cactus-cheminformatics-guide
PNNL cheminformatics LLM agent for molecular analysis
Best use case
cactus-cheminformatics-guide is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
PNNL cheminformatics LLM agent for molecular analysis
Teams using cactus-cheminformatics-guide should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/cactus-cheminformatics-guide/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How cactus-cheminformatics-guide Compares
| Feature / Agent | cactus-cheminformatics-guide | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
PNNL cheminformatics LLM agent for molecular analysis
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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
```python
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
```python
# 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
```python
# 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
1. **Molecular analysis**: Property calculation via natural language
2. **Drug screening**: Lipinski/Veber rule checking
3. **Similarity search**: Find analogs in chemical databases
4. **Structure analysis**: Substructure and functional group ID
5. **Chemical education**: Interactive chemistry exploration
## References
- [CACTUS GitHub](https://github.com/pnnl/cactus)
- [RDKit](https://www.rdkit.org/)
- [PNNL](https://www.pnnl.gov/)Related Skills
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