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Agent skill

Zinc Database

by davila7 in davila7/claude-code-templates

Access ZINC (230M+ purchasable compounds). An agent skill from davila7/claude-code-templates.

MITAuto-check passedResearch & Science

Install Zinc Database

skills CLI
$ npx skills add davila7/claude-code-templates --skill zinc-database -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install davila7/claude-code-templates zinc-database --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/davila7/claude-code-templates.git skills-src && mkdir -p .claude/skills && cp -r skills-src/cli-tool/components/skills/scientific/zinc-database .claude/skills/zinc-database && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
zinc-database
GitHub stars
32k
Used in
11 other repos
Token cost
~3.5k tokens
SKILL.md length
1,098 words
Files
2 (incl. references)
Skills in repo
477
Repo updated
First seen
Licence
MIT

At a glance

Access ZINC (230M+ purchasable compounds). An agent skill from davila7/claude-code-templates.

  • Works in 4 steps: Search by ZINC ID → Search by SMILES → Search by Supplier Codes → …
  • Tasks that involve Drug discovery and cheminformatics
  • SKILL.md covers Overview, When to Use This Skill, Database Versions and Access Methods, plus 9 more sections
  • Calls curl; reaches cartblanche22.docking.org

What it does

Zinc Database is an agent skill from davila7/claude-code-templates. 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.

Its SKILL.md is about 3.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/api_reference.md`).

It sits in Research & Science, covering Drug discovery and cheminformatics. The repository describes itself as: CLI tool for configuring and monitoring Claude Code. The licence is MIT.

When your agent uses it

  • Tasks that involve Drug discovery and cheminformatics

Example prompts

  • “/zinc-database”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Search by ZINC ID
  2. Search by SMILES
  3. Search by Supplier Codes
  4. Random Compound Sampling

What it can do on your machine

Read from SKILL.md and the folder at commit 14680ec. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • curl

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • cartblanche22.docking.org

    Also links to:

    • zinc.docking.org
    • files.docking.org
    • wiki.docking.org
    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Zinc Database loads about 3.5k tokens when it runs, and up to ~8.3k if it reads all its reference files. Until then it costs about 49 tokens; SKILL.md has 1,098 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~49
When it runs · the whole SKILL.md, loaded when a task matches
~3.5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~8.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from davila7/claude-code-templates at commit 14680ec, republished under its MIT licence (© davila7). 1,098 words, ~3,455 tokens.

Download SKILL.mdSave it as .claude/skills/zinc-database/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
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')
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"
Show full SKILL.md (493 more words)Show less

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

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.

© davila7, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in cli-tool/components/skills/scientific/zinc-database of davila7/claude-code-templates.

  • SKILL.md
  • references/api_reference.md

Open the folder on GitHubat commit 14680ec

Used in 11 other repositories

We found 16 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 11 other GitHub owners. This page covers the copy in davila7/claude-code-templates, which our catalogue first saw on October 7, 2026.

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Categories

Questions about Zinc Database

What does Zinc Database do?

Access ZINC (230M+ purchasable compounds). An agent skill from davila7/claude-code-templates. Zinc Database is an agent skill from davila7/claude-code-templates. Access ZINC (230M+ purchasable compounds).

When should I use Zinc Database?

Zinc Database fits situations like: tasks that involve Drug discovery and cheminformatics.

How do I install Zinc Database in Claude Code?

Run `npx skills add davila7/claude-code-templates --skill zinc-database -a claude-code`. Or copy the skill folder (cli-tool/components/skills/scientific/zinc-database in davila7/claude-code-templates) into .claude/skills/zinc-database in your project. Claude Code loads it when a task matches its description.

How do I install Zinc Database in Codex?

Run `npx skills add davila7/claude-code-templates --skill zinc-database -a codex`. Or copy the skill folder (cli-tool/components/skills/scientific/zinc-database in davila7/claude-code-templates) into .agents/skills/zinc-database in your project. Codex loads it when a task matches its description.

Can I use Zinc Database in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add davila7/claude-code-templates --skill zinc-database -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/zinc-database, .gemini/skills/zinc-database, .github/skills/zinc-database and .opencode/skills/zinc-database in your project.

What does Zinc Database need to run?

Going by SKILL.md and its folder, Zinc Database needs the command-line tools its instructions call (curl). Our summary lists: Python 3.

Does Zinc Database access the network?

SKILL.md names 5 domains. In commands or code: cartblanche22.docking.org; the agent is likely to contact it when it follows the instructions. As links in the text: zinc.docking.org, files.docking.org, wiki.docking.org and github.com. This is read from the text; nothing was executed.

Is Zinc Database safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Zinc Database use?

Zinc Database is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Zinc Database use?

About 3.5k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.9k tokens, read only when the agent opens those files.

What are the alternatives to Zinc Database?

Skills that share tags, products or a category with Zinc Database: Molecode (AtomFlow-AI/MoleCode, 305 stars), Drug Discovery (Tommy-yw/RunbookHermes, 546 stars), DiffDock Molecular Docking (K-Dense-AI/scientific-agent-skills, 48k stars) and Biomedical Analysis Dispatch (xjtulyc/MedgeClaw, 617 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Zinc Database?

davila7 (a GitHub user) maintains it in davila7/claude-code-templates, which has 32,463 GitHub stars. The repository holds 477 skills in this directory. The repository was last updated on October 8, 2026.

Source: davila7/claude-code-templates on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.