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:

• Report-first approach - Create report file FIRST, then populate progressively

• Target disambiguation FIRST - Resolve all identifiers before analysis

• Evidence grading - Grade all evidence as T1 (experimental) to T4 (computational)

• Disease-specific - Tailor analysis to disease context when provided

• Modality-aware - Consider small molecule vs biologics tractability

• Safety-first - Prominently flag safety concerns early

• Quantitative scoring - Every dimension scored numerically (0-100 composite)

• Negative results documented - "No data" is data; empty sections are failures

• Source references - Every statement must cite tool/database

• Completeness checklist - Mandatory section showing analysis coverage

• English-first queries - Always use English terms in tool calls. Respond in user's language

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

Target Validation Scoring System

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)

Priority Tiers

Score Tier Recommendation

80-100 Tier 1 Highly validated - proceed with confidence

60-79 Tier 2 Good target - needs focused validation

40-59 Tier 3 Moderate risk - significant validation needed

0-39 Tier 4 High risk - consider alternatives

Evidence Grading System

Tier Symbol Criteria Examples

T1 [T1] Direct mechanistic, human clinical proof FDA-approved drug, crystal structure with mechanism, patient mutation

T2 [T2] Functional studies, model organism siRNA phenotype, mouse KO, biochemical assay, CRISPR screen

T3 [T3] Association, screen hits, computational GWAS hit, DepMap essentiality, expression correlation

T4 [T4] Mention, review, text-mined, predicted Review article, database annotation, AlphaFold prediction

Phase 0: Target Disambiguation & ID Resolution (ALWAYS FIRST)

Objective: Resolve target to ALL needed identifiers before any analysis.

Resolution Strategy

Step 1: Determine input type and get initial identifiers

If gene symbol (e.g., "EGFR"):

mygene = tu.tools.MyGene_query_genes(query="EGFR", species="human", fields="symbol,name,ensembl.gene,uniprot.Swiss-Prot,entrezgene")

Extract: ensembl_id, uniprot_id, entrez_id, symbol, name

If UniProt ID (e.g., "P00533"):

uniprot = tu.tools.UniProt_get_entry_by_accession(accession="P00533")

Extract: gene names, Ensembl xrefs, function

Step 2: Resolve Ensembl ID and get versioned ID for GTEx

ensembl = tu.tools.ensembl_lookup_gene(gene_id=ensembl_id, species="homo_sapiens")

CRITICAL: species parameter is REQUIRED

CRITICAL: Response is wrapped in {status, data, url, content_type} - access via ensembl['data']

ensembl_data = ensembl.get('data', ensembl) if isinstance(ensembl, dict) else ensembl

Extract: version for versioned_id (e.g., "ENSG00000146648.18")

Step 3: Get Ensembl cross-references

xrefs = tu.tools.ensembl_get_xrefs(id=ensembl_id)

Extract: HGNC, UniProt, EntrezGene mappings

Step 4: Get OpenTargets target info

ot_target = tu.tools.OpenTargets_get_target_id_description_by_name(targetName="EGFR")

Verify ensemblId matches

Step 5: Get ChEMBL target ID

chembl_targets = tu.tools.ChEMBL_search_targets(pref_name__contains="EGFR", organism="Homo sapiens", limit=5)

Extract: target_chembl_id for later use

Step 6: Get UniProt function summary

function_info = tu.tools.UniProt_get_function_by_accession(accession=uniprot_id)

Returns list of strings (NOT dict)

Step 7: Get alternative names for collision detection

alt_names = tu.tools.UniProt_get_alternative_names_by_accession(accession=uniprot_id)

Identifier Resolution Output

1. Target Identity

Protein Function: [from UniProt_get_function_by_accession] Subcellular Location: [from UniProt_get_subcellular_location_by_accession] Target Class: [from OpenTargets_get_target_classes_by_ensemblID]

Known Parameter Corrections

Tool WRONG Parameter CORRECT Parameter

ensembl_lookup_gene

id

gene_id (+ species="homo_sapiens" REQUIRED)

Reactome_map_uniprot_to_pathways

uniprot_id

id

ensembl_get_xrefs

gene_id

id

GTEx_get_median_gene_expression

gencode_id only gencode_id

• operation="median"

OpenTargets_*

ensemblID (uppercase) ensemblId (camelCase)

OpenTargets_get_publications_*

ensemblId

entityId

OpenTargets_get_associated_drugs_by_target_ensemblID

ensemblId only ensemblId

• size (REQUIRED)

MyGene_query_genes

q

query

PubMed_search_articles

returns {articles: [...]}

returns plain list of dicts

UniProt_get_function_by_accession

returns dict returns list of strings

HPA_get_rna_expression_by_source

ensembl_id

gene_name

• source_type
• source_name (ALL required)

alphafold_get_prediction

uniprot_accession

qualifier

drugbank_get_safety_*

simple params query , case_sensitive , exact_match , limit (ALL required)

Phase 1: Disease Association Evidence (0-30 points)

Objective: Quantify the strength of target-disease association from genetic, literature, and pathway evidence.

1A. OpenTargets Disease Associations (Primary)

Get ALL disease associations for target

diseases = tu.tools.OpenTargets_get_diseases_phenotypes_by_target_ensembl(ensemblId=ensembl_id)

If specific disease provided, get detailed evidence

if disease_name: disease_info = tu.tools.OpenTargets_get_disease_id_description_by_name(diseaseName=disease_name) efo_id = disease_info.get('id') # e.g., "EFO_0003060"

evidence = tu.tools.OpenTargets_target_disease_evidence(
    efoId=efo_id, ensemblId=ensembl_id
)

# Get evidence by data source for detailed breakdown
datasource_evidence = tu.tools.OpenTargets_get_evidence_by_datasource(
    efoId=efo_id, ensemblId=ensembl_id,
    datasourceIds=["ot_genetics_portal", "eva", "gene2phenotype", "genomics_england", "uniprot_literature"],
    size=100
)

1B. GWAS Genetic Evidence

GWAS associations for target gene

gwas_snps = tu.tools.gwas_get_snps_for_gene(mapped_gene=gene_symbol, size=50)

If specific disease, search for trait-specific associations

if disease_name: gwas_studies = tu.tools.gwas_search_studies(query=disease_name, size=20)

1C. Constraint Scores (gnomAD)

Genetic constraint - intolerance to loss of function

constraints = tu.tools.gnomad_get_gene_constraints(gene_symbol=gene_symbol)

Extract: pLI, LOEUF, missense_z, pRec

High pLI (>0.9) = highly intolerant to LoF = likely essential

1D. Literature Evidence

PubMed for target-disease association

articles = tu.tools.PubMed_search_articles( query=f'"{gene_symbol}" AND "{disease_name}" AND (target OR therapeutic OR inhibitor)', limit=50 )

PubMed_search_articles returns a plain list of dicts

OpenTargets publications

pubs = tu.tools.OpenTargets_get_publications_by_target_ensemblID(entityId=ensembl_id)

Scoring Logic - Disease Association

Genetic Evidence (0-10):

• GWAS hits for specific disease: +3 per significant locus (max 6)
• Rare variant evidence (ClinVar pathogenic): +2
• Somatic mutations in disease: +2
• pLI > 0.9 (essential gene): +2

Literature Evidence (0-10):

• 100 publications on target+disease: 10

• 50-100 publications: 7
• 10-50 publications: 5
• 1-10 publications: 3
• 0 publications: 0

Pathway Evidence (0-10):

• OpenTargets overall score > 0.8: 10
• Score 0.5-0.8: 7
• Score 0.2-0.5: 4
• Score < 0.2: 1

Phase 2: Druggability Assessment (0-25 points)

Objective: Assess whether the target is amenable to therapeutic intervention.

2A. OpenTargets Tractability

Tractability assessment across modalities

tractability = tu.tools.OpenTargets_get_target_tractability_by_ensemblID(ensemblId=ensembl_id)

Returns: label, modality (SM, AB, PR, OC), value (boolean/score)

Modalities: Small Molecule, Antibody, PROTAC, Other Clinical

2B. Target Class & Family

Target classification (kinase, GPCR, ion channel, etc.)

target_classes = tu.tools.OpenTargets_get_target_classes_by_ensemblID(ensemblId=ensembl_id)

Pharos target development level

pharos = tu.tools.Pharos_get_target(gene=gene_symbol)

TDL: Tclin (approved drug) > Tchem (compounds) > Tbio (biology) > Tdark (unknown)

DGIdb druggability categories

druggability = tu.tools.DGIdb_get_gene_druggability(genes=[gene_symbol])

2C. Structural Tractability

PDB structures available

if uniprot_id: uniprot_entry = tu.tools.UniProt_get_entry_by_accession(accession=uniprot_id) # Extract PDB cross-references from entry

AlphaFold prediction

alphafold = tu.tools.alphafold_get_prediction(qualifier=uniprot_id) alphafold_summary = tu.tools.alphafold_get_summary(qualifier=uniprot_id)

For top PDB structures, analyze binding pockets

ProteinsPlus DoGSiteScorer for pocket detection

for pdb_id in top_pdb_ids[:3]: pockets = tu.tools.ProteinsPlus_predict_binding_sites(pdb_id=pdb_id) # Returns predicted druggable pockets with scores

2D. Chemical Probes & Enabling Packages

Chemical probes (validated tool compounds)

probes = tu.tools.OpenTargets_get_chemical_probes_by_target_ensemblID(ensemblId=ensembl_id)

Target Enabling Packages (TEPs)

teps = tu.tools.OpenTargets_get_target_enabling_packages_by_ensemblID(ensemblId=ensembl_id)

Scoring Logic - Druggability

Structural Tractability (0-10):

• High-res co-crystal structure with ligand: 10
• PDB structure available, pockets detected: 7
• AlphaFold only, confident pocket prediction: 5
• AlphaFold low confidence / no structure: 2
• No structural data: 0

Chemical Matter (0-10):

• Known drug-like compounds (IC50 < 100nM): 10
• Tool compounds (IC50 < 1uM): 7
• HTS hits only (IC50 > 1uM): 4
• No known ligands: 0

Target Class Bonus (0-5):

• Validated druggable family (kinase, GPCR, nuclear receptor): 5
• Enzyme, ion channel: 4
• Protein-protein interaction, transporter: 2
• Novel/unknown class: 0

Phase 3: Known Modulators & Chemical Matter (Feeds into Phase 2 scoring)

Objective: Identify existing chemical starting points for target validation.

3A. ChEMBL Bioactivity

Search for ChEMBL target

chembl_targets = tu.tools.ChEMBL_search_targets( pref_name__contains=gene_symbol, organism="Homo sapiens", limit=10 )

Get activities for best matching target

target_chembl_id = chembl_targets[0]['target_chembl_id'] activities = tu.tools.ChEMBL_get_target_activities( target_chembl_id__exact=target_chembl_id, limit=100 )

Parse: compound IDs, pChEMBL values, activity types (IC50, Ki, Kd)

Filter: potent compounds (pChEMBL >= 6.0 = IC50 <= 1uM)

3B. BindingDB Ligands

Experimental binding data

ligands = tu.tools.BindingDB_get_ligands_by_uniprot( uniprot=uniprot_id, affinity_cutoff=10000 # nM )

Returns: SMILES, affinity_type (Ki/IC50/Kd), affinity value, PMID

3C. PubChem Bioassays

HTS screening data

assays = tu.tools.PubChem_search_assays_by_target_gene(gene_symbol=gene_symbol)

Get details for top assays

for aid in assay_ids[:5]: summary = tu.tools.PubChem_get_assay_summary(aid=str(aid)) targets = tu.tools.PubChem_get_assay_targets(aid=str(aid)) actives = tu.tools.PubChem_get_assay_active_compounds(aid=str(aid))

3D. Known Drugs Targeting This Protein

OpenTargets known drugs

drugs = tu.tools.OpenTargets_get_associated_drugs_by_target_ensemblID( ensemblId=ensembl_id, size=25 )

ChEMBL drug mechanisms

drug_mechanisms = tu.tools.ChEMBL_search_mechanisms( target_chembl_id=target_chembl_id, limit=50 )

Drug interaction databases

dgidb = tu.tools.DGIdb_get_gene_info(genes=[gene_symbol])

Report Format - Chemical Matter

4. Known Modulators & Chemical Matter

4.1 Approved Drugs

4.2 ChEMBL Bioactivity Summary

Total Activities: 12,456 datapoints across 2,341 assays Most Potent Compound: CHEMBL413456 (IC50 = 0.3 nM) [T1] Chemical Series: 8 distinct scaffolds with pChEMBL >= 7.0 Selectivity Data: Available for 45 compounds (kinase panel)

4.3 BindingDB Ligands

Total Ligands: 856 with measured affinity Best Affinity: 0.1 nM (Ki) Affinity Distribution: <1nM: 23, 1-10nM: 89, 10-100nM: 234, 100nM-1uM: 510

4.4 Chemical Probes

Phase 4: Clinical Precedent (0-15 points)

Objective: Assess clinical validation from approved drugs and clinical trials.

4A. FDA-Approved Drugs

FDA label information

fda_moa = tu.tools.FDA_get_mechanism_of_action_by_drug_name(drug_name=gene_symbol) fda_indications = tu.tools.FDA_get_indications_by_drug_name(drug_name=known_drug_name)

DrugBank pharmacology

drugbank_targets = tu.tools.drugbank_get_targets_by_drug_name_or_drugbank_id( query=known_drug_name, case_sensitive=False, exact_match=False, limit=10 )

DrugBank safety info

drugbank_safety = tu.tools.drugbank_get_safety_by_drug_name_or_drugbank_id( query=known_drug_name, case_sensitive=False, exact_match=False, limit=10 )

4B. Clinical Trials

Active clinical trials targeting this protein

trials = tu.tools.search_clinical_trials( query_term=gene_symbol, intervention=gene_symbol, pageSize=50 )

If specific disease context

if disease_name: disease_trials = tu.tools.search_clinical_trials( query_term=gene_symbol, condition=disease_name, pageSize=50 )

4C. Failed Programs (Learn from Failures)

Drug warnings and withdrawals

for drug_chembl_id in known_drug_ids: warnings = tu.tools.OpenTargets_get_drug_warnings_by_chemblId(chemblId=drug_chembl_id) adverse = tu.tools.OpenTargets_get_drug_adverse_events_by_chemblId(chemblId=drug_chembl_id)

Scoring Logic - Clinical Precedent

Clinical Precedent (0-15):

• FDA-approved drug for SAME disease: 15
• FDA-approved drug for DIFFERENT disease: 12
• Phase 3 clinical trial: 10
• Phase 2 clinical trial: 7
• Phase 1 clinical trial: 5
• Preclinical compounds only: 3
• No clinical development: 0

Adjustment factors:

• Failed clinical program for safety: -3
• Drug withdrawal: -5
• Multiple approved drugs (validated class): +2

Phase 5: Safety & Toxicity Considerations (0-20 points)

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

5A. OpenTargets Safety Profile

safety = tu.tools.OpenTargets_get_target_safety_profile_by_ensemblID(ensemblId=ensembl_id)

Returns: safety liabilities, adverse effects, experimental toxicity

5B. Expression in Critical Tissues

GTEx tissue expression (identifies essential organ expression)

gtex = tu.tools.GTEx_get_median_gene_expression( operation="median", gencode_id=ensembl_versioned_id )

If empty, try unversioned ID

HPA expression

NOTE: HPA_get_rna_expression_by_source requires gene_name, source_type, source_name

hpa = tu.tools.HPA_search_genes_by_query(search_query=gene_symbol) hpa_details = tu.tools.HPA_get_comprehensive_gene_details_by_ensembl_id(ensembl_id=ensembl_id)

Check expression in safety-critical tissues

Heart, liver, kidney, brain, bone marrow = high risk if target is expressed

5C. Knockout Phenotypes

Mouse model phenotypes

mouse_models = tu.tools.OpenTargets_get_biological_mouse_models_by_ensemblID(ensemblId=ensembl_id)

Genetic constraint (proxy for essentiality)

constraints = tu.tools.gnomad_get_gene_constraints(gene_symbol=gene_symbol)

High pLI = essential gene = potential safety concern

5D. Known Adverse Events from Target Modulation

For known drugs targeting this protein

for drug_name in known_drug_names: fda_adr = tu.tools.FDA_get_adverse_reactions_by_drug_name(drug_name=drug_name) fda_warnings = tu.tools.FDA_get_warnings_and_cautions_by_drug_name(drug_name=drug_name) fda_boxed = tu.tools.FDA_get_boxed_warning_info_by_drug_name(drug_name=drug_name) fda_contraindications = tu.tools.FDA_get_contraindications_by_drug_name(drug_name=drug_name)

5E. Homologs & Off-Target Risks

Paralogs (close family members that might be hit)

homologs = tu.tools.OpenTargets_get_target_homologues_by_ensemblID(ensemblId=ensembl_id)

Paralogs with high sequence identity = selectivity challenge

Scoring Logic - Safety

Tissue Expression Selectivity (0-5):

• Target restricted to disease tissue: 5
• Low expression in heart/liver/kidney/brain: 4
• Moderate expression in 1-2 critical tissues: 2
• High expression in multiple critical tissues: 0

Genetic Validation (0-10):

• Mouse KO viable, no severe phenotype: 10
• Mouse KO viable with mild phenotype: 7
• Mouse KO has concerning phenotype: 3
• Mouse KO lethal: 0
• No KO data, low pLI (<0.5): 5
• No KO data, high pLI (>0.9): 2

Known Adverse Events (0-5):

• No known safety signals: 5
• Mild, manageable ADRs: 3
• Serious ADRs reported: 1
• Black box warning or drug withdrawal: 0

Phase 6: Pathway Context & Network Analysis

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

6A. Reactome Pathways

Map target to pathways

pathways = tu.tools.Reactome_map_uniprot_to_pathways(id=uniprot_id)

Get pathway details for top pathways

for pathway in top_pathways[:5]: detail = tu.tools.Reactome_get_pathway(id=pathway['stId']) reactions = tu.tools.Reactome_get_pathway_reactions(id=pathway['stId'])

6B. Protein-Protein Interactions

STRING network

string_ppi = tu.tools.STRING_get_protein_interactions( protein_ids=[gene_symbol], species=9606, confidence_score=0.7 )

Higher confidence = more reliable

IntAct interactions (experimental)

intact_ppi = tu.tools.intact_get_interactions(identifier=uniprot_id)

OpenTargets interactions

ot_ppi = tu.tools.OpenTargets_get_target_interactions_by_ensemblID(ensemblId=ensembl_id)

6C. Functional Enrichment

GO annotations

go_terms = tu.tools.OpenTargets_get_target_gene_ontology_by_ensemblID(ensemblId=ensembl_id)

Direct GO query

go_annotations = tu.tools.GO_get_annotations_for_gene(gene_id=gene_symbol)

STRING functional enrichment of interaction partners

enrichment = tu.tools.STRING_functional_enrichment( protein_ids=[gene_symbol], species=9606 )

Report Format - Pathway Context

7. Pathway Context & Network Analysis

7.1 Key Pathways

7.2 Protein-Protein Interactions

Total Interactors: 45 (STRING confidence > 0.7) Key Interactors: GRB2, SHC1, PLCG1, PIK3CA, STAT3

7.3 Pathway Redundancy Assessment

Compensation Risk: MODERATE

• Parallel pathways: HER2, HER3 can compensate
• Feedback loops: RAS activation bypasses EGFR
• Downstream convergence: MEK/ERK shared with other RTKs

Phase 7: Validation Evidence (0-10 points)

Objective: Assess existing functional validation data.

7A. DepMap Essentiality (CRISPR/RNAi)

Gene essentiality in cancer cell lines

deps = tu.tools.DepMap_get_gene_dependencies(gene_symbol=gene_symbol)

Negative scores = essential (cells die upon KO)

Score < -0.5: moderately essential

Score < -1.0: strongly essential

7B. Literature Validation Evidence

Search for functional studies

validation_papers = tu.tools.PubMed_search_articles( query=f'"{gene_symbol}" AND (CRISPR OR siRNA OR knockdown OR knockout OR "loss of function") AND "{disease_name}"', limit=30 )

Search for biomarker studies

biomarker_papers = tu.tools.PubMed_search_articles( query=f'"{gene_symbol}" AND (biomarker OR "target engagement" OR "pharmacodynamic")', limit=20 )

7C. Animal Model Evidence

Mouse phenotypes from OpenTargets (already retrieved in Phase 5)

Reuse mouse_models data

CTD gene-disease associations (complementary)

ctd_diseases = tu.tools.CTD_get_gene_diseases(input_terms=gene_symbol)

Scoring Logic - Validation Evidence

Functional Studies (0-5):

• CRISPR KO shows disease-relevant phenotype: 5
• siRNA knockdown shows phenotype: 4
• Biochemical assay validates mechanism: 3
• Overexpression study only: 2
• No functional data: 0

Disease Models (0-5):

• Patient-derived xenograft (PDX) response: 5
• Genetically engineered mouse model: 4
• Cell line model: 3
• In silico model only: 1
• No model data: 0

Phase 8: Structural Insights

Objective: Leverage structural biology for druggability and mechanism understanding.

8A. PDB Structures

Get PDB entries from UniProt cross-references

uniprot_entry = tu.tools.UniProt_get_entry_by_accession(accession=uniprot_id)

Parse: uniProtKBCrossReferences where database == "PDB"

Get details for each PDB

for pdb_id in pdb_ids[:10]: metadata = tu.tools.get_protein_metadata_by_pdb_id(pdb_id=pdb_id) quality = tu.tools.pdbe_get_entry_quality(pdb_id=pdb_id) summary = tu.tools.pdbe_get_entry_summary(pdb_id=pdb_id) experiment = tu.tools.pdbe_get_entry_experiment(pdb_id=pdb_id) molecules = tu.tools.pdbe_get_entry_molecules(pdb_id=pdb_id)

8B. AlphaFold Prediction

alphafold = tu.tools.alphafold_get_prediction(qualifier=uniprot_id) alphafold_info = tu.tools.alphafold_get_summary(qualifier=uniprot_id)

Check pLDDT scores for confidence

8C. Binding Pocket Analysis

ProteinsPlus DoGSiteScorer for best PDB structure

pockets = tu.tools.ProteinsPlus_predict_binding_sites(pdb_id=best_pdb_id)

Returns: pocket locations, druggability scores, volume, surface

Interaction diagram for co-crystal structures

if has_ligand: diagram = tu.tools.ProteinsPlus_generate_interaction_diagram(pdb_id=pdb_id)

8D. Domain Architecture

InterPro domains

domains = tu.tools.InterPro_get_protein_domains(uniprot_accession=uniprot_id)

Domain details for key domains

for domain in domains[:5]: detail = tu.tools.InterPro_get_domain_details(entry_id=domain['accession'])

Phase 9: Literature Deep Dive

Objective: Comprehensive literature analysis with collision-aware search.

9A. Collision Detection

Detect naming collisions before literature search

test_results = tu.tools.PubMed_search_articles( query=f'"{gene_symbol}"[Title]', limit=20 )

PubMed returns plain list of dicts

Check if >20% of results are off-topic (no biology terms)

If collision detected, add filters: AND (protein OR gene OR receptor OR kinase)

9B. Publication Metrics

Total publications

total = tu.tools.PubMed_search_articles( query=f'"{gene_symbol}" AND (protein OR gene)', limit=1 )

Check total_count field

Recent publications (5-year trend)

recent = tu.tools.PubMed_search_articles( query=f'"{gene_symbol}" AND (protein OR gene) AND ("2021"[PDAT] : "2026"[PDAT])', limit=50 )

Drug-focused publications

drug_pubs = tu.tools.PubMed_search_articles( query=f'"{gene_symbol}" AND (drug OR therapeutic OR inhibitor OR antibody)', limit=30 )

EuropePMC for broader coverage

epmc = tu.tools.EuropePMC_search_articles( query=f'"{gene_symbol}" AND drug target', limit=30 )

9C. Key Reviews and Landmark Papers

Reviews for target overview

reviews = tu.tools.PubMed_search_articles( query=f'"{gene_symbol}" AND drug target AND review[pt]', limit=10 )

OpenAlex for citation metrics

openalex_works = tu.tools.openalex_search_works( query=f'{gene_symbol} drug target', limit=20 )

Phase 10: Validation Roadmap (Synthesis)

Objective: Generate actionable recommendations based on all evidence.

This phase synthesizes all previous phases into:

• Target Validation Score (0-100)

• Priority Tier (1-4)

• GO/NO-GO Recommendation

• Recommended Experiments

• Tool Compounds for Testing

• Biomarker Strategy

• Key Risks & Mitigations

Score Calculation

def calculate_validation_score(phase_results): """ Calculate Target Validation Score (0-100).

Components:
- Disease Association: 0-30
- Druggability: 0-25
- Safety: 0-20
- Clinical Precedent: 0-15
- Validation Evidence: 0-10
"""
score = {
    'disease_genetic': 0,      # 0-10
    'disease_literature': 0,   # 0-10
    'disease_pathway': 0,      # 0-10
    'drug_structural': 0,      # 0-10
    'drug_chemical': 0,        # 0-10
    'drug_class': 0,           # 0-5
    'safety_expression': 0,    # 0-5
    'safety_genetic': 0,       # 0-10
    'safety_adverse': 0,       # 0-5
    'clinical': 0,             # 0-15
    'validation_functional': 0, # 0-5
    'validation_models': 0,    # 0-5
}

# ... scoring logic from each phase ...

total = sum(score.values())

if total >= 80:
    tier = "Tier 1"
    recommendation = "GO - Highly validated target"
elif total >= 60:
    tier = "Tier 2"
    recommendation = "CONDITIONAL GO - Needs focused validation"
elif total >= 40:
    tier = "Tier 3"
    recommendation = "CAUTION - Significant validation needed"
else:
    tier = "Tier 4"
    recommendation = "NO-GO - Consider alternatives"

return total, tier, recommendation, score

Report Template

File: [TARGET]_[DISEASE]_validation_report.md

Drug Target Validation Report: [TARGET]

Target: [Gene Symbol] ([Full Name]) Disease Context: [Disease Name] (if provided) Modality: [Small molecule / Antibody / etc.] (if specified) Generated: [Date] Status: In Progress

Executive Summary

Target Validation Score: [XX/100] Priority Tier: [Tier X] - [Description] Recommendation: [GO / CONDITIONAL GO / CAUTION / NO-GO]

Key Findings:

• [1-sentence disease association strength with evidence grade]
• [1-sentence druggability assessment]
• [1-sentence safety profile]
• [1-sentence clinical precedent]

Critical Risks:

• [Top risk 1]
• [Top risk 2]

Validation Scorecard

1. Target Identity

[Researching...]

2. Disease Association Evidence

2.1 OpenTargets Disease Associations

[Researching...]

2.2 GWAS Genetic Evidence

[Researching...]

2.3 Constraint Scores (gnomAD)

[Researching...]

2.4 Literature Evidence

[Researching...]

3. Druggability Assessment

3.1 Tractability (OpenTargets)

[Researching...]

3.2 Target Classification

[Researching...]

3.3 Structural Tractability

[Researching...]

3.4 Chemical Probes & Enabling Packages

[Researching...]

4. Known Modulators & Chemical Matter

4.1 Approved/Clinical Drugs

[Researching...]

4.2 ChEMBL Bioactivity

[Researching...]

4.3 BindingDB Ligands

[Researching...]

4.4 PubChem Bioassays

[Researching...]

4.5 Chemical Probes

[Researching...]

5. Clinical Precedent

5.1 FDA-Approved Drugs

[Researching...]

5.2 Clinical Trial Landscape

[Researching...]

5.3 Failed Programs & Lessons

[Researching...]

6. Safety & Toxicity Profile

6.1 OpenTargets Safety Liabilities

[Researching...]

6.2 Expression in Critical Tissues

[Researching...]

6.3 Knockout Phenotypes

[Researching...]

6.4 Known Adverse Events

[Researching...]

6.5 Paralog & Off-Target Risks

[Researching...]

7. Pathway Context & Network Analysis

7.1 Biological Pathways

[Researching...]

7.2 Protein-Protein Interactions

[Researching...]

7.3 Functional Enrichment

[Researching...]

7.4 Pathway Redundancy Assessment

[Researching...]

8. Validation Evidence

8.1 Target Essentiality (DepMap)

[Researching...]

8.2 Functional Studies

[Researching...]

8.3 Animal Models

[Researching...]

8.4 Biomarker Potential

[Researching...]

9. Structural Insights

9.1 Experimental Structures (PDB)

[Researching...]

9.2 AlphaFold Prediction

[Researching...]

9.3 Binding Pocket Analysis

[Researching...]

9.4 Domain Architecture

[Researching...]

10. Literature Landscape

10.1 Publication Metrics

[Researching...]

10.2 Key Publications

[Researching...]

10.3 Research Trend

[Researching...]

11. Validation Roadmap

11.1 Recommended Validation Experiments

[Researching...]

11.2 Tool Compounds for Testing

[Researching...]

11.3 Biomarker Strategy

[Researching...]

11.4 Clinical Biomarker Candidates

[Researching...]

11.5 Disease Models to Test

[Researching...]

12. Risk Assessment

12.1 Key Risks

[Researching...]

12.2 Mitigation Strategies

[Researching...]

12.3 Competitive Landscape

[Researching...]

13. Completeness Checklist

[To be populated post-audit...]

14. Data Sources & Methodology

[Will be populated as research progresses...]

Completeness Checklist (MANDATORY)

Before finalizing, verify:

13. Completeness Checklist

Phase Coverage

• Phase 0: Target disambiguation (all IDs resolved)
• Phase 1: Disease association (OT + GWAS + gnomAD + literature)
• Phase 2: Druggability (tractability + class + structure + probes)
• Phase 3: Chemical matter (ChEMBL + BindingDB + PubChem + drugs)
• Phase 4: Clinical precedent (FDA + trials + failures)
• Phase 5: Safety (OT safety + expression + KO + ADRs + paralogs)
• Phase 6: Pathway context (Reactome + STRING + GO)
• Phase 7: Validation evidence (DepMap + literature + models)
• Phase 8: Structural insights (PDB + AlphaFold + pockets + domains)
• Phase 9: Literature (collision-aware + metrics + key papers)
• Phase 10: Validation roadmap (score + recommendations)

Data Quality

• All scores justified with specific data
• Evidence grades (T1-T4) assigned to key claims
• Negative results documented (not left blank)
• Failed tools with fallbacks documented
• Source citations for all data points

Scoring

• All 12 score components calculated
• Total score summed correctly
• Priority tier assigned
• GO/NO-GO recommendation justified

Fallback Chains

Primary Tool Fallback 1 Fallback 2 If All Fail

OpenTargets_get_diseases_phenotypes_*

CTD_get_gene_diseases

PubMed search Note in report

GTEx_get_median_gene_expression (versioned) GTEx (unversioned) HPA_search_genes_by_query

Document gap

ChEMBL_get_target_activities

BindingDB_get_ligands_by_uniprot

DGIdb_get_gene_info

Note in report

gnomad_get_gene_constraints

OpenTargets_get_target_constraint_info_*

Note as unavailable

Reactome_map_uniprot_to_pathways

OpenTargets_get_target_gene_ontology_*

Use GO only

STRING_get_protein_interactions

intact_get_interactions

OpenTargets interactions

Note in report

ProteinsPlus_predict_binding_sites

alphafold_get_prediction

Literature pockets Note as limited

Modality-Specific Considerations

Small Molecule Focus

• Emphasize: binding pockets, ChEMBL compounds, Lipinski compliance

• Key tractability: OpenTargets SM tractability bucket

• Structure: co-crystal structures with small molecule ligands

• Chemical matter: IC50/Ki/Kd data from ChEMBL/BindingDB

Antibody Focus

• Emphasize: extracellular domains, cell surface expression, glycosylation

• Key tractability: OpenTargets AB tractability bucket

• Structure: ectodomain structures, epitope mapping

• Expression: surface expression in disease vs normal tissue

PROTAC Focus

• Emphasize: intracellular targets, surface lysines, E3 ligase proximity

• Key tractability: OpenTargets PROTAC tractability

• Structure: full-length structures for linker design

• Chemical matter: known binders + E3 ligase binders

Quick Reference: Verified Tool Parameters

Tool Parameters Notes

ensembl_lookup_gene

gene_id , species

species="homo_sapiens" REQUIRED; response wrapped in {status, data, url, content_type}

OpenTargets_get_*_by_ensemblID

ensemblId

camelCase, NOT ensemblID

OpenTargets_get_publications_by_target_ensemblID

entityId

NOT ensemblId

OpenTargets_get_associated_drugs_by_target_ensemblID

ensemblId , size

size is REQUIRED

OpenTargets_target_disease_evidence

efoId , ensemblId

Both REQUIRED

GTEx_get_median_gene_expression

operation , gencode_id

operation="median" REQUIRED

HPA_get_rna_expression_by_source

gene_name , source_type , source_name

ALL 3 required

PubMed_search_articles

query , limit

Returns plain list, NOT {articles:[]}

UniProt_get_function_by_accession

accession

Returns list of strings

alphafold_get_prediction

qualifier

NOT uniprot_accession

drugbank_get_safety_*

query , case_sensitive , exact_match , limit

ALL required

STRING_get_protein_interactions

protein_ids , species

protein_ids is array; species=9606

Reactome_map_uniprot_to_pathways

id

NOT uniprot_id

ChEMBL_get_target_activities

target_chembl_id__exact

Note double underscore

search_clinical_trials

query_term

REQUIRED parameter

gnomad_get_gene_constraints

gene_symbol

NOT gene_id

DepMap_get_gene_dependencies

gene_symbol

NOT gene_id

BindingDB_get_ligands_by_uniprot

uniprot , affinity_cutoff

affinity in nM

Pharos_get_target

gene or uniprot

Both optional but need one

Example Execution: EGFR for NSCLC

Phase 0 Result

• Symbol: EGFR, Ensembl: ENSG00000146648, UniProt: P00533, ChEMBL: CHEMBL203

Expected Scores (EGFR for NSCLC)

• Disease Association: ~28/30 (strong genetic + pathway + literature)

• Druggability: ~24/25 (kinase, many structures, abundant compounds)

• Safety: ~14/20 (widely expressed but manageable toxicity)

• Clinical Precedent: 15/15 (multiple approved drugs)

• Validation Evidence: ~9/10 (extensive functional data)

• Total: ~90/100 = Tier 1

Example for Novel Target (e.g., understudied kinase)

• Disease Association: ~8/30 (limited GWAS, few publications)

• Druggability: ~15/25 (kinase family bonus, AlphaFold structure)

• Safety: ~12/20 (limited data, unknown KO phenotype)

• Clinical Precedent: 0/15 (no clinical development)

• Validation Evidence: ~2/10 (minimal functional data)

• Total: ~37/100 = Tier 4