tooluniverse-drug-target-validation

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. Use when users ask about target validation, druggability assessment, target prioritization, or "is X a good drug target for Y?"

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Install skill "tooluniverse-drug-target-validation" with this command: npx skills add mims-harvard/tooluniverse/mims-harvard-tooluniverse-tooluniverse-drug-target-validation

Drug Target Validation Pipeline

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 - 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. English-first queries - Always use English terms in tool calls; respond in user's language

When to Use

Apply when users ask about:

  • "Is [target] a good drug target for [disease]?"
  • Target validation, druggability assessment, or target prioritization
  • Safety risks of modulating a target
  • Chemical starting points for target validation
  • GO/NO-GO recommendation for a target

Not for (use other skills): general target biology (tooluniverse-target-research), drug compound profiling (tooluniverse-drug-research), variant interpretation (tooluniverse-variant-interpretation), disease research (tooluniverse-disease-research).

Input Parameters

ParameterRequiredDescriptionExample
targetYesGene symbol, protein name, or UniProt IDEGFR, P00533
diseaseNoDisease/indication for contextNon-small cell lung cancer
modalityNoPreferred therapeutic modalitysmall molecule, antibody, PROTAC

Reference Files

  • SCORING_CRITERIA.md - Detailed scoring matrices, evidence grading, priority tiers, score calculation
  • REPORT_TEMPLATE.md - Full report template, completeness checklist, section format examples
  • TOOL_REFERENCE.md - Verified tool parameters, known corrections, fallback chains, modality-specific guidance, phase-by-phase tool lists
  • QUICK_START.md - Quick start guide

Scoring Overview

Total: 0-100 points across 5 dimensions (details in SCORING_CRITERIA.md):

DimensionMaxSub-dimensions
Disease Association30Genetic (10) + Literature (10) + Pathway (10)
Druggability25Structure (10) + Chemical matter (10) + Target class (5)
Safety Profile20Expression (5) + Genetic validation (10) + ADRs (5)
Clinical Precedent15Based on highest clinical stage achieved
Validation Evidence10Functional studies (5) + Disease models (5)

Priority Tiers: 80-100 = Tier 1 (GO) | 60-79 = Tier 2 (CONDITIONAL GO) | 40-59 = Tier 3 (CAUTION) | 0-39 = Tier 4 (NO-GO)

Evidence Grades: T1 (clinical proof) > T2 (functional studies) > T3 (associations) > T4 (predictions)


Pipeline Phases

Phase 0: Target Disambiguation (ALWAYS FIRST)

Resolve target to ALL identifiers before any analysis.

Steps:

  1. MyGene_query_genes - Get initial IDs (Ensembl, UniProt, Entrez)
  2. ensembl_lookup_gene - Get versioned Ensembl ID (species="homo_sapiens" REQUIRED)
  3. ensembl_get_xrefs - Cross-references (HGNC, etc.)
  4. OpenTargets_get_target_id_description_by_name - Verify OT target
  5. ChEMBL_search_targets - Get ChEMBL target ID
  6. UniProt_get_function_by_accession - Function summary (returns list of strings)
  7. UniProt_get_alternative_names_by_accession - Collision detection

Output: Table of verified identifiers (Gene Symbol, Ensembl, UniProt, Entrez, ChEMBL, HGNC) plus protein function and target class.

Phase 1: Disease Association (0-30 pts)

Quantify target-disease association from genetic, literature, and pathway evidence.

Key tools:

  • OpenTargets_get_diseases_phenotypes_by_target_ensembl - Disease associations
  • OpenTargets_target_disease_evidence - Detailed evidence (needs efoId + ensemblId)
  • OpenTargets_get_evidence_by_datasource - Evidence by data source
  • gwas_get_snps_for_gene / gwas_search_studies - GWAS evidence
  • gnomad_get_gene_constraints - Genetic constraint (pLI, LOEUF)
  • PubMed_search_articles - Literature (returns plain list of dicts)
  • OpenTargets_get_publications_by_target_ensemblID - OT publications (uses entityId)

Phase 2: Druggability (0-25 pts)

Assess whether the target is amenable to therapeutic intervention.

Key tools:

  • OpenTargets_get_target_tractability_by_ensemblID - Tractability (SM, AB, PR, OC)
  • OpenTargets_get_target_classes_by_ensemblID - Target classification
  • Pharos_get_target - TDL: Tclin > Tchem > Tbio > Tdark
  • DGIdb_get_gene_druggability - Druggability categories
  • alphafold_get_prediction (param: qualifier) / alphafold_get_summary
  • ProteinsPlus_predict_binding_sites - Pocket detection
  • OpenTargets_get_chemical_probes_by_target_ensemblID - Chemical probes
  • OpenTargets_get_target_enabling_packages_by_ensemblID - TEPs

Phase 3: Chemical Matter (feeds Phase 2 scoring)

Identify existing chemical starting points for target validation.

Key tools:

  • ChEMBL_search_targets + ChEMBL_get_target_activities - Bioactivity data (note: target_chembl_id__exact with double underscore)
  • BindingDB_get_ligands_by_uniprot - Binding data (affinity in nM)
  • PubChem_search_assays_by_target_gene + PubChem_get_assay_active_compounds - HTS data
  • OpenTargets_get_associated_drugs_by_target_ensemblID - Known drugs (size REQUIRED)
  • ChEMBL_search_mechanisms - Drug mechanisms
  • DGIdb_get_gene_info - Drug-gene interactions

Phase 4: Clinical Precedent (0-15 pts)

Assess clinical validation from approved drugs and clinical trials.

Key tools:

  • FDA_get_mechanism_of_action_by_drug_name / FDA_get_indications_by_drug_name
  • drugbank_get_targets_by_drug_name_or_drugbank_id (ALL params required: query, case_sensitive, exact_match, limit)
  • search_clinical_trials (query_term REQUIRED)
  • OpenTargets_get_drug_warnings_by_chemblId / OpenTargets_get_drug_adverse_events_by_chemblId

Phase 5: Safety (0-20 pts)

Identify safety risks from expression, genetics, and known adverse events.

Key tools:

  • OpenTargets_get_target_safety_profile_by_ensemblID - Safety liabilities
  • GTEx_get_median_gene_expression - Tissue expression (operation="median" REQUIRED)
  • HPA_search_genes_by_query / HPA_get_comprehensive_gene_details_by_ensembl_id
  • OpenTargets_get_biological_mouse_models_by_ensemblID - KO phenotypes
  • FDA_get_adverse_reactions_by_drug_name / FDA_get_boxed_warning_info_by_drug_name
  • OpenTargets_get_target_homologues_by_ensemblID - Paralog risks

Critical tissues to check: heart, liver, kidney, brain, bone marrow.

Phase 6: Pathway Context

Understand the target's role in biological networks and disease pathways.

Key tools:

  • Reactome_map_uniprot_to_pathways (param: id, NOT uniprot_id)
  • STRING_get_protein_interactions (param: protein_ids as array, species=9606)
  • intact_get_interactions - Experimental PPI
  • OpenTargets_get_target_gene_ontology_by_ensemblID - GO terms
  • STRING_functional_enrichment - Enrichment analysis

Assess: pathway redundancy, compensation risk, feedback loops.

Phase 7: Validation Evidence (0-10 pts)

Assess existing functional validation data.

Key tools:

  • DepMap_get_gene_dependencies - Essentiality (score < -0.5 = essential)
  • PubMed_search_articles - Search for CRISPR/siRNA/knockout studies
  • CTD_get_gene_diseases - Gene-disease associations

Phase 8: Structural Insights

Leverage structural biology for druggability and mechanism understanding.

Key tools:

  • UniProt_get_entry_by_accession - Extract PDB cross-references
  • get_protein_metadata_by_pdb_id / pdbe_get_entry_summary / pdbe_get_entry_quality
  • alphafold_get_prediction / alphafold_get_summary - pLDDT confidence
  • ProteinsPlus_predict_binding_sites - Druggable pockets
  • InterPro_get_protein_domains / InterPro_get_domain_details - Domain architecture

Phase 9: Literature Deep Dive

Comprehensive collision-aware literature analysis.

Steps:

  1. Collision detection: Search "{gene_symbol}"[Title] in PubMed; if >20% off-topic, add filters (AND protein OR gene OR receptor)
  2. Publication metrics: Total count, 5-year trend, drug-focused subset
  3. Key reviews: review[pt] filter in PubMed
  4. Citation metrics: openalex_search_works for impact data
  5. Broader coverage: EuropePMC_search_articles

Phase 10: Validation Roadmap (Synthesis)

Synthesize all phases into actionable output:

  1. Target Validation Score (0-100) with component breakdown
  2. Priority Tier (1-4) assignment
  3. GO/NO-GO Recommendation with justification
  4. Recommended Validation Experiments
  5. Tool Compounds for Testing
  6. Biomarker Strategy
  7. Key Risks and Mitigations

Report Output

Create file: [TARGET]_[DISEASE]_validation_report.md

Use the full template from REPORT_TEMPLATE.md. Key sections:

  • Executive Summary (score, tier, recommendation, key findings, critical risks)
  • Validation Scorecard (all 12 sub-scores with evidence)
  • Sections 1-14 covering each phase
  • Completeness Checklist (mandatory before finalizing)

Complete the Completeness Checklist (in REPORT_TEMPLATE.md) before finalizing to verify all phases were covered, all scores justified, and negative results documented.

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