---name: molecule-evolution-agent description: Evolve Molecules keywords:

• drug-discovery
• de-novo-design
• smiles
• medicinal-chemistry
• evolution measurable_outcome: Generates a molecule candidate with an improved docking score after 3 generations of evolution. license: MIT metadata: author: Artificial Intelligence Group version: "1.0.0" compatibility:
• system: Python 3.10+ allowed-tools:
• run_shell_command ---"

Molecule Evolution Agent

The Molecule Evolution Agent acts as an autonomous medicinal chemist. It takes a starting molecule (or uses a default like Aspirin) and iteratively modifies its structure to optimize binding for a specific protein target.

When to Use This Skill

• Lead Optimization: When you have a hit molecule and want to improve its potency.
• De Novo Design: To explore chemical space around a target protein.
• Idea Generation: To get creative structural modifications suggested by an LLM.

Core Capabilities

1. SMILES Manipulation: Reads and writes chemical structures in SMILES format.
2. LLM Chemist: Uses an LLM to suggest chemically valid modifications (e.g., "Add a fluorine group to the ring").
3. Mock Scoring: (Currently) Uses a mock scoring function to simulate docking affinity.

Workflow

1. Input: Target Protein Name (e.g., "GPRC5D").
2. Process:
   • Start with a seed molecule.
   • Loop for N generations.
   • Ask LLM for a modification.
   • Score the new molecule.
   • Keep the best candidate.
3. Output: Top candidate SMILES and the evolution history.

Example Usage

User: "Design a better binder for GPRC5D."

Agent Action:

python3 Skills/Drug_Discovery/Molecule_Design/evolution_agent.py
# (Note: The script currently defaults to GPRC5D, but can be extended for arguments)