zinc-database
Search ZINC for Purchasable Compounds
Finding purchasable compounds and docking-ready structures in ZINC can require complex queries. This skill guides searches, downloads, filtering, and result interpretation through documented ZINC services.
Install with my Agent
Copy this request to your Agent. It includes the canonical Skill page and manifest.
Review the Skillstore skill "zinc-database" from https://skillstore.io/skills/davila7-zinc-database.md and its manifest at https://skillstore.io/api/skills/davila7-zinc-database/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.
Test it
Using "zinc-database". Look up ZINC000000000001 and summarize the available compound fields.
Expected outcome:
The response identifies the requested compound, lists available SMILES, supplier catalogs, and tranche properties, and clearly marks any unavailable fields.
Using "zinc-database". Find close purchasable analogs of ibuprofen and explain the search settings.
Expected outcome:
- Search setup: an encoded ibuprofen SMILES query with a narrow similarity distance.
- Result summary: candidate ZINC identifiers, structures, supplier catalogs, and tranche values.
- Review note: verify stereochemistry, current availability, and structure quality before selection.
Using "zinc-database". Create a plan for a 5,000-compound lead-like docking library.
Expected outcome:
The plan defines subset fields, batched retrieval, local caching, tranche checks, 3D downloads, structure validation, and preparation for the selected docking tool.
Security Audit
SafeReview found no malicious intent, prompt injection, secret access, or data exfiltration. Subprocess examples use argument lists without shell expansion; URL, database, and reconnaissance detections are documentation or file-format false positives. Network access remains an expected dependency on public ZINC services.
Risk Factors
⚙️ External commands (50)
🌐 Network access (50)
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APA citation
davila7. (2026). zinc-database security audit report (audit version 9) [Author version unspecified]. Skillstore. https://skillstore.io/skills/davila7-zinc-database/audits/9BibTeX citation
@techreport{davila7-davila7-zinc-database-2026,
author = {davila7},
title = {zinc-database security audit report (audit version 9)},
institution = {Skillstore},
year = {2026},
number = {9},
url = {https://skillstore.io/skills/davila7-zinc-database/audits/9},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "zinc-database security audit report (audit version 9)"
version: "unspecified"
type: report
authors:
- name: "davila7"
date-released: "2026-07-23"
url: "https://skillstore.io/skills/davila7-zinc-database/audits/9"
identifiers:
- type: other
value: "skillstore:davila7-zinc-database:audit:9"
description: "Skillstore immutable audit report identifier"
Compare variants
2 installable variantsEach author remains a separate installable skill. The recommended variant is ranked by Skillstore evidence.
Why this variant is first
davila7-zinc-database
2026-09-09
k-dense-ai-zinc-database
2026-09-09
Skillstore Score
Why this score Evidence Confidence: HighWhat You Can Build
Discover Purchasable Analogs
Find structurally similar compounds for a known hit and compare identifiers, suppliers, and tranche properties.
Prepare Docking Libraries
Select suitable ZINC subsets, retrieve 3D structures, and prepare files for molecular docking workflows.
Sample Chemical Space
Generate reproducible compound sets for model evaluation, benchmarking, or exploratory analysis.
Try These Prompts
Find ZINC000000000001. Return its ZINC ID, SMILES, suppliers, and tranche details. Explain any missing fields.
Search ZINC for close analogs of [SMILES] using distance [DISTANCE]. Return [COUNT] purchasable candidates with identifiers, structures, suppliers, and tranche values.
Build a random [SUBSET] set of [COUNT] ZINC compounds. Request identifiers, SMILES, and tranche data, then summarize the sampling criteria.
Design a ZINC22 workflow for [DOCKING_TARGET]. Define property filters, batch strategy, rate controls, 3D format, validation steps, and docking-tool preparation.
Best Practices
- URL-encode SMILES and validate identifiers before sending API requests.
- Begin with exact searches, then widen similarity thresholds only when necessary.
- Batch requests, cache results, and respect public service capacity.
Avoid
- Do not launch unrestricted recursive downloads without estimating storage and bandwidth.
- Do not treat supplier listings or molecular structures as guaranteed accurate.
- Do not build shell command strings from untrusted compound inputs.
Frequently Asked Questions
What is ZINC?
Does this skill require credentials?
Can it search by SMILES?
Can it download 3D structures?
Is supplier availability guaranteed?
Does the skill include an executable client?
Developer Details
Author
davila7License
MIT
Skillstore revision
r2
Version notice
The author did not declare a version.
Ref
c43861a65bb95efcae259cd161c9d6f4dc7eec6f
Maintenance freshness
7/25/2026
Usage
10 downloads · 269 views
File structure