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skills/zinc-database/references/api_reference.md
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skills/zinc-database/references/api_reference.md
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# ZINC Database API Reference
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## Overview
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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.
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## Base URLs
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### ZINC22 (Current)
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- **CartBlanche22 API**: `https://cartblanche22.docking.org/`
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- **File Repository**: `https://files.docking.org/zinc22/`
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- **Main Website**: `https://zinc.docking.org/`
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### ZINC20 (Maintained)
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- **API**: `https://zinc20.docking.org/`
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- **File Repository**: `https://files.docking.org/zinc20/`
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### Documentation
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- **Wiki**: `https://wiki.docking.org/`
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- **GitHub**: `https://github.com/docking-org/`
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## API Endpoints
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### 1. Substance Retrieval by ZINC ID
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Retrieve compound information using ZINC identifiers.
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**Endpoint**: `/substances.txt`
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**Parameters**:
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- `zinc_id` (required): Single ZINC ID or comma-separated list
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- `output_fields` (optional): Comma-separated field names (default: all fields)
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**URL Format**:
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```
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https://cartblanche22.docking.org/substances.txt:zinc_id={ZINC_ID}&output_fields={FIELDS}
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```
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**Examples**:
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Single compound:
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```bash
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curl "https://cartblanche22.docking.org/[email protected]_fields=zinc_id,smiles,catalogs"
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```
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Multiple compounds:
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```bash
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curl "https://cartblanche22.docking.org/substances.txt:zinc_id=ZINC000000000001,ZINC000000000002,ZINC000000000003&output_fields=zinc_id,smiles,tranche"
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```
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Batch retrieval from file:
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```bash
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# Create file with ZINC IDs (one per line or comma-separated)
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curl -X POST "https://cartblanche22.docking.org/substances.txt?output_fields=zinc_id,smiles" \
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-F "zinc_id=@zinc_ids.txt"
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```
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**Response Format** (TSV):
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```
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zinc_id smiles catalogs
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ZINC000000000001 CC(C)O [vendor1,vendor2]
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ZINC000000000002 c1ccccc1 [vendor3]
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```
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### 2. Structure Search by SMILES
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Search for compounds by chemical structure with optional similarity thresholds.
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**Endpoint**: `/smiles.txt`
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**Parameters**:
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- `smiles` (required): Query SMILES string (URL-encode if necessary)
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- `dist` (optional): Tanimoto distance threshold (0-10, default: 0 = exact)
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- `adist` (optional): Alternative distance metric (0-10, default: 0)
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- `output_fields` (optional): Comma-separated field names
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**URL Format**:
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```
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https://cartblanche22.docking.org/smiles.txt:smiles={SMILES}&dist={DIST}&adist={ADIST}&output_fields={FIELDS}
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```
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**Examples**:
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Exact structure match:
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```bash
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curl "https://cartblanche22.docking.org/smiles.txt:smiles=c1ccccc1&output_fields=zinc_id,smiles"
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```
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Similarity search (Tanimoto distance = 3):
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```bash
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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"
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```
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Broad similarity search:
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```bash
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curl "https://cartblanche22.docking.org/smiles.txt:smiles=c1ccccc1&dist=5&adist=5&output_fields=zinc_id,smiles,tranche"
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```
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URL-encoded SMILES (for special characters):
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```bash
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# Original: CC(=O)Oc1ccccc1C(=O)O
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# Encoded: CC%28%3DO%29Oc1ccccc1C%28%3DO%29O
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curl "https://cartblanche22.docking.org/smiles.txt:smiles=CC%28%3DO%29Oc1ccccc1C%28%3DO%29O&dist=2"
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```
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**Distance Parameters Interpretation**:
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- `dist=0`: Exact match
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- `dist=1-3`: Close analogs (high similarity)
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- `dist=4-6`: Moderate analogs
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- `dist=7-10`: Diverse chemical space
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### 3. Supplier Code Search
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Query compounds by vendor catalog numbers.
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**Endpoint**: `/catitems.txt`
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**Parameters**:
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- `catitem_id` (required): Supplier catalog code
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- `output_fields` (optional): Comma-separated field names
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**URL Format**:
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```
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https://cartblanche22.docking.org/catitems.txt:catitem_id={SUPPLIER_CODE}&output_fields={FIELDS}
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```
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**Example**:
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```bash
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curl "https://cartblanche22.docking.org/catitems.txt:catitem_id=SUPPLIER-12345&output_fields=zinc_id,smiles,supplier_code,catalogs"
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```
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### 4. Random Compound Sampling
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Generate random compound sets with optional filtering by chemical properties.
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**Endpoint**: `/substance/random.txt`
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**Parameters**:
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- `count` (optional): Number of compounds to retrieve (default: 100, max: depends on server)
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- `subset` (optional): Filter by predefined subset (e.g., 'lead-like', 'drug-like', 'fragment')
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- `output_fields` (optional): Comma-separated field names
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**URL Format**:
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```
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https://cartblanche22.docking.org/substance/random.txt:count={COUNT}&subset={SUBSET}&output_fields={FIELDS}
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```
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**Examples**:
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Random 100 compounds (default):
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```bash
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curl "https://cartblanche22.docking.org/substance/random.txt"
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```
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Random lead-like molecules:
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```bash
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curl "https://cartblanche22.docking.org/substance/random.txt:count=1000&subset=lead-like&output_fields=zinc_id,smiles,tranche"
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```
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Random drug-like molecules:
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```bash
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curl "https://cartblanche22.docking.org/substance/random.txt:count=5000&subset=drug-like&output_fields=zinc_id,smiles"
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```
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Random fragments:
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```bash
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curl "https://cartblanche22.docking.org/substance/random.txt:count=500&subset=fragment&output_fields=zinc_id,smiles,tranche"
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```
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**Subset Definitions**:
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- `fragment`: MW < 250, suitable for fragment-based drug discovery
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- `lead-like`: MW 250-350, LogP ≤ 3.5, rotatable bonds ≤ 7
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- `drug-like`: MW 350-500, follows Lipinski's Rule of Five
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- `lugs`: Large, unusually good subset (highly curated)
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## Output Fields
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### Available Fields
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Customize API responses using the `output_fields` parameter:
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| Field | Description | Example |
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|-------|-------------|---------|
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| `zinc_id` | ZINC identifier | ZINC000000000001 |
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| `smiles` | Canonical SMILES string | CC(C)O |
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| `sub_id` | Internal substance ID | 123456 |
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| `supplier_code` | Vendor catalog number | AB-1234567 |
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| `catalogs` | List of suppliers | [emolecules, mcule, mcule-ultimate] |
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| `tranche` | Encoded molecular properties | H02P025M300-0 |
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| `mwt` | Molecular weight | 325.45 |
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| `logp` | LogP (partition coefficient) | 2.5 |
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| `hba` | H-bond acceptors | 4 |
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| `hbd` | H-bond donors | 2 |
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| `rotatable_bonds` | Rotatable bonds count | 5 |
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**Note**: Not all fields are available for all endpoints. Field availability depends on the database version and endpoint.
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### Default Fields
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If `output_fields` is not specified, endpoints return all available fields in TSV format.
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### Custom Field Selection
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Request specific fields only:
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```bash
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curl "https://cartblanche22.docking.org/[email protected]_fields=zinc_id,smiles"
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```
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Request multiple fields:
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```bash
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curl "https://cartblanche22.docking.org/[email protected]_fields=zinc_id,smiles,tranche,catalogs"
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```
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## Tranche System
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ZINC organizes compounds into tranches based on molecular properties for efficient filtering and organization.
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### Tranche Code Format
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**Pattern**: `H##P###M###-phase`
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| Component | Description | Range |
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|-----------|-------------|-------|
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| H## | Hydrogen bond donors | 00-99 |
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| P### | LogP × 10 | 000-999 (e.g., P035 = LogP 3.5) |
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| M### | Molecular weight | 000-999 Da |
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| phase | Reactivity classification | 0-9 |
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### Examples
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| Tranche Code | Interpretation |
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|--------------|----------------|
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| `H00P010M250-0` | 0 H-donors, LogP=1.0, MW=250 Da, phase 0 |
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| `H05P035M400-0` | 5 H-donors, LogP=3.5, MW=400 Da, phase 0 |
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| `H02P-005M180-0` | 2 H-donors, LogP=-0.5, MW=180 Da, phase 0 |
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### Reactivity Phases
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| Phase | Description |
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|-------|-------------|
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| 0 | Unreactive (preferred for screening) |
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| 1-9 | Increasing reactivity (PAINS, reactive groups) |
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### Parsing Tranches in Python
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```python
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import re
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def parse_tranche(tranche_str):
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"""
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Parse ZINC tranche code.
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Args:
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tranche_str: Tranche code (e.g., "H05P035M400-0")
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Returns:
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dict with h_donors, logp, mw, phase
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"""
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pattern = r'H(\d+)P(-?\d+)M(\d+)-(\d+)'
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match = re.match(pattern, tranche_str)
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if not match:
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return None
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return {
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'h_donors': int(match.group(1)),
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'logp': int(match.group(2)) / 10.0,
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'mw': int(match.group(3)),
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'phase': int(match.group(4))
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}
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# Example usage
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tranche = "H05P035M400-0"
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props = parse_tranche(tranche)
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print(props) # {'h_donors': 5, 'logp': 3.5, 'mw': 400, 'phase': 0}
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```
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### Filtering by Tranches
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Download specific tranches from file repositories:
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```bash
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# Download all compounds in a specific tranche
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wget https://files.docking.org/zinc22/H05/H05P035M400-0.db2.gz
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```
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## File Repository Access
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### Directory Structure
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ZINC22 3D structures are organized hierarchically by H-bond donors:
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```
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https://files.docking.org/zinc22/
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├── H00/
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│ ├── H00P010M200-0.db2.gz
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│ ├── H00P020M250-0.db2.gz
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│ └── ...
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├── H01/
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├── H02/
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└── ...
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```
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### File Formats
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| Extension | Format | Description |
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|-----------|--------|-------------|
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| `.db2.gz` | DOCK database | Compressed multi-conformer DB for DOCK |
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| `.mol2.gz` | MOL2 | Multi-molecule format with 3D coordinates |
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| `.sdf.gz` | SDF | Structure-Data File format |
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| `.smi` | SMILES | Plain text SMILES with ZINC IDs |
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### Downloading 3D Structures
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**Single tranche**:
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```bash
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wget https://files.docking.org/zinc22/H05/H05P035M400-0.db2.gz
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```
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**Multiple tranches** (parallel download with aria2c):
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```bash
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# Create URL list
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cat > tranche_urls.txt <<EOF
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https://files.docking.org/zinc22/H05/H05P035M400-0.db2.gz
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https://files.docking.org/zinc22/H05/H05P035M400-0.db2.gz
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https://files.docking.org/zinc22/H05/H05P040M400-0.db2.gz
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EOF
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# Download in parallel
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aria2c -i tranche_urls.txt -x 8 -j 4
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```
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**Recursive download** (use with caution - large data):
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```bash
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wget -r -np -nH --cut-dirs=1 -A "*.db2.gz" \
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https://files.docking.org/zinc22/H05/
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```
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### Extracting Structures
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```bash
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# Decompress
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gunzip H05P035M400-0.db2.gz
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# Convert to other formats using OpenBabel
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obabel H05P035M400-0.db2 -O output.sdf
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obabel H05P035M400-0.db2 -O output.mol2
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```
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## Advanced Query Patterns
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### Combining Multiple Search Criteria
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**Python wrapper for complex queries**:
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```python
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import subprocess
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import pandas as pd
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from io import StringIO
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def advanced_zinc_search(smiles=None, zinc_ids=None, dist=0,
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subset=None, count=None, output_fields=None):
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"""
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Flexible ZINC search with multiple criteria.
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Args:
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smiles: SMILES string for structure search
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zinc_ids: List of ZINC IDs for batch retrieval
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dist: Distance parameter for similarity (0-10)
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subset: Subset filter (lead-like, drug-like, fragment)
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count: Number of random compounds
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output_fields: List of fields to return
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Returns:
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pandas DataFrame with results
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"""
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if output_fields is None:
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output_fields = ['zinc_id', 'smiles', 'tranche', 'catalogs']
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fields_str = ','.join(output_fields)
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# Structure search
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if smiles:
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url = f"https://cartblanche22.docking.org/smiles.txt:smiles={smiles}&dist={dist}&output_fields={fields_str}"
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# Batch retrieval
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elif zinc_ids:
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zinc_ids_str = ','.join(zinc_ids)
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url = f"https://cartblanche22.docking.org/substances.txt:zinc_id={zinc_ids_str}&output_fields={fields_str}"
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# Random sampling
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elif count:
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url = f"https://cartblanche22.docking.org/substance/random.txt:count={count}&output_fields={fields_str}"
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if subset:
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url += f"&subset={subset}"
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else:
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raise ValueError("Must specify smiles, zinc_ids, or count")
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||||
# Execute query
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||||
result = subprocess.run(['curl', '-s', url],
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capture_output=True, text=True)
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||||
|
||||
# Parse to DataFrame
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||||
df = pd.read_csv(StringIO(result.stdout), sep='\t')
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||||
|
||||
return df
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||||
```
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|
||||
**Usage examples**:
|
||||
|
||||
```python
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||||
# Find similar compounds
|
||||
df = advanced_zinc_search(
|
||||
smiles="CC(C)Cc1ccc(cc1)C(C)C(=O)O",
|
||||
dist=3,
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||||
output_fields=['zinc_id', 'smiles', 'catalogs']
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)
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||||
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||||
# Batch retrieval
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||||
zinc_ids = ["ZINC000000000001", "ZINC000000000002"]
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df = advanced_zinc_search(zinc_ids=zinc_ids)
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||||
# Random drug-like set
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df = advanced_zinc_search(
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count=1000,
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||||
subset='drug-like',
|
||||
output_fields=['zinc_id', 'smiles', 'tranche']
|
||||
)
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||||
```
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||||
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||||
### Property-Based Filtering
|
||||
|
||||
Filter compounds by molecular properties using tranche data:
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||||
|
||||
```python
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||||
def filter_by_properties(df, mw_range=None, logp_range=None,
|
||||
max_hbd=None, phase=0):
|
||||
"""
|
||||
Filter DataFrame by molecular properties.
|
||||
|
||||
Args:
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||||
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
|
||||
|
||||
```python
|
||||
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
|
||||
|
||||
```python
|
||||
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
|
||||
|
||||
```bash
|
||||
# 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
|
||||
|
||||
```bash
|
||||
# 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
|
||||
|
||||
```python
|
||||
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
|
||||
|
||||
```python
|
||||
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
|
||||
|
||||
| Feature | ZINC22 | ZINC20 | ZINC15 |
|
||||
|---------|--------|--------|--------|
|
||||
| Compounds | 230M+ purchasable | Focused on leads | ~750M total |
|
||||
| API | CartBlanche22 | Similar | REST-like |
|
||||
| Tranches | Yes | Yes | Yes |
|
||||
| 3D Structures | Yes | Yes | Yes |
|
||||
| Status | Current, growing | Maintained | Legacy |
|
||||
|
||||
### 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
|
||||
Reference in New Issue
Block a user