eric861129/skills_all-in-one/zinc-database

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

1. Query ZINC22 using appropriate search method:

``bash # 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:

```python 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 ```

1. 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:

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

1. Perform similarity search with distance threshold:

``bash 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 ``

1. Analyze results to identify purchasable analogs:

```python 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)) ```

1. 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:

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

1. Query ZINC22 API:

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

1. 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

1. Generate random sample:

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

1. 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