Pareto-aware molecular design balancing multiple ADMET properties simultaneously. Based on MultiMol (Yu 2025) and MOLLM (Ran 2025).
56
65%
Does it follow best practices?
Run evals on this skill
Adds up to 20 points to the overall score
View guide
Passed
No findings from the security scan
Fix and improve this skill with Tessl
tessl review fix ./backend/cli/skills/coding/multi-objective-optimization/SKILL.mdReal 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 |
dc00e07
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.