molclaw-mol-opt-physchem

name	molclaw-mol-opt-physchem
description	Integrating molecular property calculation tools with the reasoning capabilities of Large Language Models (LLMs) to optimize key physicochemical properties of drug molecules, such as LogP, QED, and solubility.
license	MIT license
metadata	{"skill-author":"PJLab"}

Molecule Optimization for Physicochemical Properties

step 1

Use skill molclaw-admet to calculate multiple physicochemical properties for the source molecule and generate a summary report, with special emphasis on the properties specified in the user query.

step 1.5 — RL-Assisted Generation (Recommended When Applicable)

If the optimization target can be expressed as numeric property ranges (e.g., "lower LogP to 1-3", "improve QED above 0.7", "MW between 300-500"), use reinvent_similarity_optimization to generate a batch of optimized candidates BEFORE applying LLM reasoning:

response = await client.session.call_tool(
    "reinvent_similarity_optimization",
    arguments={
        "target_smiles": source_smiles,
        "similarity_weight": 0.5,        # Keep similarity to source
        "fp_radius": 2,
        "qed_weight": 0.3,               # Set >0 if QED is a target
        "mmp_weight": 0.0,
        "mw_weight": mw_wt,              # Set >0 if MW is a target
        "mw_low": target_mw_low, "mw_high": target_mw_high,
        "logp_weight": logp_wt,           # Set >0 if LogP is a target
        "logp_low": target_logp_low, "logp_high": target_logp_high,
        "tpsa_weight": tpsa_wt,           # Set >0 if TPSA/solubility is a target
        "tpsa_low": target_tpsa_low, "tpsa_high": target_tpsa_high,
        "max_steps": 100
    }
)
rl_candidates = client.parse_result(response)["output_smiles_list"]

Select the top candidates from the RL output (by ADMET evaluation in step 4), then optionally apply LLM reasoning (step 2) for further fine-tuning on the best RL candidate. This combination is more effective than LLM-only optimization because the RL optimizer can systematically explore the property landscape.

If the optimization target is qualitative (e.g., "reduce metabolic liability", "improve oral bioavailability") and cannot be directly expressed as a numeric scoring function, proceed directly to step 2 (LLM reasoning).

step 2

Based on the molecular property analysis report generated in step 1 and the following prompt, leverage the reasoning capabilities of the Large Language Model (LLM) to generate an optimized molecule from the source molecule, while providing a detailed rationale for the optimization.

---

Role Definition

You are an expert medicinal chemist with 15+ years of experience in lead optimization and drug design. You specialize in optimizing physicochemical properties of small molecule drugs while maintaining structural integrity and synthetic feasibility.

Task Overview

Your task is to optimize the source molecule to improve specific physicochemical properties (LogP, QED, or Solubility) while following drug discovery best practices. You must provide:

1. A structured optimization reasoning process
2. The final optimized molecule in SMILES format
3. Clear justification for each modification

Background Knowledge & Guidelines

1. Lipinski's Rule of Five (RO5) - Fundamental Drug-likeness Criteria

Property	Optimal Range	Impact
Molecular Weight (MW)	< 500 Da	Higher MW reduces oral bioavailability
LogP (lipophilicity)	-0.4 to 5.0	Affects membrane permeability & solubility
Hydrogen Bond Donors (HBD)	≤ 5	Too many reduces cell permeability
Hydrogen Bond Acceptors (HBA)	≤ 10	Too many reduces absorption
Rotatable Bonds	≤ 10	Affects molecular flexibility & bioavailability

2. QED (Quantitative Estimate of Drug-likeness)

• Score Range: 0 to 1 (higher is better, >0.67 is desirable)
• Key Factors: MW, LogP, HBA, HBD, TPSA, rotatable bonds, aromatic rings
• Optimization Strategy: Balance all factors rather than maximizing single property

3. Solubility Optimization Principles

Strategy	Chemical Modification	Effect
Add polar groups	-OH, -NH₂, -COOH, -SO₃H	↑ Water solubility, ↓ LogP
Reduce lipophilicity	Remove aromatic rings, alkyl chains	↑ Solubility
Introduce ionizable groups	Amines, carboxylic acids	↑ Aqueous solubility at physiological pH
Reduce molecular weight	Remove non-essential substituents	↑ Solubility
Disrupt crystal packing	Add branching, reduce symmetry	↑ Solubility

4. LogP Optimization Principles

Goal	Strategy	Example Modifications
↓ LogP (more hydrophilic)	Add polar groups, remove hydrophobic groups	Replace -CH₃ with -OH, add -NH₂
↑ LogP (more lipophilic)	Add aromatic rings, alkyl chains, halogens	Add -Ph, -Cl, -CF₃, extend alkyl chains

Optimization Workflow (Must Follow)

Phase A: Analyze Source Molecule

• Review the property values calculated by molclaw-admet in step 1
• Locate problematic structural features
• Identify modification sites that won't disrupt core pharmacophore
• Stereochemistry check: If the source SMILES contains stereochemical markers (@, @@, /, \), list all stereocenters and cis/trans bonds. These MUST be preserved unchanged in the output unless the modification directly involves that stereocenter.

Phase B: Design Modification Strategy

• Select 1-2 specific structural changes
• Justify each change with medicinal chemistry rationale
• Consider synthetic feasibility

Phase C: Validate Optimized Molecule

• Ensure SMILES is chemically valid
• Verify modifications align with target property improvement
• Check no critical drug-likeness violations introduced
• Stereochemistry verify: Confirm all original @, @@, /, \ markers are present and unchanged (unless the stereo center was intentionally modified)

Optimization Examples

Example 1: Solubility Optimization — Bioisosteric Ring Replacement (Benzene → Pyridine)

Source Molecule: c1ccc(-c2ccc(NC(=O)C)cc2)cc1 (a biphenyl acetamide)

{
  "Analysis": "Poor solubility driven by biphenyl core. LogP ~2.9. High planarity and symmetry promote crystal packing.",
  "OptimizationStrategy": "Replace one phenyl with pyridine: 'c1ccccc1' → 'c1ccncc1'. Adds HBA, reduces LogP ~0.4, disrupts symmetry.",
  "Final Target Molecule": "c1ccnc(-c2ccc(NC(=O)C)cc2)c1",
  "ExpectedImprovement": "LogP ~2.9 → ~2.5. Solubility improves via added HBA and broken symmetry. Shape preserved.",
  "Confidence": "High"
}

Example 2: Failure Case — Invalid SMILES and Correction

Source Molecule: c1ccc2c(c1)cc(NC(=O)C)c1ccccc12 (an amino-fluorene amide) Goal: Lower LogP

❌ First Attempt (FAILED):

{
  "Analysis": "LogP high (~3.5) due to fluorene tricyclic system.",
  "OptimizationStrategy": "Add -OH to fluorene ring and insert ring nitrogen.",
  "Final Target Molecule": "c1ccc2c(c1)cc(NC(=O)C)c1cc(O)ccc12N",
  "ExpectedImprovement": "LogP decrease expected.",
  "Confidence": "Medium"
}

Validation: ❌ INVALID — appending N outside ring closure breaks the bicyclic ring numbering. Two simultaneous changes compounded error risk.

✅ Second Attempt (CORRECTED):

{
  "Analysis": "LogP high (~3.5) due to fluorene tricyclic system. Keep scaffold intact.",
  "OptimizationStrategy": "Single change only: add -OH to first phenyl ring. Insert '(O)' within ring traversal path.",
  "Final Target Molecule": "c1cc(O)c2c(c1)cc(NC(=O)C)c1ccccc12",
  "ExpectedImprovement": "LogP ~3.5 → ~2.9. Adds 1 HBD + 1 HBA. Scaffold preserved.",
  "Confidence": "High"
}

Lessons: (1) One change at a time for fused ring systems. (2) Never append atoms outside ring closures — use parentheses () within the ring path. (3) Prefer the simpler edit.

Critical Constraints & Warnings

⚠️ DO NOT:

• Break core pharmacophore structures essential for activity
• Create chemically unstable or impossible structures
• Introduce toxic functional groups (nitro-aromatics, reactive epoxides, etc.)
• Generate SMILES with syntax errors
• Make changes that violate multiple RO5 criteria simultaneously
• Drop or alter stereochemical markers (@, @@, /, \) from the source SMILES unless the modification directly targets that stereocenter

✅ DO:

• Maintain scaffold integrity when possible
• Use bioisosteric replacements when removing functional groups
• Consider synthetic accessibility
• Provide clear rationale for each modification
• Ensure output SMILES is valid and parsable
• Preserve all stereochemistry from the source molecule

Output Format Requirement

Your response MUST be valid JSON format:

{
  "Analysis": "Brief analysis of source molecule properties and issues",
  "OptimizationStrategy": "Step-by-step modification plan with rationale",
  "Final Target Molecule": "Valid SMILES string of optimized molecule",
  "ExpectedImprovement": "Description of expected property improvement",
  "Confidence": "High/Medium/Low based on modification complexity"
}

---

step 3

Utilize skill molclaw-smiles-valid-check to validate the structural integrity of the molecule generated in step 2. If the molecule is invalid, analyze the root cause, reflect on the error, and re-execute step 2. Do NOT repeat the same modification that produced the invalid SMILES. If valid, proceed directly to step 4.

step 4

Utilize skill molclaw-admet to calculate multiple physicochemical properties for the newly generated molecule and rigorously compare them with the source molecule. The primary objective is to maximize the improvement of target properties, aiming for substantial gains rather than marginal adjustments.

Note: If the user specifies particular optimization goals, prioritize the user's specified goals over the default targets listed below.

Strict Improvement Thresholds (Default Targets):

• LogP: Change ≥ 2.5 in the desired direction (significantly stricter than previous standards).
• QED: Increase ≥ 0.5 or QED score reaches at least 0.9 (demanding a major leap in drug-likeness).
• Solubility (LogS): Increase ≥ 4.0 (requiring a drastic enhancement in solubility).

If the target properties fail to meet these elevated thresholds, conduct a deep root-cause analysis and critically reflect on the optimization strategy. You must re-execute step 2 with a fundamentally different approach. Under no circumstances should you repeat a strategy that previously failed to achieve these significant improvements.

⚠ Computation-First Principle (L3 Principle 13)

Property values used for optimization decisions (LogP, QED, solubility, ADMET) MUST come from tool computations (pred_mol_admet, calculate_mol_drug_chemistry, etc.), not from LLM chemical intuition. Each optimization rationale must cite the specific tool-computed value it aims to improve.

⚠ Data Integrity (L3 Principle 11)

Before writing any comparison table or optimization trajectory, verify each value against the actual tool return. Report exact tool-returned values, not rounded or estimated numbers.