// 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.
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
Time-blind friendly planning, executive function support, and daily structure for ADHD brains. Specializes in realistic time estimation, dopamine-aware task design, and building systems that actually work for neurodivergent minds.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
End-to-end binder design using BindCraft hallucination. Use this skill when: (1) Designing protein binders with built-in AF2 validation, (2) Running production-quality binder campaigns, (3) Using different design protocols (fast, default, slow), (4) Need joint backbone and sequence optimization, (5) Want high experimental success rate. For backbone-only generation, use rfdiffusion. For QC thresholds, use protein-qc. For tool selection guidance, use binder-design.
Guidance for choosing the right protein binder design tool. Use this skill when: (1) Deciding between BoltzGen, BindCraft, or RFdiffusion, (2) Planning a binder design campaign, (3) Understanding trade-offs between different approaches, (4) Selecting tools for specific target types. For specific tool parameters, use the individual tool skills (boltzgen, bindcraft, rfdiffusion, etc.).
Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO. Supports Clustal, PHYLIP, Stockholm, FASTA, Nexus, and other alignment formats for phylogenetics and conservation analysis. Use when reading, writing, or converting alignment file formats.
| name | bindingdb-database |
| description | 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. |
| license | CC-BY-3.0 |
| metadata | {"skill-author":"Kuan-lin Huang"} |
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:
Use BindingDB when:
Base URL: https://www.bindingdb.org/axis2/services/BDBService
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()
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)
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")
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])
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))
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)")
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
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()
| 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 | 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 |
Target Source Organism to ensure human protein data