Pareto-aware molecular design balancing multiple ADMET properties simultaneously. Based on MultiMol (Yu 2025) and MOLLM (Ran 2025).
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:
Do NOT use this skill for:
molecular-optimization)admet-prediction)pip install rdkit-pypi numpy pandaspip install matplotlib # For Pareto front visualizationpython 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.jsonpython scripts/pareto_optimize.py \
--input candidates.csv \
--objectives "LogP:minimize:3.0,QED:maximize:0.5" \
--mode analyze \
--output pareto_front.jsonpython scripts/property_radar.py \
--reference "original_smiles" \
--candidates optimized.csv \
--output radar.png| 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 |
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