Chemical Literature and Patent Mapping
SkillDev toolsRetrieve extensive literature (PubMed) and patent associated with a specific chemical compound via PubChem.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Chemical Literature and Patent Mapping skill
What this skill tells your AI
The instructions your AI receives, as published by learningmatter-mit/atomisticskills in .agents/skills/general-chemical-literature/SKILL.md and read by ahel’s review.
Goal
To programmatically check if a specific chemical compound exists in recent literature or patent databases. This skill uses PubChem's PUG-REST XRefs endpoint to extract an exhaustive list of associated PubMed IDs and Patent numbers.
This is incredibly useful as an autonomous "novelty check" for generated molecules.
Instructions
1. Extract Literature and Patents by CID
Provide the CID of the target molecule. By default, the script will output the absolute total number of hits but limits the JSON save array to 1000 to prevent memory flooding for ubiquitous molecules (like Aspirin, which has over 100,000 patents). Adjust --limit as needed.
# Env: base-agent
python .agents/skills/general-chemical-literature/scripts/get_xrefs.py \
--cid 2244 \
--limit 50 \
--outdir research/aspirin_literature \
--output xrefs_aspirin.json
Examples
We can pull cross-references for Aspirin (CID: 2244), saving the top 50 identifiers.
# Env: base-agent
python .agents/skills/general-chemical-literature/scripts/get_xrefs.py \
--cid 2244 \
--limit 50 \
--outdir .agents/skills/general-chemical-literature/examples/aspirin \
--output xrefs_aspirin.json
Constraints
- Novelty Assessment Limitation: If 0 PMIDs or Patents are returned, it strongly implies the molecule is highly novel (or purely computational), but it does not guarantee absolute non-existence.
- Link Generation: The script automatically prints actionable links (
pubmed.ncbi.nlm.nih.gov/andpatents.google.com/patent/) for the top 5 results for immediate verification. - Network Limits: Handled internally via standard exponential backoff.
Author: Bowen Deng Contact: GitHub @learningmatter-mit
Signals
- GitHub stars
- 164
- Forks
- 24
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
general-chemical-literature- Source
- github.com/learningmatter-mit/atomisticskills