[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6XFPSG0T0JZRHFZFMFW5271":3,"skill-same:01M6XFPSG0T0JZRHFZFMFW5271":88},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":28,"publishedAt":49,"linkOnly":50,"markdownContent":51,"markdownBody":52,"frontMatter":53,"refName":62,"commitSha":63,"skillDirectory":64,"forksCount":14,"repositoryHtmlUrl":65,"repositoryHomepageUrl":6,"ownerHtmlUrl":66,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":67,"install":68,"resources":81},"01M6XFPSG0T0JZRHFZFMFW5271","multi-objective-optimization",null,"Pareto-aware molecular design balancing multiple ADMET properties simultaneously. Based on MultiMol (Yu 2025) and MOLLM (Ran 2025).","fadrienne\u002FMyGitHub","fadrienne","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F197874571?v=4","01M6X64V8VWCRSMFZ4EPS2BWXG","Francesca Adrienne","01M6X64VD5MW4F9PHDWQKS7ZAF",0,"MIT","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fcoding\u002Fmulti-objective-optimization\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000032","设计师","PROFESSION","适用职业",[29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48],"multi objective optimization","Optimization","objective","multi","synthetic","multi-objective","molecular-design","ADMET","pareto","2025","drug-discovery","design","molecular","numpy","properties","coding","discovery","rdkit","pypi","pandas","2026-11-10 10:12:30",false,"---\nname: multi-objective-optimization\ndescription: Pareto-aware molecular design balancing multiple ADMET properties simultaneously. Based on MultiMol (Yu 2025) and MOLLM (Ran 2025).\ncategory: coding\nlicense: MIT\nmetadata:\n    skill-author: Synthetic Sciences\nversion: 1.0.0\ntags: [drug-discovery, multi-objective, Pareto, optimization, molecular-design]\ndependencies: [\"rdkit-pypi\", \"numpy\", \"pandas\"]\n---\n\n# Multi-Objective Molecular Optimization\n\n## Overview\n\nReal 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.\n\nBased on:\n- **MultiMol** (Yu et al., 2025): 82.3% multi-objective success rate with generate-then-rank\n- **MOLLM** (Ran et al., 2025): LLMs as genetic operators for multi-objective molecular design\n- **DrugR** (Liu et al., 2026): Multi-granular reward balancing across property groups\n\n## When to Use This Skill\n\n- **\"Improve potency while keeping hERG safe\"** — classic multi-objective lead optimization\n- **Balancing ADMET tradeoffs** — LogP vs solubility, BBB penetration vs peripheral safety\n- **Pareto analysis** — identify which candidates best balance competing objectives\n- **Property-constrained generation** — generate molecules within a defined property box\n\n**Do NOT use this skill for:**\n- Single-property optimization (use `molecular-optimization`)\n- Property prediction without optimization (use `admet-prediction`)\n\n### Related Skills\n- **molecular-optimization**: Single-objective iterative optimization\n- **admet-prediction**: Compute properties used as objectives\n- **admet-reasoning**: Understand why properties need improvement\n\n## Installation\n\n```bash\npip install rdkit-pypi numpy pandas\n```\n\n### Optional\n\n```bash\npip install matplotlib  # For Pareto front visualization\n```\n\n## Core Workflows\n\n### 1. Multi-Objective Optimization\n\n```bash\npython scripts\u002Fpareto_optimize.py \\\n    --smiles \"c1ccc(NC(=O)c2ccccc2Cl)cc1\" \\\n    --objectives \"LogP:minimize:3.0,QED:maximize:0.5,TPSA:range:20:130\" \\\n    --candidates 16 \\\n    --output pareto_results.json\n```\n\n### 2. Pareto Analysis of Existing Candidates\n\n```bash\npython scripts\u002Fpareto_optimize.py \\\n    --input candidates.csv \\\n    --objectives \"LogP:minimize:3.0,QED:maximize:0.5\" \\\n    --mode analyze \\\n    --output pareto_front.json\n```\n\n### 3. Property Radar Plot\n\n```bash\npython scripts\u002Fproperty_radar.py \\\n    --reference \"original_smiles\" \\\n    --candidates optimized.csv \\\n    --output radar.png\n```\n\n## Script Reference\n\n| Script | Purpose | Key Outputs |\n|--------|---------|-------------|\n| `pareto_optimize.py` | Generate and rank candidates by Pareto dominance | JSON with Pareto front, dominated set, objective scores |\n| `property_radar.py` | Multi-property radar visualization | PNG radar plot comparing candidates |\n","# Multi-Objective Molecular Optimization\n\n## Overview\n\nReal 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.\n\nBased on:\n- **MultiMol** (Yu et al., 2025): 82.3% multi-objective success rate with generate-then-rank\n- **MOLLM** (Ran et al., 2025): LLMs as genetic operators for multi-objective molecular design\n- **DrugR** (Liu et al., 2026): Multi-granular reward balancing across property groups\n\n## When to Use This Skill\n\n- **\"Improve potency while keeping hERG safe\"** — classic multi-objective lead optimization\n- **Balancing ADMET tradeoffs** — LogP vs solubility, BBB penetration vs peripheral safety\n- **Pareto analysis** — identify which candidates best balance competing objectives\n- **Property-constrained generation** — generate molecules within a defined property box\n\n**Do NOT use this skill for:**\n- Single-property optimization (use `molecular-optimization`)\n- Property prediction without optimization (use `admet-prediction`)\n\n### Related Skills\n- **molecular-optimization**: Single-objective iterative optimization\n- **admet-prediction**: Compute properties used as objectives\n- **admet-reasoning**: Understand why properties need improvement\n\n## Installation\n\n```bash\npip install rdkit-pypi numpy pandas\n```\n\n### Optional\n\n```bash\npip install matplotlib  # For Pareto front visualization\n```\n\n## Core Workflows\n\n### 1. Multi-Objective Optimization\n\n```bash\npython scripts\u002Fpareto_optimize.py \\\n    --smiles \"c1ccc(NC(=O)c2ccccc2Cl)cc1\" \\\n    --objectives \"LogP:minimize:3.0,QED:maximize:0.5,TPSA:range:20:130\" \\\n    --candidates 16 \\\n    --output pareto_results.json\n```\n\n### 2. Pareto Analysis of Existing Candidates\n\n```bash\npython scripts\u002Fpareto_optimize.py \\\n    --input candidates.csv \\\n    --objectives \"LogP:minimize:3.0,QED:maximize:0.5\" \\\n    --mode analyze \\\n    --output pareto_front.json\n```\n\n### 3. Property Radar Plot\n\n```bash\npython scripts\u002Fproperty_radar.py \\\n    --reference \"original_smiles\" \\\n    --candidates optimized.csv \\\n    --output radar.png\n```\n\n## Script Reference\n\n| Script | Purpose | Key Outputs |\n|--------|---------|-------------|\n| `pareto_optimize.py` | Generate and rank candidates by Pareto dominance | JSON with Pareto front, dominated set, objective scores |\n| `property_radar.py` | Multi-property radar visualization | PNG radar plot comparing candidates |\n",{"name":5,"tags":54,"license":15,"version":57,"category":44,"metadata":58,"description":7,"dependencies":60},[39,34,55,56,35],"Pareto","optimization","1.0.0",{"skill-author":59},"Synthetic Sciences",[61,42,48],"rdkit-pypi","main","4aa39efd1cdab0b4657ecee16c92a92011a5b10f","openscience\u002Fbackend\u002Fcli\u002Fskills\u002Fcoding\u002Fmulti-objective-optimization","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub","https:\u002F\u002Fgithub.com\u002Ffadrienne","2026-10-28 00:43:09",{"source":8,"skillName":5,"commands":69,"prompt":79,"zipAvailable":80},[70,73,76],{"tool":71,"command":72},"npx","npx skills add fadrienne\u002FMyGitHub --skill multi-objective-optimization",{"tool":74,"command":75},"bunx","bunx skills add fadrienne\u002FMyGitHub --skill multi-objective-optimization",{"tool":77,"command":78},"pnpm","pnpm dlx skills add fadrienne\u002FMyGitHub --skill multi-objective-optimization","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fcoding\u002Fmulti-objective-optimization\u002FSKILL.md",true,[82],{"path":83,"resourceType":84,"resourceTypeDesc":85,"sizeBytes":86,"sourceHtmlUrl":87},"scripts\u002Fpareto_optimize.py","SCRIPT","脚本",9584,"https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fopenscience\u002Fbackend\u002Fcli\u002Fskills\u002Fcoding\u002Fmulti-objective-optimization\u002Fscripts\u002Fpareto_optimize.py",[89,118,145,177,204,231],{"skillNo":90,"name":91,"nameZh":6,"description":92,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":93,"categories":94,"keywords":98,"publishedAt":49},"01M7167B28PK20HP7R7ZED2DY0","statistical-power","Sample size calculation, power analysis, and effect size conversion for scientific studies. Use BEFORE data collection (a priori) to determine required N, or after (post-hoc) to compute achieved power. Covers parametric tests (t-test, ANOVA, chi-square, proportions, correlation, regression), simulation-based power for complex\u002Fclustered\u002Fnested designs, and power curves. Use this skill when someone asks 'how many samples do I need', 'what is my power', 'what effect size can I detect', or any variant. Pairs with \u002Fexperimental-design (choose design first, then compute power) and \u002Fstatistical-analysis (analyze data after collection).","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-power\u002FSKILL.md",[95],{"categoryNo":96,"categoryName":97,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[99,100,101,102,103,104,40,105,106,107,108,109,110,111,112,113,114,115,116,117],"power","statistical power","effect","scipy","regression","choose","uv","when","Bash","collection","analysis","calculation","ANOVA","conversion","analyze","t-test","chi-square","simulation","sample",{"skillNo":119,"name":120,"nameZh":6,"description":121,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":122,"categories":123,"keywords":127,"publishedAt":49},"01M71677EBCM8EPCE9BZCK8505","statistical-analysis","Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fstatistical-analysis\u002FSKILL.md",[124],{"categoryNo":125,"categoryName":126,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",[109,128,99,129,130,131,106,132,133,134,135,136,137,138,139,140,141,142,143,144],"statistical analysis","academic research","implementing","selection","you","reporting","data","for","tests","use","with","test","research","results","statsmodels","models","help",{"skillNo":146,"name":147,"nameZh":6,"description":148,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":149,"categories":150,"keywords":158,"publishedAt":49},"01M71673R93XAHFH81DBNVNG7H","scientific-writing","Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (2) convert to flowing prose. IMRAD structure, citations (APA\u002FAMA\u002FVancouver), figures\u002Ftables, reporting guidelines (CONSORT\u002FSTROBE\u002FPRISMA), for research papers and journal submissions.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-writing\u002FSKILL.md",[151,154,155],{"categoryNo":152,"categoryName":153,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":96,"categoryName":97,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":156,"categoryName":157,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000036","内容创作者",[159,160,161,162,163,164,165,166,167,168,107,169,170,171,172,173,174,175,133,176],"writing","scientific","scientific writing","Citations","deep","deep research","never","papers","journal","false","api","prisma","structure","openrouter","Outlines","core skill","read","features",{"skillNo":178,"name":179,"nameZh":6,"description":180,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":181,"categories":182,"keywords":187,"publishedAt":49},"01M7166ZPKXBF4EBPHVTJPQBB1","scientific-visualization","Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib\u002Fseaborn\u002Fplotly with publication styles. For quick exploration use seaborn or plotly directly.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fscientific-visualization\u002FSKILL.md",[183,186],{"categoryNo":184,"categoryName":185,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化",{"categoryNo":96,"categoryName":97,"taxonomyType":21,"taxonomyTypeDesc":22},[188,160,189,190,191,192,193,194,195,167,106,196,197,198,199,200,201,202,135,203],"visualization","scientific visualization","error","ready","quick","exploration","meta-skill","Meta","safe","plotly","annotations","formatting","Publication","figures","panel","submission",{"skillNo":205,"name":206,"nameZh":6,"description":207,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":208,"categories":209,"keywords":216,"publishedAt":49},"01M7166VXJWQSSC3RQZP60WBGR","research-code","Debug, profile, review, and restructure Python research and analysis code. Use when analysis scripts crash, produce wrong results, run too slowly, or need a correctness review. Covers: reading tracebacks and diagnosing root causes, hypothesis-driven debugging, reviewing code for common statistical\u002Fnumerical errors, profiling data pipelines, improving reproducibility (seeds, environments, determinism), and structuring analysis scripts for sharing. Distinct from \u002Fexploratory-data-analysis (which understands data) and \u002Fstatistical-analysis (which chooses and runs tests) — this skill focuses on the code itself.","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fresearch-code\u002FSKILL.md",[210,213],{"categoryNo":211,"categoryName":212,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":214,"categoryName":215,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000030","开发工程师",[217,218,140,219,220,221,222,105,223,106,107,109,224,225,226,227,228,229,175,230],"code","research code","which","memory","data-analysis","errors","debugging","common","review","data pipelines","run","scripts","line","write",{"skillNo":232,"name":233,"nameZh":6,"description":234,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":235,"licenseFileUrl":6,"sourceHtmlUrl":236,"categories":237,"keywords":244,"publishedAt":49},"01M7166CF5HSR7SVZYZFWTDX55","markdown-mermaid-writing","Comprehensive markdown and Mermaid diagram writing skill. Use when creating any scientific document, report, analysis, or visualization. Establishes text-based diagrams as the default documentation standard with full style guides (markdown + mermaid), 24 diagram type references, and 9 document templates.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Ffadrienne\u002FMyGitHub\u002Fblob\u002Fmain\u002Fscientific-agent-skills\u002Fskills\u002Fmarkdown-mermaid-writing\u002FSKILL.md",[238,241,242,243],{"categoryNo":239,"categoryName":240,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":152,"categoryName":153,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":96,"categoryName":97,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":156,"categoryName":157,"taxonomyType":26,"taxonomyTypeDesc":27},[159,245,246,247,248,249,188,250,251,106,107,109,252,253,254,255,256,257,160,175],"markdown mermaid writing","mermaid","markdown","community","diagrams","project","default","team","guides","text","documentation","references","diagram"]