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cactus-cheminformatics-guide

PNNL cheminformatics LLM agent for molecular analysis

Published by brycewang-stanford ↗brycewang-stanford/Auto-Empirical-Research-Skills

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cactus-cheminformatics-guide

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Use cactus-cheminformatics-guide to analyze [molecule or SMILES] for molecular properties, functional groups, and Lipinski drug-likeness, and return a concise results table with interpretations.

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  • [molecule or SMILES]

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SKILL.md

name: cactus-cheminformatics-guidedescription: "PNNL cheminformatics LLM agent for molecular analysis"metadata:   openclaw:     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

  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