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zinc-database

Query ZINC22 for purchasable compounds and download 3D structures for docking.

33.0k|3.2k|Updated Oct 19, 2025
Search Tags:#chemical space#ZINC#virtual screening#SMILES#drug discovery#molecular docking#compounds
One-click install
npx skills add https://github.com/K-Dense-AI/claude-scientific-skills --skill zinc-database
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: zinc-database
Source: https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-databases/zinc-database
Command: npx skills add https://github.com/K-Dense-AI/claude-scientific-skills --skill zinc-database

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, pandas, and includes references (resource) components.

What problem does it solve?

Finding commercially available compounds for drug discovery, virtual screening, or analog searches is often fragmented and inefficient. This Skill automates access to the ZINC database, providing a unified interface to over 230 million purchasable compounds and their 3D structures, drastically reducing search time.

Core Features & Use Cases

  • Compound Search: Query by ZINC ID, SMILES, or supplier codes for exact matches or similarity searches with customizable distance thresholds.
  • 3D Structure Access: Download 3D-ready molecular structures in formats like MOL2 or SDF, optimized for molecular docking and virtual screening.
  • Analog Discovery & Filtering: Efficiently find structurally similar compounds to a hit molecule and filter by drug-likeness properties using the tranche system.
  • Use Case: Given a lead compound's SMILES, automatically search ZINC for 100 purchasable analogs with a specified similarity threshold, and prepare their 3D structures for immediate docking, streamlining lead optimization.

Quick Start

Search by SMILES for exact match (e.g., benzene)

curl "https://cartblanche22.docking.org/smiles.txt:smiles=c1ccccc1&output_fields=zinc_id,smiles"

Search for similar compounds (e.g., Ibuprofen analogs with Tanimoto distance = 3)

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"

Frequently Asked Questions about zinc-database

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I search the ZINC database for purchasable compounds by SMILES?▼

Virtual screening with ZINC uses SMILES strings to query over 230 million purchasable compounds. Submit your molecule's SMILES to the ZINC API with output_fields parameters to retrieve matching zinc_id, SMILES, and supplier information for lead discovery.

Can I download 3D structures from ZINC for molecular docking?▼

Yes, ZINC provides 3D-ready molecular structures in MOL2 and SDF formats optimized for docking workflows. Retrieve structures directly via the API after identifying compounds through SMILES or similarity searches.

What's the best way to find analog compounds similar to a hit molecule?▼

ZINC analog discovery uses Tanimoto distance thresholds to identify structurally similar purchasable compounds from your lead structure. Filter results by drug-likeness properties and tranche system to narrow analog sets for lead optimization.

Do I need to format compound data before querying ZINC?▼

Compound queries require SMILES strings or ZINC IDs as input; pandas and requests libraries handle data preparation and API communication. Structure your input data with valid SMILES notation or existing ZINC identifiers for seamless searches.

Can ZINC support supplier-specific or filtered compound searches?▼

ZINC22 supports queries across drug-like, lead-like, and fragment subsets with supplier code filtering. Customize searches by tranche system and catitems to target compounds from specific vendors or chemical property ranges.

What happens if my SMILES query returns no exact matches in ZINC?▼

Exact SMILES matches may be unavailable; use similarity searching with adjustable Tanimoto distance thresholds instead. Lower distance values retrieve closer analogs, expanding discovery options when direct matches don't exist.

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