zinc-database — База химических соединений для научных исследований

--- name: zinc-database 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. license: Unknown metadata: skill-author: K-Dense Inc. --- # 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**: ```bash curl "https://cartblanche22.docking.org/[email protected]_fields=smiles,zinc_id" ``` **Multiple IDs**: ```bash 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**: ```bash 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**: ```bash curl "https://cartblanche22.docking.org/smiles.txt:smiles=c1ccccc1" ``` **Example - Similarity search**: ```bash 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**: ```bash 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**: ```bash 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**: ```bash 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: ```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 ``` 3. **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 ``` 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: ```python hit_smiles = "CC(C)Cc1ccc(cc1)C(C)C(=O)O" # Example: Ibuprofen ``` 2. **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 ``` 3. **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)) ``` 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: ```python zinc_ids = [ "ZINC000000000001", "ZINC000000000002", "ZINC000000000003" ] zinc_ids_str = ",".join(zinc_ids) ``` 2. **Query ZINC22 API**: ```bash 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**: ```bash 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**: ```bash 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 ```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 ```python 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.