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ZINC Database

Overview

ZINC is a freely accessible repository of 230M+ purchasable compounds maintained by UCSF. Search by ZINC ID or SMILES, perform similarity searches, download 3D-ready structures for docking, discover analogs for virtual screening and drug discovery.

When to Use This Skill

This skill should be used when:

• Virtual screening : Finding compounds for molecular docking studies
• Lead discovery : Identifying commercially-available compounds for drug development
• Structure searches : Performing similarity or analog searches by SMILES
• Compound retrieval : Looking up molecules by ZINC IDs or supplier codes
• Chemical space exploration : Exploring purchasable chemical diversity
• Docking studies : Accessing 3D-ready molecular structures
• Analog searches : Finding similar compounds based on structural similarity
• Supplier queries : Identifying compounds from specific chemical vendors
• Random sampling : Obtaining random compound sets for screening

Database Versions

ZINC has evolved through multiple versions:

• ZINC22 (Current): Largest version with 230+ million purchasable compounds and multi-billion scale make-on-demand compounds
• ZINC20 : Still maintained, focused on lead-like and drug-like compounds
• ZINC15 : Predecessor version, legacy but still documented

This skill primarily focuses on ZINC22, the most current and comprehensive version.

Access Methods

Web Interface

Primary access point: https://zinc.docking.org/ Interactive searching: https://cartblanche22.docking.org/

API Access

All ZINC22 searches can be performed programmatically via the CartBlanche22 API:

Base URL : https://cartblanche22.docking.org/

All API endpoints return data in text or JSON format with customizable fields.

Core Capabilities

1. Search by ZINC ID

Retrieve specific compounds using their ZINC identifiers.

Web interface : https://cartblanche22.docking.org/search/zincid

API endpoint :

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

Multiple IDs :

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

Response fields : zinc_id, smiles, sub_id, supplier_code, catalogs, tranche (includes H-count, LogP, MW, phase)

2. Search by SMILES

Find compounds by chemical structure using SMILES notation, with optional distance parameters for analog searching.

Web interface : https://cartblanche22.docking.org/search/smiles

API endpoint :

curl "https://cartblanche22.docking.org/[email protected]=4-Fadist=4"

Parameters :

• smiles: Query SMILES string (URL-encoded if necessary)
• dist: Tanimoto distance threshold (default: 0 for exact match)
• adist: Alternative distance parameter for broader searches (default: 0)
• output_fields: Comma-separated list of desired output fields

Example - Exact match :

curl "https://cartblanche22.docking.org/smiles.txt:smiles=c1ccccc1"

Example - Similarity search :

curl "https://cartblanche22.docking.org/smiles.txt:smiles=c1ccccc1&dist=3&output_fields=zinc_id,smiles,tranche"

3. Search by Supplier Codes

Query compounds from specific chemical suppliers or retrieve all molecules from particular catalogs.

Web interface : https://cartblanche22.docking.org/search/catitems

API endpoint :

curl "https://cartblanche22.docking.org/catitems.txt:catitem_id=SUPPLIER-CODE-123"

Use cases :

• Verify compound availability from specific vendors
• Retrieve all compounds from a catalog
• Cross-reference supplier codes with ZINC IDs

4. Random Compound Sampling

Generate random compound sets for screening or benchmarking purposes.

Web interface : https://cartblanche22.docking.org/search/random

API endpoint :

curl "https://cartblanche22.docking.org/substance/random.txt:count=100"

Parameters :

• count: Number of random compounds to retrieve (default: 100)
• subset: Filter by subset (e.g., 'lead-like', 'drug-like', 'fragment')
• output_fields: Customize returned data fields

Example - Random lead-like molecules :

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

Common Workflows

Workflow 1: Preparing a Docking Library

1. Define search criteria based on target properties or desired chemical space

2. Query ZINC22 using appropriate search method:

   Example: Get drug-like compounds with specific LogP and MW

curl "https://cartblanche22.docking.org/substance/random.txt:count=10000&subset=drug-like&output_fields=zinc_id,smiles,tranche" > docking_library.txt

1. Parse results to extract ZINC IDs and SMILES:

   import pandas as pd

# Load results

df = pd.read_csv('docking_library.txt', sep='\t')

# Filter by properties in tranche data
# Tranche format: H##P###M###-phase
# H = H-bond donors, P = LogP*10, M = MW

4. Download 3D structures for docking using ZINC ID or download from file repositories

Workflow 2: Finding Analogs of a Hit Compound

1. Obtain SMILES of the hit compound:

   hit_smiles = "CC(C)Cc1ccc(cc1)C(C)C(=O)O" # Example: Ibuprofen

2. Perform similarity search with distance threshold:

   curl "https://cartblanche22.docking.org/smiles.txt:smiles=CC(C)Cc1ccc(cc1)C(C)C(=O)O&dist=5&output_fields=zinc_id,smiles,catalogs" > analogs.txt

3. Analyze results to identify purchasable analogs:

   import pandas as pd

analogs = pd.read_csv('analogs.txt', sep='\t')

print(f"Found {len(analogs)} analogs")
print(analogs[['zinc_id', 'smiles', 'catalogs']].head(10))

4. Retrieve 3D structures for the most promising analogs

Workflow 3: Batch Compound Retrieval

1. Compile list of ZINC IDs from literature, databases, or previous screens:

   zinc_ids = [ "ZINC000000000001", "ZINC000000000002", "ZINC000000000003" ] zinc_ids_str = ",".join(zinc_ids)

2. Query ZINC22 API :

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

3. Process results for downstream analysis or purchasing

Workflow 4: Chemical Space Sampling

1. Select subset parameters based on screening goals:

   • Fragment: MW < 250, good for fragment-based drug discovery
   • Lead-like: MW 250-350, LogP ≤ 3.5
   • Drug-like: MW 350-500, follows Lipinski's Rule of Five

2. Generate random sample :

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

3. Analyze chemical diversity and prepare for virtual screening

Output Fields

Customize API responses with the output_fields parameter:

Available fields :

• zinc_id: ZINC identifier
• smiles: SMILES string representation
• sub_id: Internal substance ID
• supplier_code: Vendor catalog number
• catalogs: List of suppliers offering the compound
• tranche: Encoded molecular properties (H-count, LogP, MW, reactivity phase)

Example :

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

Tranche System

ZINC organizes compounds into "tranches" based on molecular properties:

Format : H##P###M###-phase

• H## : Number of hydrogen bond donors (00-99)
• P### : LogP × 10 (e.g., P035 = LogP 3.5)
• M### : Molecular weight in Daltons (e.g., M400 = 400 Da)
• phase : Reactivity classification

Example tranche : H05P035M400-0

• 5 H-bond donors
• LogP = 3.5
• MW = 400 Da
• Reactivity phase 0

Use tranche data to filter compounds by drug-likeness criteria.

Downloading 3D Structures

For molecular docking, 3D structures are available via file repositories:

File repository : https://files.docking.org/zinc22/

Structures are organized by tranches and available in multiple formats:

• MOL2: Multi-molecule format with 3D coordinates
• SDF: Structure-data file format
• DB2.GZ: Compressed database format for DOCK

Refer to ZINC documentation at https://wiki.docking.org for downloading protocols and batch access methods.

Python Integration

Using curl with Python

import subprocess
import json

def query_zinc_by_id(zinc_id, output_fields="zinc_id,smiles,catalogs"):
    """Query ZINC22 by ZINC ID."""
    url = f"https://cartblanche22.docking.org/[email protected]_id={zinc_id}&output_fields={output_fields}"
    result = subprocess.run(['curl', url], capture_output=True, text=True)
    return result.stdout

def search_by_smiles(smiles, dist=0, adist=0, output_fields="zinc_id,smiles"):
    """Search ZINC22 by SMILES with optional distance parameters."""
    url = f"https://cartblanche22.docking.org/smiles.txt:smiles={smiles}&dist={dist}&adist={adist}&output_fields={output_fields}"
    result = subprocess.run(['curl', url], capture_output=True, text=True)
    return result.stdout

def get_random_compounds(count=100, subset=None, output_fields="zinc_id,smiles,tranche"):
    """Get random compounds from ZINC22."""
    url = f"https://cartblanche22.docking.org/substance/random.txt:count={count}&output_fields={output_fields}"
    if subset:
        url += f"&subset={subset}"
    result = subprocess.run(['curl', url], capture_output=True, text=True)
    return result.stdout

Parsing Results

import pandas as pd
from io import StringIO

# Query ZINC and parse as DataFrame
result = query_zinc_by_id("ZINC000000000001")
df = pd.read_csv(StringIO(result), sep='\t')

# Extract tranche properties
def parse_tranche(tranche_str):
    """Parse ZINC tranche code to extract properties."""
    # Format: H##P###M###-phase
    import re
    match = re.match(r'H(\d+)P(\d+)M(\d+)-(\d+)', tranche_str)
    if match:
        return {
            'h_donors': int(match.group(1)),
            'logP': int(match.group(2)) / 10.0,
            'mw': int(match.group(3)),
            'phase': int(match.group(4))
        }
    return None

df['tranche_props'] = df['tranche'].apply(parse_tranche)

Best Practices

Query Optimization

• Start specific : Begin with exact searches before expanding to similarity searches
• Use appropriate distance parameters : Small dist values (1-3) for close analogs, larger (5-10) for diverse analogs
• Limit output fields : Request only necessary fields to reduce data transfer
• Batch queries : Combine multiple ZINC IDs in a single API call when possible

Performance Considerations

• Rate limiting : Respect server resources; avoid rapid consecutive requests
• Caching : Store frequently accessed compounds locally
• Parallel downloads : When downloading 3D structures, use parallel wget or aria2c for file repositories
• Subset filtering : Use lead-like, drug-like, or fragment subsets to reduce search space

Data Quality

• Verify availability : Supplier catalogs change; confirm compound availability before large orders
• Check stereochemistry : SMILES may not fully specify stereochemistry; verify 3D structures
• Validate structures : Use cheminformatics tools (RDKit, OpenBabel) to verify structure validity
• Cross-reference : When possible, cross-check with other databases (PubChem, ChEMBL)

Resources

references/api_reference.md

Comprehensive documentation including:

• Complete API endpoint reference
• URL syntax and parameter specifications
• Advanced query patterns and examples
• File repository organization and access
• Bulk download methods
• Error handling and troubleshooting
• Integration with molecular docking software

Consult this document for detailed technical information and advanced usage patterns.

Important Disclaimers

Data Reliability

ZINC explicitly states: "We do not guarantee the quality of any molecule for any purpose and take no responsibility for errors arising from the use of this database."

• Compound availability may change without notice
• Structure representations may contain errors
• Supplier information should be verified independently
• Use appropriate validation before experimental work

Appropriate Use

• ZINC is intended for academic and research purposes in drug discovery
• Verify licensing terms for commercial use
• Respect intellectual property when working with patented compounds
• Follow your institution's guidelines for compound procurement

Additional Resources

• ZINC Website : https://zinc.docking.org/
• CartBlanche22 Interface : https://cartblanche22.docking.org/
• ZINC Wiki : https://wiki.docking.org/
• File Repository : https://files.docking.org/zinc22/
• GitHub : https://github.com/docking-org/
• Primary Publication : Irwin et al., J. Chem. Inf. Model 2020 (ZINC15)
• ZINC22 Publication : Irwin et al., J. Chem. Inf. Model 2023

Citations

When using ZINC in publications, cite the appropriate version:

ZINC22 : Irwin, J. J., et al. "ZINC22—A Free Multi-Billion-Scale Database of Tangible Compounds for Ligand Discovery." Journal of Chemical Information and Modeling 2023.

ZINC15 : Irwin, J. J., et al. "ZINC15 – Ligand Discovery for Everyone." Journal of Chemical Information and Modeling 2020, 60, 6065–6073.

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