AA
AAV vector design: capsid selection, promoter optimization, payload capacity.
FreedomIntelligence/OpenClaw-Medical-Skills查看
数据与复现
TO
tooluniverse-drug-target-validation
维护者 FreedomIntelligence · 最近更新 2026年4月1日
Comprehensive computational validation of drug targets for early-stage drug discovery. Evaluates targets across 10 dimensions (disambiguation, disease association, druggability, chemical matter, clinical precedent, safety, pathway context, validation evidence, structural insights, validation roadmap) using 60+ ToolUniverse tools. Produces a quantitative Target Validation Score (0-100) with GO/NO-GO recommendation. U….
原始来源
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/tooluniverse-drug-target-validation
- 维护者
- FreedomIntelligence
- 许可
- MIT
- 最近更新
- 2026年4月1日
技能摘要
来自 SKILL.md 的关键信息
核心说明
- Validate drug target hypotheses ，使用 multi-dimensional computational evidence before committing to wet-lab work. Produces quantitative Target Validation Score (0-100) ，支持 priority tier 分类 、 GO/NO-GO recommendation。
- KEY PRINCIPLES：1. Report-first approach - Create report file FIRST，then populate progressively 2. Target disambiguation FIRST - Resolve all identifiers before analysis 3. Evidence grading - Grade all evidence as T1 (experimental) to T4 (computational) 4. Disease-specific - Tailor analysis to disease context when provided 5. Modality-aware - Consider small molecule vs biologics tractability 6. Safety-first - Prominently flag safety concerns early 7. Quantitative scoring - Every dimension scored numerically (0-100 composite) 8. Negative results documented - "No data" is data；empty sections are failures 9. Source references - Every statement must cite tool/database 10. Completeness checklist - Mandatory section showing analysis coverage 11. English-first queries - Always use English terms in tool calls. Respond in user's language。
- mygene = tu.tools.MyGene_query_genes(query="EGFR"，species="human"，fields="symbol，name，ensembl.gene，uniprot.Swiss-Prot，entrezgene")。
原始文档
SKILL.md 摘录
When to Use This Skill
Apply when users:
- Ask "Is [target] a good drug target for [disease]?"
- Need target validation or druggability assessment
- Want to compare targets for drug discovery prioritization
- Ask about safety risks of modulating a target
- Need chemical starting points for target validation
- Ask about pathway context for a target
- Need a GO/NO-GO recommendation for a target
- Want a comprehensive target dossier for investment decisions
NOT for (use other skills instead):
- General target biology overview -> Use
tooluniverse-target-research - Drug compound profiling -> Use
tooluniverse-drug-research - Variant interpretation -> Use
tooluniverse-variant-interpretation - Disease research -> Use
tooluniverse-disease-research
Input Parameters
| Parameter | Required | Description | Example |
|---|---|---|---|
| target | Yes | Gene symbol, protein name, or UniProt ID | EGFR, P00533, Epidermal growth factor receptor |
| disease | No | Disease/indication for context | Non-small cell lung cancer, Pancreatic cancer |
| modality | No | Preferred therapeutic modality | small molecule, antibody, protein therapeutic, PROTAC |
Score Components (Total: 0-100)
Disease Association (0-30 points):
- Genetic evidence: 0-10 (GWAS, rare variants, somatic mutations)
- Literature evidence: 0-10 (publications, clinical studies)
- Pathway evidence: 0-10 (disease pathway involvement)
Druggability (0-25 points):
- Structural tractability: 0-10 (structure quality, binding pockets)
- Chemical matter: 0-10 (known compounds, bioactivity data)
- Target class: 0-5 (validated target family bonus)
Safety Profile (0-20 points):
- Tissue expression selectivity: 0-5 (expression in critical tissues)
- Genetic validation: 0-10 (knockout phenotypes, human genetics)
- Known adverse events: 0-5 (safety signals from modulators)
Clinical Precedent (0-15 points):
- Approved drugs: 15 (strong precedent, validated target)
- Clinical trials: 10 (moderate precedent)
- Preclinical compounds: 5 (weak precedent)
- None: 0 (novel target)
Validation Evidence (0-10 points):
- Functional studies: 0-5 (CRISPR, siRNA, biochemical)
- Disease models: 0-5 (animal models, patient data)
适用场景
- Ask "Is [target] good drug target ，用于 [disease]。
- Need target validation 或 druggability assessment。
- Want to compare targets ，用于 drug discovery prioriti。
不适用场景
- Do not rely on this catalog entry alone ，用于 installation 或 maintenance details。
相关技能
相关技能
AG
AgentD autonomous drug discovery: target identification, hit finding, ADMET optimization.
FreedomIntelligence/OpenClaw-Medical-Skills查看
Call HLA alleles from NGS data using OptiType, HLA-HD, or arcasHLA for immunogenomics applications. Use when determining…
FreedomIntelligence/OpenClaw-Medical-Skills查看
Query PharmGKB and CPIC for drug-gene interactions, pharmacogenomic annotations, and dosing guidelines. Use when predict…
FreedomIntelligence/OpenClaw-Medical-Skills查看