brycewang-stanford/cactus-cheminformatics-guide
skills/43-wentorai-research-plugins/skills/domains/chemistry/cactus-cheminformatics-guide/SKILL.md
PNNL cheminformatics LLM agent for molecular analysis
$ install --global
skillsauthnpx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research cactus-cheminformatics-guideInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- cactus-cheminformatics-guide
- description:
- PNNL cheminformatics LLM agent for molecular analysis
- emoji:
- 🌵
- category:
- domains
- subcategory:
- chemistry
- keywords:
- ["CACTUS", "cheminformatics", "PNNL", "molecular analysis", "LLM chemistry", "chemical agent"]
- source:
- https://github.com/pnnl/cactus
CACTUS Cheminformatics Agent Guide
Overview
CACTUS is a cheminformatics LLM agent developed at Pacific Northwest National Laboratory (PNNL) that provides AI-assisted molecular analysis, property prediction, and chemical reasoning. It wraps RDKit, molecular databases, and ML models behind a conversational interface, enabling researchers to query molecular properties, perform similarity searches, and run cheminformatics workflows using natural language.
Usage
from cactus import ChemAgent
agent = ChemAgent(llm_provider="anthropic")
# Natural language chemistry queries
result = agent.ask(
"What is the molecular weight and LogP of aspirin? "
"Is it drug-like by Lipinski's rules?"
)
print(result.answer)
# Aspirin (CC(=O)Oc1ccccc1C(=O)O):
# MW: 180.16, LogP: 1.24
# Lipinski: PASS (MW<500, LogP<5, HBD=1≤5, HBA=4≤10)
# Molecular property calculation
props = agent.calculate_properties(
smiles="CC(=O)Oc1ccccc1C(=O)O",
properties=["mw", "logp", "tpsa", "hbd", "hba", "rotatable"],
)
print(props)
Similarity Search
# Find similar molecules
similar = agent.similarity_search(
query_smiles="CC(=O)Oc1ccccc1C(=O)O", # Aspirin
database="chembl",
threshold=0.7, # Tanimoto similarity
max_results=10,
)
for mol in similar:
print(f"{mol.name}: {mol.smiles} "
f"(similarity: {mol.tanimoto:.3f})")
Substructure Analysis
# Substructure search
matches = agent.substructure_search(
pattern="c1ccccc1C(=O)O", # Benzoic acid motif
database="drugbank",
max_results=20,
)
# Functional group identification
groups = agent.identify_functional_groups(
smiles="CC(=O)Oc1ccccc1C(=O)O"
)
# ["ester", "carboxylic_acid", "aromatic_ring"]
Use Cases
- Molecular analysis: Property calculation via natural language
- Drug screening: Lipinski/Veber rule checking
- Similarity search: Find analogs in chemical databases
- Structure analysis: Substructure and functional group ID
- Chemical education: Interactive chemistry exploration
References
- CACTUS GitHub
- RDKit
- PNNL
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