Molclaw Rgroup Optimization

R-Group RL Optimization (LibInvent Staged Learning)

When to Use This Tool

Use libinvent_rgroup_optimization when you have a fixed scaffold and want to find R-group decorations that are optimized toward specific property targets (QED, MW, LogP, TPSA) using reinforcement learning. This tool combines the scaffold-constrained generation of LibInvent with the goal-directed optimization of REINVENT4's staged_learning mode.

Key difference from libinvent_rgroup_sampling:

Use this tool when:

• You have a scaffold with [*:N] attachment points AND specific property goals
• The task requires "optimize R-groups to maximize QED while keeping MW < 500"
• rgroup_sampling + filtering yields too few molecules meeting property constraints
• You need R-group-specific constraints (e.g., "R-groups should be small, max 1 ring")

Tool Description

RL-based R-group optimization on a fixed scaffold.
Args:
    scaffolds_file (str): Path to a .smi file containing one or more scaffolds with [*:N] attachment points (one scaffold per line)
    target_smiles (str): Optional reference molecule SMILES for similarity scoring. Default '' (no similarity target).
    similarity_weight (float): Weight for Tanimoto similarity to target_smiles. Only used when target_smiles is non-empty. Default 0.0.
    qed_weight (float): Weight for QED optimization. Default 0.4.
    mw_weight (float): Weight for molecular weight scoring (double_sigmoid). Default 0.2.
    mw_low (float): Lower bound of optimal MW range. Default 200.
    mw_high (float): Upper bound of optimal MW range. Default 500.
    logp_weight (float): Weight for LogP scoring (double_sigmoid). Default 0.1.
    logp_low (float): Lower bound of optimal LogP range. Default 0.
    logp_high (float): Upper bound of optimal LogP range. Default 5.
    tpsa_weight (float): Weight for TPSA scoring. Default 0.0.
    tpsa_low (float): Default 0.
    tpsa_high (float): Default 140.
    rgroup_mw_weight (float): Weight for R-group molecular weight constraint. Default 0.0.
    rgroup_mw_min (float): Minimum R-group MW. Default 15.
    rgroup_mw_max (float): Maximum R-group MW. Default 200.
    rgroup_rings_weight (float): Weight for R-group ring count constraint. Default 0.0.
    rgroup_rings_min (int): Minimum rings in R-group. Default 0.
    rgroup_rings_max (int): Maximum rings in R-group. Default 2.
    max_steps (int): Maximum RL training steps. Default 50.
Return:
    status (str): 'success' or 'error'
    msg (str): Descriptive message
    save_smiles_file (str): Path to the saved CSV with optimized molecules
    output_smiles_list (List[str]): List of optimized SMILES

Scaffold File Preparation

The tool requires a .smi file (one scaffold SMILES per line). Prepare it before calling:

# Create scaffold file
scaffold_smiles = "c1ccc([*:1])cc1C(=O)N[*:2]"
with open("scaffolds.smi", "w") as f:
    f.write(scaffold_smiles + "\n")

# Then call the tool
response = await client.session.call_tool(
    "libinvent_rgroup_optimization",
    arguments={
        "scaffolds_file": "scaffolds.smi",
        "target_smiles": "",
        "similarity_weight": 0.0,
        "qed_weight": 0.4,
        "mw_weight": 0.2,
        "mw_low": 300,
        "mw_high": 500,
        "logp_weight": 0.1,
        "logp_low": 1,
        "logp_high": 4,
        "tpsa_weight": 0.1,
        "tpsa_low": 40,
        "tpsa_high": 120,
        "rgroup_mw_weight": 0.1,
        "rgroup_mw_min": 15,
        "rgroup_mw_max": 150,
        "rgroup_rings_weight": 0.1,
        "rgroup_rings_min": 0,
        "rgroup_rings_max": 2,
        "max_steps": 50
    }
)

R-Group Constraint Guide

Important Notes

1. Scaffold file must exist before calling this tool. The tool checks for file existence and will error if the file is not found.
2. target_smiles and similarity_weight work together. Both must be set for similarity scoring to activate. If target_smiles='' or similarity_weight=0.0, similarity scoring is disabled.
3. The output CSV column name is SMILES (uppercase).
4. This tool runs RL training and takes minutes, not seconds.

⚠ Mandatory Generation Count Verification (L3 Principle 11)

actual_count = len(result["output_smiles_list"])