Data & ReproDrug Discovery & CheminformaticsFreedomIntelligence/OpenClaw-Medical-SkillsData & Reproduction
TO

tooluniverse-drug-target-validation

Maintainer FreedomIntelligence · Last updated April 1, 2026

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….

OpenClawNanoClawAnalysisReproductiontooluniverse-drug-target-validation🏥 medical & clinicaldrug safety & chemical biologycomprehensive

Original source

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/tooluniverse-drug-target-validation

Maintainer
FreedomIntelligence
License
MIT
Last updated
April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

  • Validate drug target hypotheses using multi-dimensional computational evidence before committing to wet-lab work. Produces a quantitative Target Validation Score (0-100) with priority tier classification and 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").

Source Doc

Excerpt From SKILL.md

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

ParameterRequiredDescriptionExample
targetYesGene symbol, protein name, or UniProt IDEGFR, P00533, Epidermal growth factor receptor
diseaseNoDisease/indication for contextNon-small cell lung cancer, Pancreatic cancer
modalityNoPreferred therapeutic modalitysmall 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)

Use cases

  • Ask "Is [target] a good drug target for [disease]?
  • Need target validation or druggability assessment.
  • Want to compare targets for drug discovery prioriti.

Not for

  • Do not rely on this catalog entry alone for installation or maintenance details.

Related skills

Related skills

Back to directory
AA
Data & ReproDrug Discovery & Cheminformatics

aav-vector-design-agent

AAV vector design: capsid selection, promoter optimization, payload capacity.

OpenClawNanoClawAnalysis
FreedomIntelligence/OpenClaw-Medical-SkillsView
AG
Data & ReproDrug Discovery & Cheminformatics

agentd-drug-discovery

AgentD autonomous drug discovery: target identification, hit finding, ADMET optimization.

OpenClawNanoClawAnalysis
FreedomIntelligence/OpenClaw-Medical-SkillsView
BI
Data & ReproDrug Discovery & Cheminformatics

bio-clinical-databases-hla-typing

Call HLA alleles from NGS data using OptiType, HLA-HD, or arcasHLA for immunogenomics applications. Use when determining HLA genotype for tr…

OpenClawNanoClawAnalysis
FreedomIntelligence/OpenClaw-Medical-SkillsView
BI
Data & ReproDrug Discovery & Cheminformatics

bio-clinical-databases-pharmacogenomics

Query PharmGKB and CPIC for drug-gene interactions, pharmacogenomic annotations, and dosing guidelines. Use when predicting drug response fr…

OpenClawNanoClawAnalysis
FreedomIntelligence/OpenClaw-Medical-SkillsView