Zinc DatabaseSAFE

The largest open-source medical AI skills library for OpenClaw🦞.

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
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."
---

# 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')
  

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

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