datamol
Analyze Molecular Data with Datamol
Cheminformatics workflows often require detailed RDKit code for routine molecular processing. This skill provides focused Datamol guidance for analysis, visualization, and scalable data preparation.
Install with my Agent
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Review the Skillstore skill "datamol" from https://skillstore.io/skills/davila7-datamol.md and its manifest at https://skillstore.io/api/skills/davila7-datamol/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 "datamol". Standardize ethanol and an invalid SMILES entry.
Expected outcome:
- Ethanol: CCO, valid after standardization.
- Invalid entry: rejected because molecule parsing returned no structure.
Using "datamol". Compare a compound library and select 100 diverse molecules.
Expected outcome:
- Selection method: fingerprint-based diversity picking.
- Result: 100 representative molecules with duplicate structures removed.
- Scale note: diversity selection avoids a full clustering distance matrix.
Using "datamol". Summarize a 3D conformer analysis request.
Expected outcome:
Generate an ETKDGv3 ensemble, minimize energies, cluster by RMSD, and retain representative conformers with calculated surface areas.
Security Audit
SafeAll 137 static findings are false positives caused by Markdown formatting, chemistry terminology, placeholder URLs, public links, and a dictionary keys() call. The reviewed files contain Datamol documentation and no prompt injection, reconnaissance, credential access, or executable Ruby backtick behavior. No semantic security finding was identified.
Risk Factors
🌐 Network access (5)
⚙️ External commands (50)
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APA citation
davila7. (2026). datamol security audit report (audit version 9) [Author version unspecified]. Skillstore. https://skillstore.io/skills/davila7-datamol/audits/9BibTeX citation
@techreport{davila7-davila7-datamol-2026,
author = {davila7},
title = {datamol security audit report (audit version 9)},
institution = {Skillstore},
year = {2026},
number = {9},
url = {https://skillstore.io/skills/davila7-datamol/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: "datamol security audit report (audit version 9)"
version: "unspecified"
type: report
authors:
- name: "davila7"
date-released: "2026-07-23"
url: "https://skillstore.io/skills/davila7-datamol/audits/9"
identifiers:
- type: other
value: "skillstore:davila7-datamol: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-datamol
2026-08-21
k-dense-ai-datamol
2026-08-21
Skillstore Score
Why this score Evidence Confidence: HighWhat You Can Build
Prepare Screening Libraries
Standardize structures, remove invalid records, calculate descriptors, and select diverse compounds before virtual screening.
Build Molecular Features
Generate fingerprints and descriptor tables for reproducible machine learning experiments.
Analyze Conformer Ensembles
Generate conformers, cluster geometries, calculate surface areas, and select representative structures.
Try These Prompts
Using Datamol, validate these SMILES: [SMILES]. Explain rejected entries and return standardized SMILES for valid molecules.
Prepare a Datamol workflow for [input file]. Compute molecular weight, LogP, hydrogen-bond counts, and TPSA, then describe invalid-row handling.
Design a Datamol workflow that standardizes [dataset], computes fingerprints, and selects [count] diverse molecules. Explain fingerprint and selection settings.
Develop a scaffold-aware Datamol analysis for [dataset] with [activity column]. Include scaffold grouping, train-test separation, aligned visualization, and scale safeguards.
Best Practices
- Standardize molecules from external sources before calculating descriptors, fingerprints, or similarities.
- Check parsing results for missing molecules before batch operations.
- Choose parallel worker counts and algorithms according to dataset size and available memory.
Avoid
- Do not treat failed molecule parsing as a valid empty structure.
- Do not use full-distance clustering for libraries that exceed practical memory limits.
- Do not assume remote paths work without network access, provider packages, and configured authorization.
Frequently Asked Questions
What must be installed?
Which molecular formats are supported?
Does the skill return Datamol-specific molecules?
Can it process large molecular datasets?
Can it access cloud storage?
When should I use RDKit directly?
Developer Details
Author
davila7License
MIT
Skillstore revision
r2
Version notice
The author did not declare a version.
Ref
c43861a65bb95efcae259cd161c9d6f4dc7eec6f
Maintenance freshness
7/24/2026
Usage
4 downloads · 501 views
File structure