all affinity data for your compound across all targets
Compare affinity ratios between on-target and off-targets
Identify selectivity cliffs (structural changes that improve selectivity)
Cross-reference with ChEMBL for additional selectivity data
Workflow 3: Machine Learning Dataset Preparation
def prepare_ml_dataset(df, uniprot_ids, affinity_col="IC50 (nM)",
max_affinity_nM=100000, min_count=50):
"""Prepare BindingDB data for ML model training."""
records = []
for uid in uniprot_ids:
target_df = query_target_affinity(df, uid, [affinity_col], max_affinity_nM)
if len(target_df) >= min_count:
target_df = target_df.copy()
target_df["target"] = uid
records.append(target_df)
if not records:
return pd.DataFrame()
combined = pd.concat(records)
# Add pAffinity (normalized)
combined["pAffinity"] = -((combined[affinity_col] * 1e-9).apply(
lambda x: __import__('math').log10(max(x, 1e-12))
))
return combined[["Ligand SMILES", "target", "pAffinity", affinity_col]].dropna()
Key Data Fields
Field
Description
Ligand SMILES
2D structure of the compound
Ligand InChI Key
Unique chemical identifier
Ki (nM)
Inhibition constant (equilibrium, functional)
Kd (nM)
Dissociation constant (thermodynamic, binding)
IC50 (nM)
Half-maximal inhibitory concentration
EC50 (nM)
Half-maximal effective concentration
kon (M-1-s-1)
Association rate constant
koff (s-1)
Dissociation rate constant
UniProt (SwissProt) Primary ID
Target UniProt accession
Target Name
Protein name
PDB ID(s) for Ligand-Target Complex
Crystal structures
PubChem CID
PubChem compound ID
ChEMBL ID of Ligand
ChEMBL compound ID
Affinity Interpretation
Affinity
Classification
Drug-likeness
< 1 nM
Sub-nanomolar
Very potent (picomolar range)
1–10 nM
Nanomolar
Potent, typical for approved drugs
10–100 nM
Moderate
Common lead compounds
100–1000 nM
Weak
Fragment/starting point
> 1000 nM
Very weak
Generally below drug-relevance threshold
Best Practices
Use Ki for direct binding: Ki reflects true binding affinity independent of enzymatic mechanism
IC50 context-dependency: IC50 values depend on substrate concentration (Cheng-Prusoff equation)
Normalize units: BindingDB reports in nM; verify units when comparing across studies
Filter by target organism: Use Target Source Organism to ensure human protein data
Handle missing values: Not all compounds have all measurement types
Cross-reference with ChEMBL: ChEMBL has more curated activity data for medicinal chemistry
