zinc-database
GitHub该技能用于访问ZINC数据库，支持通过ID或SMILES检索化合物、进行相似性与模拟搜索，获取3D结构以辅助虚拟筛选和药物发现。
Trigger Scenarios
Install
npx skills add synthetic-sciences/openscience --skill zinc-database -g -y
SKILL.md
Frontmatter
{
"name": "zinc-database",
"license": "Unknown",
"category": "databases",
"metadata": {
"skill-author": "Synthetic Sciences"
},
"description": "Access ZINC (230M+ purchasable compounds). Search by ZINC ID\/SMILES, similarity searches, 3D-ready structures for docking, analog discovery, for virtual screening and drug discovery."
}
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
-
Define search criteria based on target properties or desired chemical space
-
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 -
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 -
Download 3D structures for docking using ZINC ID or download from file repositories
Workflow 2: Finding Analogs of a Hit Compound
-
Obtain SMILES of the hit compound:
hit_smiles = "CC(C)Cc1ccc(cc1)C(C)C(=O)O" # Example: Ibuprofen -
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 -
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)) -
Retrieve 3D structures for the most promising analogs
Workflow 3: Batch Compound Retrieval
-
Compile list of ZINC IDs from literature, databases, or previous screens:
zinc_ids = [ "ZINC000000000001", "ZINC000000000002", "ZINC000000000003" ] zinc_ids_str = ",".join(zinc_ids) -
Query ZINC22 API:
curl "https://cartblanche22.docking.org/substances.txt:zinc_id=ZINC000000000001,ZINC000000000002&output_fields=zinc_id,smiles,supplier_code,catalogs" -
Process results for downstream analysis or purchasing
Workflow 4: Chemical Space Sampling
-
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
-
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 -
Analyze chemical diversity and prepare for virtual screening
Output Fields
Customize API responses with the output_fields parameter:
Available fields:
zinc_id: ZINC identifiersmiles: SMILES string representationsub_id: Internal substance IDsupplier_code: Vendor catalog numbercatalogs: List of suppliers offering the compoundtranche: 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.
Version History
- e9844a4 Current 2026-07-11 17:26
