brycewang-stanford 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