multi-objective-optimization

# Multi-Objective Molecular Optimization ## Overview Real drug design is never single-objective. A useful molecule must simultaneously satisfy potency, selectivity, solubility, metabolic stability, and safety constraints. This skill implements Pareto-aware optimization that balances multiple properties without collapsing to a single weighted score. Based on: - **MultiMol** (Yu et al., 2025): 82.3% multi-objective success rate with generate-then-rank - **MOLLM** (Ran et al., 2025): LLMs as genetic operators for multi-objective molecular design - **DrugR** (Liu et al., 2026): Multi-granular reward balancing across property groups ## When to Use This Skill - **"Improve potency while keeping hERG safe"** — classic multi-objective lead optimization - **Balancing ADMET tradeoffs** — LogP vs solubility, BBB penetration vs peripheral safety - **Pareto analysis** — identify which candidates best balance competing objectives - **Property-constrained generation** — generate molecules within a defined property box **Do NOT use this skill for:** - Single-property optimization (use `molecular-optimization`) - Property prediction without optimization (use `admet-prediction`) ### Related Skills - **molecular-optimization**: Single-objective iterative optimization - **admet-prediction**: Compute properties used as objectives - **admet-reasoning**: Understand why properties need improvement ## Installation ```bash pip install rdkit-pypi numpy pandas ``` ### Optional ```bash pip install matplotlib # For Pareto front visualization ``` ## Core Workflows ### 1. Multi-Objective Optimization ```bash python scripts/pareto_optimize.py \ --smiles "c1ccc(NC(=O)c2ccccc2Cl)cc1" \ --objectives "LogP:minimize:3.0,QED:maximize:0.5,TPSA:range:20:130" \ --candidates 16 \ --output pareto_results.json ``` ### 2. Pareto Analysis of Existing Candidates ```bash python scripts/pareto_optimize.py \ --input candidates.csv \ --objectives "LogP:minimize:3.0,QED:maximize:0.5" \ --mode analyze \ --output pareto_front.json ``` ### 3. Property Radar Plot ```bash python scripts/property_radar.py \ --reference "original_smiles" \ --candidates optimized.csv \ --output radar.png ``` ## Script Reference | Script | Purpose | Key Outputs | |--------|---------|-------------| | `pareto_optimize.py` | Generate and rank candidates by Pareto dominance | JSON with Pareto front, dominated set, objective scores | | `property_radar.py` | Multi-property radar visualization | PNG radar plot comparing candidates |