ZINC Database API Reference

ZINC Database API Reference

Overview

Complete technical reference for programmatic access to the ZINC database, covering API endpoints, query syntax, parameters, response formats, and advanced usage patterns for ZINC22, ZINC20, and legacy versions.

Base URLs

ZINC22 (Current)

• CartBlanche22 API: https://cartblanche22.docking.org/
• File Repository: https://files.docking.org/zinc22/
• Main Website: https://zinc.docking.org/

ZINC20 (Maintained)

• API: https://zinc20.docking.org/
• File Repository: https://files.docking.org/zinc20/

Documentation

• Wiki: https://wiki.docking.org/
• GitHub: https://github.com/docking-org/

API Endpoints

1. Substance Retrieval by ZINC ID

Retrieve compound information using ZINC identifiers.

Endpoint: /substances.txt

Parameters:

• zinc_id (required): Single ZINC ID or comma-separated list
• output_fields (optional): Comma-separated field names (default: all fields)

URL Format:

https://cartblanche22.docking.org/substances.txt:zinc_id={ZINC_ID}&output_fields={FIELDS}

Examples:

Single compound:

curl "https://cartblanche22.docking.org/[email protected]_fields=zinc_id,smiles,catalogs"

Multiple compounds:

curl "https://cartblanche22.docking.org/substances.txt:zinc_id=ZINC000000000001,ZINC000000000002,ZINC000000000003&output_fields=zinc_id,smiles,tranche"

Batch retrieval from file:

# Create file with ZINC IDs (one per line or comma-separated)
curl -X POST "https://cartblanche22.docking.org/substances.txt?output_fields=zinc_id,smiles" \
  -F "zinc_id=@zinc_ids.txt"

Response Format (TSV):

zinc_id	smiles	catalogs
ZINC000000000001	CC(C)O	[vendor1,vendor2]
ZINC000000000002	c1ccccc1	[vendor3]

2. Structure Search by SMILES

Search for compounds by chemical structure with optional similarity thresholds.

Endpoint: /smiles.txt

Parameters:

• smiles (required): Query SMILES string (URL-encode if necessary)
• dist (optional): Tanimoto distance threshold (0-10, default: 0 = exact)
• adist (optional): Alternative distance metric (0-10, default: 0)
• output_fields (optional): Comma-separated field names

URL Format:

https://cartblanche22.docking.org/smiles.txt:smiles={SMILES}&dist={DIST}&adist={ADIST}&output_fields={FIELDS}

Examples:

Exact structure match:

curl "https://cartblanche22.docking.org/smiles.txt:smiles=c1ccccc1&output_fields=zinc_id,smiles"

Similarity search (Tanimoto distance = 3):

curl "https://cartblanche22.docking.org/smiles.txt:smiles=CC(C)Cc1ccc(cc1)C(C)C(=O)O&dist=3&output_fields=zinc_id,smiles,catalogs"

Broad similarity search:

curl "https://cartblanche22.docking.org/smiles.txt:smiles=c1ccccc1&dist=5&adist=5&output_fields=zinc_id,smiles,tranche"

URL-encoded SMILES (for special characters):

# Original: CC(=O)Oc1ccccc1C(=O)O
# Encoded: CC%28%3DO%29Oc1ccccc1C%28%3DO%29O
curl "https://cartblanche22.docking.org/smiles.txt:smiles=CC%28%3DO%29Oc1ccccc1C%28%3DO%29O&dist=2"

Distance Parameters Interpretation:

• dist=0: Exact match
• dist=1-3: Close analogs (high similarity)
• dist=4-6: Moderate analogs
• dist=7-10: Diverse chemical space

3. Supplier Code Search

Query compounds by vendor catalog numbers.

Endpoint: /catitems.txt

Parameters:

• catitem_id (required): Supplier catalog code
• output_fields (optional): Comma-separated field names

URL Format:

https://cartblanche22.docking.org/catitems.txt:catitem_id={SUPPLIER_CODE}&output_fields={FIELDS}

Example:

curl "https://cartblanche22.docking.org/catitems.txt:catitem_id=SUPPLIER-12345&output_fields=zinc_id,smiles,supplier_code,catalogs"

4. Random Compound Sampling

Generate random compound sets with optional filtering by chemical properties.

Endpoint: /substance/random.txt

Parameters:

• count (optional): Number of compounds to retrieve (default: 100, max: depends on server)
• subset (optional): Filter by predefined subset (e.g., 'lead-like', 'drug-like', 'fragment')
• output_fields (optional): Comma-separated field names

URL Format:

https://cartblanche22.docking.org/substance/random.txt:count={COUNT}&subset={SUBSET}&output_fields={FIELDS}

Examples:

Random 100 compounds (default):

curl "https://cartblanche22.docking.org/substance/random.txt"

Random lead-like molecules:

curl "https://cartblanche22.docking.org/substance/random.txt:count=1000&subset=lead-like&output_fields=zinc_id,smiles,tranche"

Random drug-like molecules:

curl "https://cartblanche22.docking.org/substance/random.txt:count=5000&subset=drug-like&output_fields=zinc_id,smiles"

Random fragments:

curl "https://cartblanche22.docking.org/substance/random.txt:count=500&subset=fragment&output_fields=zinc_id,smiles,tranche"

Subset Definitions:

• fragment: MW < 250, suitable for fragment-based drug discovery
• lead-like: MW 250-350, LogP ≤ 3.5, rotatable bonds ≤ 7
• drug-like: MW 350-500, follows Lipinski's Rule of Five
• lugs: Large, unusually good subset (highly curated)

Output Fields

Available Fields

Customize API responses using the output_fields parameter:

Note: Not all fields are available for all endpoints. Field availability depends on the database version and endpoint.

Default Fields

If output_fields is not specified, endpoints return all available fields in TSV format.

Custom Field Selection

Request specific fields only:

curl "https://cartblanche22.docking.org/[email protected]_fields=zinc_id,smiles"

Request multiple fields:

curl "https://cartblanche22.docking.org/[email protected]_fields=zinc_id,smiles,tranche,catalogs"

Tranche System

ZINC organizes compounds into tranches based on molecular properties for efficient filtering and organization.

Tranche Code Format

Pattern: H##P###M###-phase

Examples

Reactivity Phases

Parsing Tranches in Python

import re

def parse_tranche(tranche_str):
    """
    Parse ZINC tranche code.

    Args:
        tranche_str: Tranche code (e.g., "H05P035M400-0")

    Returns:
        dict with h_donors, logp, mw, phase
    """
    pattern = r'H(\d+)P(-?\d+)M(\d+)-(\d+)'
    match = re.match(pattern, tranche_str)

    if not match:
        return None

    return {
        'h_donors': int(match.group(1)),
        'logp': int(match.group(2)) / 10.0,
        'mw': int(match.group(3)),
        'phase': int(match.group(4))
    }

# Example usage
tranche = "H05P035M400-0"
props = parse_tranche(tranche)
print(props)  # {'h_donors': 5, 'logp': 3.5, 'mw': 400, 'phase': 0}

Filtering by Tranches

Download specific tranches from file repositories:

# Download all compounds in a specific tranche
wget https://files.docking.org/zinc22/H05/H05P035M400-0.db2.gz

File Repository Access

Directory Structure

ZINC22 3D structures are organized hierarchically by H-bond donors:

https://files.docking.org/zinc22/
├── H00/
│   ├── H00P010M200-0.db2.gz
│   ├── H00P020M250-0.db2.gz
│   └── ...
├── H01/
├── H02/
└── ...

File Formats

Downloading 3D Structures

Single tranche:

wget https://files.docking.org/zinc22/H05/H05P035M400-0.db2.gz

Multiple tranches (parallel download with aria2c):

# Create URL list
cat > tranche_urls.txt <<EOF
https://files.docking.org/zinc22/H05/H05P035M400-0.db2.gz
https://files.docking.org/zinc22/H05/H05P035M400-0.db2.gz
https://files.docking.org/zinc22/H05/H05P040M400-0.db2.gz
EOF

# Download in parallel
aria2c -i tranche_urls.txt -x 8 -j 4

Recursive download (use with caution - large data):

wget -r -np -nH --cut-dirs=1 -A "*.db2.gz" \
  https://files.docking.org/zinc22/H05/

Extracting Structures

# Decompress
gunzip H05P035M400-0.db2.gz

# Convert to other formats using OpenBabel
obabel H05P035M400-0.db2 -O output.sdf
obabel H05P035M400-0.db2 -O output.mol2

Advanced Query Patterns

Combining Multiple Search Criteria

Python wrapper for complex queries:

import subprocess
import pandas as pd
from io import StringIO

def advanced_zinc_search(smiles=None, zinc_ids=None, dist=0,
                         subset=None, count=None, output_fields=None):
    """
    Flexible ZINC search with multiple criteria.

    Args:
        smiles: SMILES string for structure search
        zinc_ids: List of ZINC IDs for batch retrieval
        dist: Distance parameter for similarity (0-10)
        subset: Subset filter (lead-like, drug-like, fragment)
        count: Number of random compounds
        output_fields: List of fields to return

    Returns:
        pandas DataFrame with results
    """
    if output_fields is None:
        output_fields = ['zinc_id', 'smiles', 'tranche', 'catalogs']

    fields_str = ','.join(output_fields)

    # Structure search
    if smiles:
        url = f"https://cartblanche22.docking.org/smiles.txt:smiles={smiles}&dist={dist}&output_fields={fields_str}"

    # Batch retrieval
    elif zinc_ids:
        zinc_ids_str = ','.join(zinc_ids)
        url = f"https://cartblanche22.docking.org/substances.txt:zinc_id={zinc_ids_str}&output_fields={fields_str}"

    # Random sampling
    elif count:
        url = f"https://cartblanche22.docking.org/substance/random.txt:count={count}&output_fields={fields_str}"
        if subset:
            url += f"&subset={subset}"

    else:
        raise ValueError("Must specify smiles, zinc_ids, or count")

    # Execute query
    result = subprocess.run(['curl', '-s', url],
                          capture_output=True, text=True)

    # Parse to DataFrame
    df = pd.read_csv(StringIO(result.stdout), sep='\t')

    return df

Usage examples:

# Find similar compounds
df = advanced_zinc_search(
    smiles="CC(C)Cc1ccc(cc1)C(C)C(=O)O",
    dist=3,
    output_fields=['zinc_id', 'smiles', 'catalogs']
)

# Batch retrieval
zinc_ids = ["ZINC000000000001", "ZINC000000000002"]
df = advanced_zinc_search(zinc_ids=zinc_ids)

# Random drug-like set
df = advanced_zinc_search(
    count=1000,
    subset='drug-like',
    output_fields=['zinc_id', 'smiles', 'tranche']
)

Property-Based Filtering

Filter compounds by molecular properties using tranche data:

def filter_by_properties(df, mw_range=None, logp_range=None,
                        max_hbd=None, phase=0):
    """
    Filter DataFrame by molecular properties.

    Args:
        df: DataFrame with 'tranche' column
        mw_range: Tuple (min_mw, max_mw)
        logp_range: Tuple (min_logp, max_logp)
        max_hbd: Maximum H-bond donors
        phase: Reactivity phase (0 = unreactive)

    Returns:
        Filtered DataFrame
    """
    # Parse tranches
    df['tranche_props'] = df['tranche'].apply(parse_tranche)
    df['mw'] = df['tranche_props'].apply(lambda x: x['mw'] if x else None)
    df['logp'] = df['tranche_props'].apply(lambda x: x['logp'] if x else None)
    df['hbd'] = df['tranche_props'].apply(lambda x: x['h_donors'] if x else None)
    df['phase'] = df['tranche_props'].apply(lambda x: x['phase'] if x else None)

    # Apply filters
    mask = pd.Series([True] * len(df))

    if mw_range:
        mask &= (df['mw'] >= mw_range[0]) & (df['mw'] <= mw_range[1])

    if logp_range:
        mask &= (df['logp'] >= logp_range[0]) & (df['logp'] <= logp_range[1])

    if max_hbd is not None:
        mask &= df['hbd'] <= max_hbd

    if phase is not None:
        mask &= df['phase'] == phase

    return df[mask]

# Example: Get drug-like compounds with specific properties
df = advanced_zinc_search(count=10000, subset='drug-like')
filtered = filter_by_properties(
    df,
    mw_range=(300, 450),
    logp_range=(1.0, 4.0),
    max_hbd=3,
    phase=0
)

Rate Limiting and Best Practices

Rate Limiting

ZINC does not publish explicit rate limits, but users should:

• Avoid rapid-fire requests: Space out queries by at least 1 second
• Use batch operations: Query multiple ZINC IDs in single request
• Cache results: Store frequently accessed data locally
• Off-peak usage: Perform large downloads during off-peak hours (UTC nights/weekends)

Etiquette

import time

def polite_zinc_query(query_func, *args, delay=1.0, **kwargs):
    """Wrapper to add delay between queries."""
    result = query_func(*args, **kwargs)
    time.sleep(delay)
    return result

Error Handling

def robust_zinc_query(url, max_retries=3, timeout=30):
    """
    Query ZINC with retry logic.

    Args:
        url: Full ZINC API URL
        max_retries: Maximum retry attempts
        timeout: Request timeout in seconds

    Returns:
        Query results or None on failure
    """
    import subprocess
    import time

    for attempt in range(max_retries):
        try:
            result = subprocess.run(
                ['curl', '-s', '--max-time', str(timeout), url],
                capture_output=True,
                text=True,
                check=True
            )

            # Check for empty or error responses
            if not result.stdout or 'error' in result.stdout.lower():
                raise ValueError("Invalid response")

            return result.stdout

        except (subprocess.CalledProcessError, ValueError) as e:
            if attempt < max_retries - 1:
                wait_time = 2 ** attempt  # Exponential backoff
                print(f"Retry {attempt + 1}/{max_retries} after {wait_time}s...")
                time.sleep(wait_time)
            else:
                print(f"Failed after {max_retries} attempts")
                return None

Integration with Molecular Docking

Preparing DOCK6 Libraries

# 1. Download tranche files
wget https://files.docking.org/zinc22/H05/H05P035M400-0.db2.gz

# 2. Decompress
gunzip H05P035M400-0.db2.gz

# 3. Use directly with DOCK6
dock6 -i dock.in -o dock.out -l H05P035M400-0.db2

AutoDock Vina Integration

# 1. Download MOL2 format
wget https://files.docking.org/zinc22/H05/H05P035M400-0.mol2.gz
gunzip H05P035M400-0.mol2.gz

# 2. Convert to PDBQT using prepare_ligand script
prepare_ligand4.py -l H05P035M400-0.mol2 -o ligands.pdbqt -A hydrogens

# 3. Run Vina
vina --receptor protein.pdbqt --ligand ligands.pdbqt \
     --center_x 25.0 --center_y 25.0 --center_z 25.0 \
     --size_x 20.0 --size_y 20.0 --size_z 20.0

RDKit Integration

from rdkit import Chem
from rdkit.Chem import AllChem, Descriptors
import pandas as pd

def process_zinc_results(zinc_df):
    """
    Process ZINC results with RDKit.

    Args:
        zinc_df: DataFrame with SMILES column

    Returns:
        DataFrame with calculated properties
    """
    # Convert SMILES to molecules
    zinc_df['mol'] = zinc_df['smiles'].apply(Chem.MolFromSmiles)

    # Calculate properties
    zinc_df['mw'] = zinc_df['mol'].apply(Descriptors.MolWt)
    zinc_df['logp'] = zinc_df['mol'].apply(Descriptors.MolLogP)
    zinc_df['hbd'] = zinc_df['mol'].apply(Descriptors.NumHDonors)
    zinc_df['hba'] = zinc_df['mol'].apply(Descriptors.NumHAcceptors)
    zinc_df['tpsa'] = zinc_df['mol'].apply(Descriptors.TPSA)
    zinc_df['rotatable'] = zinc_df['mol'].apply(Descriptors.NumRotatableBonds)

    # Generate 3D conformers
    for mol in zinc_df['mol']:
        if mol:
            AllChem.EmbedMolecule(mol, randomSeed=42)
            AllChem.MMFFOptimizeMolecule(mol)

    return zinc_df

# Save to SDF for docking
def save_to_sdf(zinc_df, output_file):
    """Save molecules to SDF file."""
    writer = Chem.SDWriter(output_file)
    for idx, row in zinc_df.iterrows():
        if row['mol']:
            row['mol'].SetProp('ZINC_ID', row['zinc_id'])
            writer.write(row['mol'])
    writer.close()

Troubleshooting

Common Issues

Issue: Empty or no results

• Solution: Check SMILES syntax, verify ZINC IDs exist, try broader similarity search

Issue: Timeout errors

• Solution: Reduce result count, use batch queries, try during off-peak hours

Issue: Invalid SMILES encoding

• Solution: URL-encode special characters (use urllib.parse.quote() in Python)

Issue: Tranche files not found

• Solution: Verify tranche code format, check file repository structure

Debug Mode

def debug_zinc_query(url):
    """Print query details for debugging."""
    print(f"Query URL: {url}")

    result = subprocess.run(['curl', '-v', url],
                          capture_output=True, text=True)

    print(f"Status: {result.returncode}")
    print(f"Stderr: {result.stderr}")
    print(f"Stdout length: {len(result.stdout)}")
    print(f"First 500 chars:\n{result.stdout[:500]}")

    return result.stdout

Version Differences

ZINC22 vs ZINC20 vs ZINC15

API Compatibility

Most query patterns work across versions, but URLs differ:

• ZINC22: cartblanche22.docking.org
• ZINC20: zinc20.docking.org
• ZINC15: zinc15.docking.org

Additional Resources

• ZINC Wiki: https://wiki.docking.org/
• ZINC22 Documentation: https://wiki.docking.org/index.php/Category:ZINC22
• ZINC API Guide: https://wiki.docking.org/index.php/ZINC_api
• File Access Guide: https://wiki.docking.org/index.php/ZINC22:Getting_started
• Publications:
  • ZINC22: J. Chem. Inf. Model. 2023
  • ZINC15: J. Chem. Inf. Model. 2020, 60, 6065-6073
• Support: Contact via ZINC website or GitHub issues