Molclaw Similarity Optimization

Similarity-Guided RL Molecular Optimization (REINVENT4 Staged Learning)

When to Use This Tool

Use reinvent_similarity_optimization when you have a target molecule and want to generate derivatives that are similar to it while simultaneously optimizing specific physicochemical properties (QED, MW, LogP, TPSA, etc.). Unlike sampling-based tools (reinvent_mol2mol_sampling) that randomly sample then filter, this tool uses reinforcement learning (RL) to actively guide generation toward molecules that score well on a multi-component objective function.

Key difference from reinvent_mol2mol_sampling:

Use this tool when:

• The user specifies explicit property targets (e.g., "improve QED to >0.7", "keep MW between 300-500", "reduce LogP to 2-4")
• You need molecules that are similar to a reference AND satisfy multiple property constraints
• The task is "optimize this molecule's drug-likeness" or "generate analogs with better ADMET properties"
• mol2mol_sampling + filtering produces too few hits because the property constraints are tight

Do NOT use when:

• You just need diverse analogs without specific property targets (use mol2mol_sampling)
• You have no target molecule (use reinvent_denovo_sampling)
• You want to optimize R-groups on a fixed scaffold (use libinvent_rgroup_optimization)

Tool Description

Reinforcement-learning-based molecular optimization: generates molecules similar to a target while optimizing a weighted combination of physicochemical properties.
Args:
    target_smiles (str): Target molecule SMILES — the reference for similarity calculation
    similarity_weight (float): Weight for Tanimoto similarity to target. Higher = more emphasis on resembling the target. Default 0.7. Range: 0.0-1.0.
    fp_radius (int): Morgan fingerprint radius for similarity calculation. Options: 1 (ECFP2, loose), 2 (ECFP4, standard), 3 (ECFP6, strict). Default 3.
    qed_weight (float): Weight for QED (Quantitative Estimate of Drug-likeness). Default 0.3. Set >0 to optimize drug-likeness.
    mmp_weight (float): Weight for Matched Molecular Pair similarity. Default 0.0.
    mw_weight (float): Weight for molecular weight scoring. Default 0.0. Uses double_sigmoid transform.
    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. Default 0.0. Uses double_sigmoid transform.
    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. Uses double_sigmoid transform.
    tpsa_low (float): Lower bound of optimal TPSA range. Default 0.
    tpsa_high (float): Upper bound of optimal TPSA range. Default 140.
    max_steps (int): Maximum RL training steps. More steps = better convergence but slower. Default 100.
Return:
    status (str): 'success' or 'error'
    msg (str): Descriptive message
    save_smiles_file (str): Path to the saved CSV file containing generated molecules with their SMILES and scores
    output_smiles_list (List[str]): List of generated SMILES (the best molecules found during RL optimization)

Weight Configuration Recipes

Recipe 1: Pure Similarity Exploration

Generate molecules maximally similar to target, no property constraints.

arguments = {
    "target_smiles": target,
    "similarity_weight": 1.0,
    "fp_radius": 2,
    "qed_weight": 0.0,
    "mmp_weight": 0.0,
    "mw_weight": 0.0, "mw_low": 200, "mw_high": 500,
    "logp_weight": 0.0, "logp_low": 0, "logp_high": 5,
    "tpsa_weight": 0.0, "tpsa_low": 0, "tpsa_high": 140,
    "max_steps": 50
}

Recipe 2: Similarity + Drug-likeness (Most Common)

Balance target similarity with overall drug-likeness.

arguments = {
    "target_smiles": target,
    "similarity_weight": 0.6,
    "fp_radius": 3,
    "qed_weight": 0.4,
    "mmp_weight": 0.0,
    "mw_weight": 0.0, "mw_low": 200, "mw_high": 500,
    "logp_weight": 0.0, "logp_low": 0, "logp_high": 5,
    "tpsa_weight": 0.0, "tpsa_low": 0, "tpsa_high": 140,
    "max_steps": 100
}

Recipe 3: Multi-Property Constrained Optimization

Optimize toward specific property ranges while maintaining similarity.

arguments = {
    "target_smiles": target,
    "similarity_weight": 0.4,
    "fp_radius": 2,
    "qed_weight": 0.2,
    "mmp_weight": 0.0,
    "mw_weight": 0.15, "mw_low": 300, "mw_high": 450,   # Constrain MW
    "logp_weight": 0.15, "logp_low": 1, "logp_high": 3,  # Target LogP 1-3
    "tpsa_weight": 0.1, "tpsa_low": 60, "tpsa_high": 120, # Target TPSA
    "max_steps": 150
}

Recipe 4: Solubility-Focused Optimization

Improve aqueous solubility (lower LogP, higher TPSA) while retaining similarity.

arguments = {
    "target_smiles": target,
    "similarity_weight": 0.5,
    "fp_radius": 2,
    "qed_weight": 0.1,
    "mmp_weight": 0.0,
    "mw_weight": 0.1, "mw_low": 200, "mw_high": 500,
    "logp_weight": 0.2, "logp_low": 0, "logp_high": 2.5,  # Push LogP down
    "tpsa_weight": 0.1, "tpsa_low": 80, "tpsa_high": 140,  # Push TPSA up
    "max_steps": 100
}

How Scoring Works

Each scoring component uses a transform function (typically double_sigmoid) to convert the raw property value into a score between 0 and 1. Values within the [low, high] range score ~1.0; values outside score lower. The total score is a weighted sum of all component scores.

Weight guidelines:

• Weights do NOT need to sum to 1.0 — REINVENT4 normalizes internally
• A weight of 0.0 completely disables that component
• Higher weight = stronger optimization pressure for that property
• Start with 2-3 components, add more if needed

max_steps Selection Guide

Usage Example

response = await client.session.call_tool(
    "reinvent_similarity_optimization",
    arguments={
        "target_smiles": "COCCOC1=C(C=C2C(=C1)C(=NC=N2)NC3=CC=CC(=C3)C#C)OCCOC",
        "similarity_weight": 0.5,
        "fp_radius": 2,
        "qed_weight": 0.3,
        "mmp_weight": 0.0,
        "mw_weight": 0.1,
        "mw_low": 300,
        "mw_high": 500,
        "logp_weight": 0.1,
        "logp_low": 1,
        "logp_high": 3,
        "tpsa_weight": 0.0,
        "tpsa_low": 0,
        "tpsa_high": 140,
        "max_steps": 100
    }
)
result = client.parse_result(response)
optimized_smiles = result["output_smiles_list"]

Important Notes

1. This tool runs RL training, not just sampling. Each call takes minutes, not seconds. The max_steps parameter directly controls runtime.
2. Output molecules are the BEST found during RL, not random samples. They are pre-selected for high scores on the combined objective.
3. Combine with downstream evaluation. RL-optimized molecules still need docking validation, ADMET profiling, and other checks — the RL scoring function is an approximation, not a substitute for full evaluation.
4. Scaffold preservation is NOT guaranteed. Unlike mol2mol_sampling with scaffold_generic prior, this tool optimizes freely. If scaffold retention is needed, use mol2mol_sampling for generation and this tool's concepts (QED/property ranges) as post-filtering criteria.
5. The output CSV column name is SMILES (uppercase), not output_smiles.

⚠ Mandatory Generation Count Verification (L3 Principle 11)

After calling this tool, verify the output molecule count. RL optimization typically produces fewer unique molecules than the batch_size × max_steps suggests, because the diversity filter removes duplicates.

actual_count = len(result["output_smiles_list"])

⚠ Computation-First Principle (L3 Principle 13)

When using this tool for property optimization, the weight configuration MUST be grounded in tool-computed property values of the starting molecule (from pred_mol_admet or calculate_mol_basic_info), not from LLM estimates. For example, if the starting molecule's LogP is 3.2 (computed), set logp_high to a value lower than 3.2 to push LogP down.