Comprehensive Target Intelligence Gatherer

Gather complete target intelligence by exploring 9 parallel research paths. Supports targets identified by gene symbol, UniProt accession, Ensembl ID, or gene name.

KEY PRINCIPLES:

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

• Tool parameter verification - Verify params via get_tool_info before calling unfamiliar tools

• Evidence grading - Grade all claims by evidence strength (T1-T4)

• Citation requirements - Every fact must have inline source attribution

• Mandatory completeness - All sections must exist with data minimums or explicit "No data" notes

• Disambiguation first - Resolve all identifiers before research

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

• Collision-aware literature search - Detect and filter naming collisions

• English-first queries - Always use English terms in tool calls, even if the user writes in another language. Translate gene names, disease names, and search terms to English. Only try original-language terms as a fallback if English returns no results. Respond in the user's language

Phase 0: Tool Parameter Verification (CRITICAL)

BEFORE calling ANY tool for the first time, verify its parameters:

Always check tool params to prevent silent failures

tool_info = tu.tools.get_tool_info(tool_name="Reactome_map_uniprot_to_pathways")

Reveals: takes id not uniprot_id

Known Parameter Corrections (Updated)

Tool WRONG Parameter CORRECT Parameter

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

ensemblId (camelCase)

GTEx Versioned ID Fallback (CRITICAL)

GTEx often requires versioned Ensembl IDs. If ENSG00000123456 returns empty:

Step 1: Get gene info with version

gene_info = tu.tools.ensembl_lookup_gene(id=ensembl_id, species="human") version = gene_info.get('version', 1)

Step 2: Try versioned ID

versioned_id = f"{ensembl_id}.{version}" # e.g., "ENSG00000123456.12" result = tu.tools.GTEx_get_median_gene_expression( gencode_id=versioned_id, operation="median" )

When to Use This Skill

Apply when users:

• Ask about a drug target, protein, or gene

• Need target validation or assessment

• Request druggability analysis

• Want comprehensive target profiling

• Ask "what do we know about [target]?"

• Need target-disease associations

• Request safety profile for a target

Critical Workflow Requirements

1. Report-First Approach (MANDATORY)

DO NOT show the search process or tool outputs to the user. Instead:

Create the report file FIRST - Before any data collection:

• File name: [TARGET]_target_report.md

• Initialize with all 14 section headers

• Add placeholder: [Researching...] in each section

Progressively update the report - As you gather data:

• Update each section immediately after retrieving data

• Replace [Researching...] with actual content

• Include "No data returned" when tools return empty results

Methodology in appendix only - If user requests methodology details, create separate [TARGET]_methods_appendix.md

2. Evidence Grading System (MANDATORY)

CRITICAL: Grade every claim by evidence strength.

Evidence Tiers

Tier Symbol Criteria Examples

T1 ★★★ Direct mechanistic evidence, human genetic proof CRISPR KO, patient mutations, crystal structure with mechanism

T2 ★★☆ Functional studies, model organism validation siRNA phenotype, mouse KO, biochemical assay

T3 ★☆☆ Association, screen hits, computational GWAS hit, DepMap essentiality, expression correlation

T4 ☆☆☆ Mention, review, text-mined, predicted Review article, database annotation, computational prediction

Required Evidence Grading Locations

Evidence grades MUST appear in:

• Executive Summary - Key disease claims graded

• Section 8.2 Disease Associations - Every disease link graded with source type

• Section 11 Literature - Key papers table with evidence tier

• Section 13 Recommendations - Scorecard items reference evidence quality

Per-Section Evidence Summary

Evidence Quality for this Section: Strong

• Mechanistic (T1): 12 papers
• Functional (T2): 8 papers
• Association (T3): 15 papers
• Mention (T4): 23 papers Data Gaps: No CRISPR data; mouse KO phenotypes limited

3. Citation Requirements (MANDATORY)

Every piece of information MUST include its source:

EGFR mutations cause lung adenocarcinoma [★★★: PMID:15118125, activating mutations in patients]. Source: ClinVar, CIViC

Core Strategy: 9 Research Paths

Execute 9 research paths (Path 0 is always first):

Target Query (e.g., "EGFR" or "P00533") │ ├─ IDENTIFIER RESOLUTION (always first) │ └─ Check if GPCR → GPCRdb_get_protein │ ├─ PATH 0: Open Targets Foundation (ALWAYS FIRST - fills gaps in all other paths) │ ├─ PATH 1: Core Identity (names, IDs, sequence, organism) │ └─ InterProScan_scan_sequence for novel domain prediction (NEW) ├─ PATH 2: Structure & Domains (3D structure, domains, binding sites) │ └─ If GPCR: GPCRdb_get_structures (active/inactive states) ├─ PATH 3: Function & Pathways (GO terms, pathways, biological role) ├─ PATH 4: Protein Interactions (PPI network, complexes) ├─ PATH 5: Expression Profile (tissue expression, single-cell) ├─ PATH 6: Variants & Disease (mutations, clinical significance) │ └─ DisGeNET_search_gene for curated gene-disease associations ├─ PATH 7: Drug Interactions (known drugs, druggability, safety) │ ├─ Pharos_get_target for TDL classification (Tclin/Tchem/Tbio/Tdark) │ ├─ BindingDB_get_ligands_by_uniprot for known ligands (NEW) │ ├─ PubChem_search_assays_by_target_gene for HTS data (NEW) │ ├─ If GPCR: GPCRdb_get_ligands (curated agonists/antagonists) │ └─ DepMap_get_gene_dependencies for target essentiality └─ PATH 8: Literature & Research (publications, trends)

Identifier Resolution (Phase 1)

CRITICAL: Resolve ALL identifiers before any research path.

def resolve_target_ids(tu, query): """ Resolve target query to ALL needed identifiers. Returns dict with: query, uniprot, ensembl, ensembl_version, symbol, entrez, chembl_target, hgnc """ ids = { 'query': query, 'uniprot': None, 'ensembl': None, 'ensembl_versioned': None, # For GTEx 'symbol': None, 'entrez': None, 'chembl_target': None, 'hgnc': None, 'full_name': None, 'synonyms': [] }

# [Resolution logic based on input type]
# ... (see current implementation)

# CRITICAL: Get versioned Ensembl ID for GTEx
if ids['ensembl']:
    gene_info = tu.tools.ensembl_lookup_gene(id=ids['ensembl'], species="human")
    if gene_info and gene_info.get('version'):
        ids['ensembl_versioned'] = f"{ids['ensembl']}.{gene_info['version']}"
    
    # Also get synonyms for literature collision detection
    ids['full_name'] = gene_info.get('description', '').split(' [')[0]

# Get UniProt alternative names for synonyms
if ids['uniprot']:
    alt_names = tu.tools.UniProt_get_alternative_names_by_accession(accession=ids['uniprot'])
    if alt_names:
        ids['synonyms'].extend(alt_names)

return ids

GPCR Target Detection (NEW)

~35% of approved drugs target GPCRs. After identifier resolution, check if target is a GPCR:

def check_gpcr_target(tu, ids): """ Check if target is a GPCR and retrieve specialized data. Call after identifier resolution. """ symbol = ids.get('symbol', '')

# Build GPCRdb entry name
entry_name = f"{symbol.lower()}_human"

gpcr_info = tu.tools.GPCRdb_get_protein(
    operation="get_protein",
    protein=entry_name
)

if gpcr_info.get('status') == 'success':
    # Target is a GPCR - get specialized data
    
    # Get structures with receptor state
    structures = tu.tools.GPCRdb_get_structures(
        operation="get_structures",
        protein=entry_name
    )
    
    # Get known ligands (critical for binder projects)
    ligands = tu.tools.GPCRdb_get_ligands(
        operation="get_ligands",
        protein=entry_name
    )
    
    # Get mutation data
    mutations = tu.tools.GPCRdb_get_mutations(
        operation="get_mutations",
        protein=entry_name
    )
    
    return {
        'is_gpcr': True,
        'gpcr_family': gpcr_info['data'].get('family'),
        'gpcr_class': gpcr_info['data'].get('receptor_class'),
        'structures': structures.get('data', {}).get('structures', []),
        'ligands': ligands.get('data', {}).get('ligands', []),
        'mutations': mutations.get('data', {}).get('mutations', []),
        'ballesteros_numbering': True  # GPCRdb provides this
    }

return {'is_gpcr': False}

GPCRdb Report Section (add to Section 2 for GPCR targets):

2.x GPCR-Specific Data (GPCRdb)

Receptor Class: Class A (Rhodopsin-like)
GPCR Family: Adrenoceptors

Structures by State:

Known Ligands: 45 agonists, 32 antagonists, 8 allosteric modulators
Key Binding Site Residues (Ballesteros-Weinstein): 3.32, 5.42, 6.48, 7.39

Collision Detection for Literature Search

Before literature search, detect naming collisions:

def detect_collisions(tu, symbol, full_name): """ Detect if gene symbol has naming collisions in literature. Returns negative filter terms if collisions found. """ # Search by symbol in title results = tu.tools.PubMed_search_articles( query=f'"{symbol}"[Title]', limit=20 )

# Check if >20% are off-topic
off_topic_terms = []
for paper in results.get('articles', []):
    title = paper.get('title', '').lower()
    # Check if title mentions biology/protein/gene context
    bio_terms = ['protein', 'gene', 'cell', 'expression', 'mutation', 'kinase', 'receptor']
    if not any(term in title for term in bio_terms):
        # Extract potential collision terms
        # e.g., "JAK" might collide with "Just Another Kinase" jokes
        # e.g., "WDR7" might collide with other WDR family members in certain contexts
        pass

# Build negative filter
collision_filter = ""
if off_topic_terms:
    collision_filter = " NOT " + " NOT ".join(off_topic_terms)

return collision_filter

PATH 0: Open Targets Foundation (ALWAYS FIRST)

Objective: Populate baseline data for Sections 5, 8, 9, 10, 11 before specialized queries.

CRITICAL: Open Targets provides the most comprehensive aggregated data. Query ALL these endpoints:

Endpoint Section Data Type

OpenTargets_get_diseases_phenotypes_by_target_ensemblId

8 Diseases/phenotypes

OpenTargets_get_target_tractability_by_ensemblId

9 Druggability assessment

OpenTargets_get_target_safety_profile_by_ensemblId

10 Safety liabilities

OpenTargets_get_target_interactions_by_ensemblId

6 PPI network

OpenTargets_get_target_gene_ontology_by_ensemblId

5 GO annotations

OpenTargets_get_publications_by_target_ensemblId

11 Literature

OpenTargets_get_biological_mouse_models_by_ensemblId

8/10 Mouse KO phenotypes

OpenTargets_get_chemical_probes_by_target_ensemblId

9 Chemical probes

OpenTargets_get_associated_drugs_by_target_ensemblId

9 Known drugs

Path 0 Implementation

def path_0_open_targets(tu, ids): """ Open Targets foundation data - fills gaps for sections 5, 6, 8, 9, 10, 11. ALWAYS run this first. """ ensembl_id = ids['ensembl'] if not ensembl_id: return {'status': 'skipped', 'reason': 'No Ensembl ID'}

results = {}

# 1. Diseases & Phenotypes (Section 8)
diseases = tu.tools.OpenTargets_get_diseases_phenotypes_by_target_ensemblId(
    ensemblId=ensembl_id
)
results['diseases'] = diseases if diseases else {'note': 'No disease associations returned'}

# 2. Tractability (Section 9)
tractability = tu.tools.OpenTargets_get_target_tractability_by_ensemblId(
    ensemblId=ensembl_id
)
results['tractability'] = tractability if tractability else {'note': 'No tractability data returned'}

# 3. Safety Profile (Section 10)
safety = tu.tools.OpenTargets_get_target_safety_profile_by_ensemblId(
    ensemblId=ensembl_id
)
results['safety'] = safety if safety else {'note': 'No safety liabilities identified'}

# 4. Interactions (Section 6)
interactions = tu.tools.OpenTargets_get_target_interactions_by_ensemblId(
    ensemblId=ensembl_id
)
results['interactions'] = interactions if interactions else {'note': 'No interactions returned'}

# 5. GO Annotations (Section 5)
go_terms = tu.tools.OpenTargets_get_target_gene_ontology_by_ensemblId(
    ensemblId=ensembl_id
)
results['go_terms'] = go_terms if go_terms else {'note': 'No GO annotations returned'}

# 6. Publications (Section 11)
publications = tu.tools.OpenTargets_get_publications_by_target_ensemblId(
    ensemblId=ensembl_id
)
results['publications'] = publications if publications else {'note': 'No publications returned'}

# 7. Mouse Models (Section 8/10)
mouse_models = tu.tools.OpenTargets_get_biological_mouse_models_by_ensemblId(
    ensemblId=ensembl_id
)
results['mouse_models'] = mouse_models if mouse_models else {'note': 'No mouse model data returned'}

# 8. Chemical Probes (Section 9)
probes = tu.tools.OpenTargets_get_chemical_probes_by_target_ensemblId(
    ensemblId=ensembl_id
)
results['chemical_probes'] = probes if probes else {'note': 'No chemical probes available'}

# 9. Associated Drugs (Section 9)
drugs = tu.tools.OpenTargets_get_associated_drugs_by_target_ensemblId(
    ensemblId=ensembl_id
)
results['drugs'] = drugs if drugs else {'note': 'No approved/trial drugs found'}

return results

Negative Results Are Data

CRITICAL: Always document when a query returns empty:

9.3 Chemical Probes

Status: No validated chemical probes available for this target. Source: OpenTargets_get_chemical_probes_by_target_ensemblId returned empty

Implication: Tool compound development would be needed for chemical biology studies.

PATH 2: Structure & Domains (Enhanced)

Objective: Robust structure coverage using 3-step chain.

3-Step Structure Search Chain

Do NOT rely solely on PDB text search. Use this chain:

def path_structure_robust(tu, ids): """ Robust structure search using 3-step chain. """ structures = {'pdb': [], 'alphafold': None, 'domains': [], 'method_notes': []}

# STEP 1: UniProt PDB Cross-References (most reliable)
if ids['uniprot']:
    entry = tu.tools.UniProt_get_entry_by_accession(accession=ids['uniprot'])
    pdb_xrefs = [x for x in entry.get('uniProtKBCrossReferences', []) 
                if x.get('database') == 'PDB']
    for xref in pdb_xrefs:
        pdb_id = xref.get('id')
        # Get details for each PDB
        pdb_info = tu.tools.get_protein_metadata_by_pdb_id(pdb_id=pdb_id)
        if pdb_info:
            structures['pdb'].append(pdb_info)
    structures['method_notes'].append(f"Step 1: {len(pdb_xrefs)} PDB cross-refs from UniProt")

# STEP 2: Sequence-based PDB Search (catches missing annotations)
if ids['uniprot'] and len(structures['pdb']) < 5:
    sequence = tu.tools.UniProt_get_sequence_by_accession(accession=ids['uniprot'])
    if sequence and len(sequence) < 1000:  # Reasonable length for search
        similar = tu.tools.PDB_search_similar_structures(
            sequence=sequence[:500],  # Use first 500 AA if long
            identity_cutoff=0.7
        )
        if similar:
            for hit in similar[:10]:  # Top 10 similar
                if hit['pdb_id'] not in [s.get('pdb_id') for s in structures['pdb']]:
                    structures['pdb'].append(hit)
    structures['method_notes'].append(f"Step 2: Sequence search (identity ≥70%)")

# STEP 3: Domain-based Search (for multi-domain proteins)
if ids['uniprot']:
    domains = tu.tools.InterPro_get_protein_domains(uniprot_accession=ids['uniprot'])
    structures['domains'] = domains if domains else []
    
    # For large proteins with domains, search by domain sequence windows
    if len(structures['pdb']) < 3 and domains:
        for domain in domains[:3]:  # Top 3 domains
            domain_name = domain.get('name', '')
            # Could search PDB by domain name
            domain_hits = tu.tools.PDB_search_by_keyword(query=domain_name, limit=5)
            if domain_hits:
                structures['method_notes'].append(f"Step 3: Domain '{domain_name}' search")

# AlphaFold (always check)
alphafold = tu.tools.alphafold_get_prediction(uniprot_accession=ids['uniprot'])
structures['alphafold'] = alphafold if alphafold else {'note': 'No AlphaFold prediction'}

# IMPORTANT: Document limitations
if not structures['pdb']:
    structures['limitation'] = "No direct PDB hit does NOT mean no structure exists. Check: (1) structures under different UniProt entries, (2) homolog structures, (3) domain-only structures."

return structures

Structure Section Output Format

4.1 Experimental Structures (PDB)

Total PDB Entries: 23 structures (Source: UniProt cross-references) Search Method: 3-step chain (UniProt xrefs → sequence search → domain search)

Note: "No direct PDB hit" ≠ "no structure exists". Check homologs and domain structures.

PATH 5: Expression Profile (Enhanced)

GTEx with Versioned ID Fallback

def path_expression(tu, ids): """ Expression data with GTEx versioned ID fallback. """ results = {'gtex': None, 'hpa': None, 'failed_tools': []}

# GTEx with fallback
ensembl_id = ids['ensembl']
versioned_id = ids.get('ensembl_versioned')

# Try unversioned first
gtex_result = tu.tools.GTEx_get_median_gene_expression(
    gencode_id=ensembl_id,
    operation="median"
)

# Fallback to versioned if empty
if not gtex_result or gtex_result.get('data') == []:
    if versioned_id:
        gtex_result = tu.tools.GTEx_get_median_gene_expression(
            gencode_id=versioned_id,
            operation="median"
        )
        if gtex_result and gtex_result.get('data'):
            results['gtex'] = gtex_result
            results['gtex_note'] = f"Used versioned ID: {versioned_id}"
    
    if not results.get('gtex'):
        results['failed_tools'].append({
            'tool': 'GTEx_get_median_gene_expression',
            'tried': [ensembl_id, versioned_id],
            'fallback': 'See HPA data below'
        })
else:
    results['gtex'] = gtex_result

# HPA (always query as backup)
hpa_result = tu.tools.HPA_get_rna_expression_by_source(ensembl_id=ensembl_id)
results['hpa'] = hpa_result if hpa_result else {'note': 'No HPA RNA data'}

return results

Human Protein Atlas - Extended Expression (NEW)

HPA provides comprehensive protein expression data including tissue-level, cell-level, and cell line expression.

def get_hpa_comprehensive_expression(tu, gene_symbol): """ Get comprehensive expression data from Human Protein Atlas.

Provides:
- Tissue expression (protein and RNA)
- Subcellular localization
- Cell line expression comparison
- Tissue specificity
"""

# 1. Search for gene to get IDs
gene_info = tu.tools.HPA_search_genes_by_query(search_query=gene_symbol)

if not gene_info:
    return {'error': f'Gene {gene_symbol} not found in HPA'}

# 2. Get tissue expression with specificity
tissue_search = tu.tools.HPA_generic_search(
    search_query=gene_symbol,
    columns="g,gs,rnat,rnatsm,scml,scal",  # Gene, synonyms, tissue specificity, subcellular
    format="json"
)

# 3. Compare expression in cancer cell lines vs normal tissue
cell_lines = ['a549', 'mcf7', 'hela', 'hepg2', 'pc3']
cell_line_expression = {}

for cell_line in cell_lines:
    try:
        expr = tu.tools.HPA_get_comparative_expression_by_gene_and_cellline(
            gene_name=gene_symbol,
            cell_line=cell_line
        )
        cell_line_expression[cell_line] = expr
    except:
        continue

return {
    'gene_info': gene_info,
    'tissue_data': tissue_search,
    'cell_line_expression': cell_line_expression,
    'source': 'Human Protein Atlas'
}

HPA Expression Output for Report:

Tissue Expression Profile (Human Protein Atlas)

Subcellular Localization: Cytoplasm, Plasma membrane

Cancer Cell Line Expression

Source: Human Protein Atlas via HPA_search_genes_by_query, HPA_get_comparative_expression_by_gene_and_cellline

Why HPA for Target Research:

• Drug target validation - Confirm expression in target tissue

• Safety assessment - Expression in essential organs

• Biomarker potential - Tissue-specific expression

• Cell line selection - Choose appropriate models

PATH 6: Variants & Disease (Enhanced)

6.1 ClinVar SNV vs CNV Separation

8.3 Clinical Variants (ClinVar)

Single Nucleotide Variants (SNVs)

Total Pathogenic SNVs: 47

Copy Number Variants (CNVs) - Reported Separately

Note: CNV data separated as it represents different mutation mechanism

6.2 DisGeNET Integration (NEW)

DisGeNET provides curated gene-disease associations with evidence scores. Requires: DISGENET_API_KEY

def get_disgenet_associations(tu, ids): """ Get gene-disease associations from DisGeNET. Complements Open Targets with curated association scores. """ symbol = ids.get('symbol') if not symbol: return {'status': 'skipped', 'reason': 'No gene symbol'}

# Get all disease associations for gene
gda = tu.tools.DisGeNET_search_gene(
    operation="search_gene",
    gene=symbol,
    limit=50
)

if gda.get('status') != 'success':
    return {'status': 'error', 'message': 'DisGeNET query failed'}

associations = gda.get('data', {}).get('associations', [])

# Categorize by evidence strength
strong = []     # score >= 0.7
moderate = []   # score 0.4-0.7  
weak = []       # score < 0.4

for assoc in associations:
    score = assoc.get('score', 0)
    disease_name = assoc.get('disease_name', '')
    umls_cui = assoc.get('disease_id', '')
    
    entry = {
        'disease': disease_name,
        'umls_cui': umls_cui,
        'score': score,
        'evidence_index': assoc.get('ei'),
        'dsi': assoc.get('dsi'),  # Disease Specificity Index
        'dpi': assoc.get('dpi')   # Disease Pleiotropy Index
    }
    
    if score >= 0.7:
        strong.append(entry)
    elif score >= 0.4:
        moderate.append(entry)
    else:
        weak.append(entry)

return {
    'total_associations': len(associations),
    'strong_associations': strong,
    'moderate_associations': moderate,
    'weak_associations': weak[:10],  # Limit weak
    'disease_pleiotropy': len(associations)  # How many diseases linked
}

DisGeNET Report Section (add to Section 8 - Disease Associations):

8.x DisGeNET Gene-Disease Associations (NEW)

Total Diseases Associated: 47
Disease Pleiotropy Index: High (gene linked to many disease types)

Strong Associations (Score ≥0.7)

Moderate Associations (Score 0.4-0.7)

Note: DisGeNET score integrates curated databases, GWAS, animal models, and literature

Evidence Tier Assignment:

• DisGeNET Score ≥0.7 → Consider T2 evidence (multiple validated sources)

• DisGeNET Score 0.4-0.7 → Consider T3 evidence

• DisGeNET Score <0.4 → T4 evidence only

PATH 7: Druggability & Target Validation (ENHANCED)

7.1 Pharos/TCRD - Target Development Level (NEW)

NIH's Illuminating the Druggable Genome (IDG) portal provides TDL classification for all human proteins:

def get_pharos_target_info(tu, ids): """ Get Pharos/TCRD target development level and druggability.

TDL Classification:
- Tclin: Approved drug targets
- Tchem: Targets with small molecule activities (IC50 < 30nM)
- Tbio: Targets with biological annotations
- Tdark: Understudied proteins
"""
gene_symbol = ids.get('symbol')
uniprot = ids.get('uniprot')

# Try by gene symbol first
if gene_symbol:
    result = tu.tools.Pharos_get_target(
        gene=gene_symbol
    )
elif uniprot:
    result = tu.tools.Pharos_get_target(
        uniprot=uniprot
    )
else:
    return {'status': 'error', 'message': 'Need gene symbol or UniProt'}

if result.get('status') == 'success' and result.get('data'):
    target = result['data']
    return {
        'name': target.get('name'),
        'symbol': target.get('sym'),
        'tdl': target.get('tdl'),  # Tclin/Tchem/Tbio/Tdark
        'family': target.get('fam'),  # Kinase, GPCR, etc.
        'novelty': target.get('novelty'),
        'description': target.get('description'),
        'publications': target.get('publicationCount'),
        'interpretation': interpret_tdl(target.get('tdl'))
    }
return None

def interpret_tdl(tdl): """Interpret Target Development Level for druggability.""" interpretations = { 'Tclin': 'Approved drug target - highest confidence for druggability', 'Tchem': 'Small molecule active - good chemical tractability', 'Tbio': 'Biologically characterized - may require novel modalities', 'Tdark': 'Understudied - limited data, high novelty potential' } return interpretations.get(tdl, 'Unknown')

def search_disease_targets(tu, disease_name): """Find targets associated with a disease via Pharos."""

result = tu.tools.Pharos_get_disease_targets(
    disease=disease_name,
    top=50
)

if result.get('status') == 'success':
    targets = result['data'].get('targets', [])
    # Group by TDL for prioritization
    by_tdl = {'Tclin': [], 'Tchem': [], 'Tbio': [], 'Tdark': []}
    for t in targets:
        tdl = t.get('tdl', 'Unknown')
        if tdl in by_tdl:
            by_tdl[tdl].append(t)
    return by_tdl
return None

Pharos Report Section (add to Section 9 - Druggability):

9.x Pharos/TCRD Target Classification (NEW)

Target Development Level: Tchem
Protein Family: Kinase
Novelty Score: 0.35 (moderately studied)
Publication Count: 12,456

TDL Interpretation: Target has validated small molecule activities with IC50 < 30nM. Good chemical starting points exist.

Disease Targets Analysis (for disease-centric queries):

Source: Pharos/TCRD via Pharos_get_target

7.2 DepMap - Target Essentiality Validation (NEW)

CRISPR knockout data from cancer cell lines to validate target essentiality:

def assess_target_essentiality(tu, ids): """ Is this target essential for cancer cell survival?

Negative effect scores = gene is essential (cells die upon KO)
"""
gene_symbol = ids.get('symbol')

if not gene_symbol:
    return {'status': 'error', 'message': 'Need gene symbol'}

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

if deps.get('status') == 'success':
    return {
        'gene': gene_symbol,
        'data': deps.get('data', {}),
        'interpretation': 'Negative scores indicate gene is essential for cell survival',
        'note': 'Score < -0.5 is strongly essential, < -1.0 is extremely essential'
    }
return None

def get_cancer_type_essentiality(tu, gene_symbol, cancer_type): """Check if gene is essential in specific cancer type."""

# Get cell lines for cancer type
cell_lines = tu.tools.DepMap_get_cell_lines(
    cancer_type=cancer_type,
    page_size=20
)

return {
    'gene': gene_symbol,
    'cancer_type': cancer_type,
    'cell_lines': cell_lines.get('data', {}).get('cell_lines', []),
    'note': 'Query individual cell lines for dependency scores via DepMap portal'
}

DepMap Report Section (add to Section 9 - Druggability):

9.x Target Essentiality (DepMap) (NEW)

Gene Essentiality Assessment:

Selectivity: Differential essentiality suggests cancer-type selective target

Cell Lines Tested: 1,054 cancer cell lines from DepMap

Interpretation: Score < -0.5 indicates strong dependency. This target is more essential in lung cancer than other cancer types - suggesting lung-selective targeting may be feasible.

Source: DepMap via DepMap_get_gene_dependencies

7.3 InterProScan - Novel Domain Prediction (NEW)

For uncharacterized proteins, run InterProScan to predict domains and function:

def predict_protein_domains(tu, sequence, title="Query protein"): """ Run InterProScan for de novo domain prediction.

Use when:
- Protein has no InterPro annotations
- Novel/uncharacterized protein
- Custom sequence analysis
"""

result = tu.tools.InterProScan_scan_sequence(
    sequence=sequence,
    title=title,
    go_terms=True,
    pathways=True
)

if result.get('status') == 'success':
    data = result.get('data', {})
    
    # Job may still be running
    if data.get('job_status') == 'RUNNING':
        return {
            'job_id': data.get('job_id'),
            'status': 'running',
            'note': 'Use InterProScan_get_job_results to retrieve when ready'
        }
    
    # Parse completed results
    return {
        'domains': data.get('domains', []),
        'domain_count': data.get('domain_count', 0),
        'go_annotations': data.get('go_annotations', []),
        'pathways': data.get('pathways', []),
        'sequence_length': data.get('sequence_length')
    }
return None

def check_interproscan_job(tu, job_id): """Check status and get results for InterProScan job."""

status = tu.tools.InterProScan_get_job_status(job_id=job_id)

if status.get('data', {}).get('is_finished'):
    results = tu.tools.InterProScan_get_job_results(job_id=job_id)
    return results.get('data', {})

return status.get('data', {})

When to use InterProScan:

• Novel/uncharacterized proteins (Tdark in Pharos)

• Custom sequences (e.g., protein variants)

• Proteins with outdated/sparse InterPro annotations

• Validating domain predictions

InterProScan Report Section (for novel proteins):

Domain Prediction (InterProScan) (NEW)

Used for uncharacterized protein analysis

Predicted Domains:

Predicted GO Terms:

• GO:0004672 protein kinase activity
• GO:0005524 ATP binding

Predicted Pathways:

• Reactome: Signal Transduction

Source: InterProScan via InterProScan_scan_sequence

7.4 BindingDB - Known Ligands & Binding Data (NEW)

BindingDB provides experimental binding affinity data (Ki, IC50, Kd) for target-ligand pairs:

def get_bindingdb_ligands(tu, uniprot_id, affinity_cutoff=10000): """ Get ligands with measured binding affinities from BindingDB.

Critical for:
- Identifying chemical starting points
- Understanding existing chemical matter
- Assessing tractability with small molecules

Args:
    uniprot_id: UniProt accession (e.g., P00533 for EGFR)
    affinity_cutoff: Maximum affinity in nM (lower = more potent)
"""

# Get ligands by UniProt
result = tu.tools.BindingDB_get_ligands_by_uniprot(
    uniprot=uniprot_id,
    affinity_cutoff=affinity_cutoff
)

if result:
    ligands = []
    for entry in result:
        ligands.append({
            'smiles': entry.get('smile'),
            'affinity_type': entry.get('affinity_type'),  # Ki, IC50, Kd
            'affinity_nM': entry.get('affinity'),
            'monomer_id': entry.get('monomerid'),
            'pmid': entry.get('pmid')
        })
    
    # Sort by affinity (most potent first)
    ligands.sort(key=lambda x: float(x['affinity_nM']) if x['affinity_nM'] else float('inf'))
    
    return {
        'total_ligands': len(ligands),
        'ligands': ligands[:20],  # Top 20 most potent
        'best_affinity': ligands[0]['affinity_nM'] if ligands else None
    }

return {'total_ligands': 0, 'ligands': [], 'note': 'No ligands found in BindingDB'}

def get_ligands_by_structure(tu, pdb_id, affinity_cutoff=10000): """Get ligands for a protein by PDB structure ID."""

result = tu.tools.BindingDB_get_ligands_by_pdb(
    pdb_ids=pdb_id,
    affinity_cutoff=affinity_cutoff,
    sequence_identity=100
)

return result

def find_compound_targets(tu, smiles, similarity_cutoff=0.85): """Find other targets for a compound (polypharmacology)."""

result = tu.tools.BindingDB_get_targets_by_compound(
    smiles=smiles,
    similarity_cutoff=similarity_cutoff
)

return result

BindingDB Report Section (add to Section 9 - Druggability):

Known Ligands (BindingDB) (NEW)

Total Ligands with Binding Data: 156 Best Reported Affinity: 0.3 nM (Ki)

Most Potent Ligands

Chemical Tractability Assessment:

• ✅ Tchem-level target: Multiple ligands with <30nM affinity
• ✅ Diverse chemotypes: Multiple scaffolds identified
• ✅ Published literature: Ligands have PMID references

Source: BindingDB via BindingDB_get_ligands_by_uniprot

Affinity Interpretation for Druggability:

Affinity Range Interpretation Drug Development Potential

<1 nM Ultra-potent Clinical compound likely

1-10 nM Highly potent Drug-like

10-100 nM Potent Good starting point

100-1000 nM Moderate Needs optimization

1000 nM Weak Early hit only

7.5 PubChem BioAssay - Screening Data (NEW)

PubChem BioAssay provides HTS screening data and dose-response curves:

def get_pubchem_assays_for_target(tu, gene_symbol): """ Get bioassays targeting a gene from PubChem.

Provides:
- HTS screening results
- Dose-response data (IC50/EC50)
- Active compound counts
"""

# Search assays by target gene
assays = tu.tools.PubChem_search_assays_by_target_gene(
    gene_symbol=gene_symbol
)

assay_info = []
if assays.get('data', {}).get('aids'):
    for aid in assays['data']['aids'][:10]:  # Top 10 assays
        # Get assay details
        summary = tu.tools.PubChem_get_assay_summary(aid=aid)
        targets = tu.tools.PubChem_get_assay_targets(aid=aid)
        
        assay_info.append({
            'aid': aid,
            'summary': summary.get('data', {}),
            'targets': targets.get('data', {})
        })

return {
    'total_assays': len(assays.get('data', {}).get('aids', [])),
    'assay_details': assay_info
}

def get_active_compounds_from_assay(tu, aid): """Get active compounds from a specific bioassay."""

actives = tu.tools.PubChem_get_assay_active_compounds(aid=aid)

return {
    'aid': aid,
    'active_cids': actives.get('data', {}).get('cids', []),
    'count': len(actives.get('data', {}).get('cids', []))
}

PubChem BioAssay Report Section:

PubChem BioAssay Data (NEW)

Assays Targeting This Gene: 45

Total Active Compounds Across Assays: ~500

Source: PubChem via PubChem_search_assays_by_target_gene, PubChem_get_assay_active_compounds

PATH 8: Literature & Research (Collision-Aware)

Collision-Aware Query Strategy

def path_literature_collision_aware(tu, ids): """ Literature search with collision detection and filtering. """ symbol = ids['symbol'] full_name = ids.get('full_name', '') uniprot = ids['uniprot'] synonyms = ids.get('synonyms', [])

# Step 1: Detect collisions
collision_filter = detect_collisions(tu, symbol, full_name)

# Step 2: Build high-precision seed queries
seed_queries = [
    f'"{symbol}"[Title] AND (protein OR gene OR expression)',  # Symbol in title
    f'"{full_name}"[Title]' if full_name else None,  # Full name in title
    f'"UniProt:{uniprot}"' if uniprot else None,  # UniProt accession
]
seed_queries = [q for q in seed_queries if q]

# Add key synonyms
for syn in synonyms[:3]:
    seed_queries.append(f'"{syn}"[Title]')

# Step 3: Execute seed queries and collect PMIDs
seed_pmids = set()
for query in seed_queries:
    if collision_filter:
        query = f"({query}){collision_filter}"
    results = tu.tools.PubMed_search_articles(query=query, limit=30)
    for article in results.get('articles', []):
        seed_pmids.add(article.get('pmid'))

# Step 4: Expand via citation network (for sparse targets)
if len(seed_pmids) < 30:
    expanded_pmids = set()
    for pmid in list(seed_pmids)[:10]:  # Top 10 seeds
        # Get related articles
        related = tu.tools.PubMed_get_related(pmid=pmid, limit=20)
        for r in related.get('articles', []):
            expanded_pmids.add(r.get('pmid'))
        
        # Get citing articles
        citing = tu.tools.EuropePMC_get_citations(pmid=pmid, limit=20)
        for c in citing.get('citations', []):
            expanded_pmids.add(c.get('pmid'))
    
    seed_pmids.update(expanded_pmids)

# Step 5: Classify papers by evidence tier
papers_by_tier = {'T1': [], 'T2': [], 'T3': [], 'T4': []}
# ... classification logic based on title/abstract keywords

return {
    'total_papers': len(seed_pmids),
    'collision_filter_applied': collision_filter if collision_filter else 'None needed',
    'seed_queries': seed_queries,
    'papers_by_tier': papers_by_tier
}

Retry Logic & Fallback Chains

Retry Policy

For each critical tool, implement retry with exponential backoff:

def call_with_retry(tu, tool_name, params, max_retries=3): """ Call tool with retry logic. """ for attempt in range(max_retries): try: result = getattr(tu.tools, tool_name)(**params) if result and not result.get('error'): return result except Exception as e: if attempt < max_retries - 1: time.sleep(2 ** attempt) # Exponential backoff else: return {'error': str(e), 'tool': tool_name, 'attempts': max_retries} return None

Fallback Chains (CRITICAL)

Primary Tool Fallback 1 Fallback 2 Failure Action

ChEMBL_get_target_activities

GtoPdb_get_target_ligands

OpenTargets drugs

Note in report

intact_get_interactions

STRING_get_protein_interactions

OpenTargets interactions

Note in report

GO_get_annotations_for_gene

OpenTargets GO

MyGene GO

Note in report

GTEx_get_median_gene_expression

HPA_get_rna_expression

Note as unavailable Document in report

gnomad_get_gene_constraints

OpenTargets constraint

Note in report

DGIdb_get_drug_gene_interactions

OpenTargets drugs

GtoPdb

Note in report

Failure Surfacing Rule

NEVER silently skip failed tools. Always document:

7.1 Tissue Expression

GTEx Data: Unavailable (API timeout after 3 attempts) Fallback Data (HPA):

Note: For complete GTEx data, query directly at gtexportal.org

Per-Section Data Minimums & Completeness Audit

Minimum Data Requirements (Enforced)

Section Minimum Data If Not Met

6. PPIs ≥20 interactors Document which tools failed + why

7. Expression Top 10 tissues with TPM + HPA RNA summary Note "limited data" with specific gaps

8. Disease Top 10 OT diseases + gnomAD constraints + ClinVar summary Separate SNV/CNV; note if constraint unavailable

9. Druggability OT tractability + probes + drugs + DGIdb + GtoPdb fallback "No drugs/probes" is valid data

10. Literature Total count + 5-year trend + 3-5 key papers with evidence tiers Note if sparse (<50 papers)

Post-Run Completeness Audit

Before finalizing the report, run this checklist:

Completeness Audit (REQUIRED)

Data Minimums Check

• PPIs: ≥20 interactors OR explanation why fewer
• Expression: Top 10 tissues with values OR explicit "unavailable"
• Diseases: Top 10 associations with scores OR "no associations"
• Constraints: All 4 scores (pLI, LOEUF, missense Z, pRec) OR "unavailable"
• Druggability: All modalities assessed; probes + drugs listed OR "none"

Negative Results Documented

• Empty tool results noted explicitly (not left blank)
• Failed tools with fallbacks documented
• "No data" sections have implications noted

Evidence Quality

• T1-T4 grades in Executive Summary disease claims
• T1-T4 grades in Disease Associations table
• Key papers table has evidence tiers
• Per-section evidence summaries included

Source Attribution

• Every data point has source tool/database cited
• Section-end source summaries present

Data Gap Table (Required if minimums not met)

15. Data Gaps & Limitations

Recommendations for Data Gaps:

1. For PPIs: Query BioGRID with broader parameters; check yeast-2-hybrid studies
2. For Expression: Query GEO directly for tissue-specific datasets

Report Template (Initial File)

File: [TARGET]_target_report.md

Target Intelligence Report: [TARGET NAME]

Generated: [Date] | Query: [Original query] | Status: In Progress

1. Executive Summary

[Researching...] <!-- REQUIRED: 2-3 sentences, disease claims must have T1-T4 grades -->

2. Target Identifiers

[Researching...] <!-- REQUIRED: UniProt, Ensembl (versioned), Entrez, ChEMBL, HGNC, Symbol -->

3. Basic Information

3.1 Protein Description

[Researching...]

3.2 Protein Function

[Researching...]

3.3 Subcellular Localization

[Researching...]

4. Structural Biology

4.1 Experimental Structures (PDB)

[Researching...] <!-- METHOD: 3-step chain (UniProt xrefs → sequence search → domain search) -->

4.2 AlphaFold Prediction

[Researching...]

4.3 Domain Architecture

[Researching...]

4.4 Key Structural Features

[Researching...]

5. Function & Pathways

5.1 Gene Ontology Annotations

[Researching...] <!-- REQUIRED: Evidence codes mapped to T1-T4 -->

5.2 Pathway Involvement

[Researching...]

6. Protein-Protein Interactions

[Researching...] <!-- MINIMUM: ≥20 interactors OR explanation -->

7. Expression Profile

7.1 Tissue Expression (GTEx/HPA)

[Researching...] <!-- NOTE: Use versioned Ensembl ID for GTEx if needed -->

7.2 Tissue Specificity

[Researching...] <!-- MINIMUM: Top 10 tissues with TPM values -->

8. Genetic Variation & Disease

8.1 Constraint Scores

[Researching...] <!-- REQUIRED: pLI, LOEUF, missense Z, pRec with interpretations -->

8.2 Disease Associations

[Researching...] <!-- REQUIRED: Top 10 with OT scores; T1-T4 evidence grades -->

8.3 Clinical Variants (ClinVar)

[Researching...] <!-- REQUIRED: Separate SNV and CNV tables -->

8.4 Mouse Model Phenotypes

[Researching...]

9. Druggability & Pharmacology

9.1 Tractability Assessment

[Researching...] <!-- REQUIRED: All modalities (SM, Ab, PROTAC, other) -->

9.2 Known Drugs

[Researching...]

9.3 Chemical Probes

[Researching...] <!-- NOTE: "No probes" is valid data - document explicitly -->

9.4 Clinical Pipeline

[Researching...]

9.5 ChEMBL Bioactivity

[Researching...]

10. Safety Profile

10.1 Safety Liabilities

[Researching...]

10.2 Expression-Based Toxicity Risk

[Researching...]

10.3 Mouse KO Phenotypes

[Researching...]

11. Literature & Research Landscape

11.1 Publication Metrics

[Researching...] <!-- REQUIRED: Total, 5y, 1y, drug-related, clinical -->

11.2 Research Trend

[Researching...]

11.3 Key Publications

[Researching...] <!-- REQUIRED: Table with PMID, title, year, evidence tier -->

11.4 Evidence Summary by Theme

[Researching...] <!-- REQUIRED: T1-T4 breakdown per research theme -->

12. Competitive Landscape

[Researching...]

13. Summary & Recommendations

13.1 Target Validation Scorecard

[Researching...] <!-- REQUIRED: 6 criteria, 1-5 scores, evidence quality noted -->

13.2 Strengths

[Researching...]

13.3 Challenges & Risks

[Researching...]

13.4 Recommendations

[Researching...] <!-- REQUIRED: ≥3 prioritized (HIGH/MEDIUM/LOW) -->

14. Data Sources & Methodology

[Will be populated as research progresses...]

15. Data Gaps & Limitations

[To be populated post-audit...]

Quick Reference: Tool Parameters

Tool Parameter Notes

Reactome_map_uniprot_to_pathways

id

NOT uniprot_id

ensembl_get_xrefs

id

NOT gene_id

GTEx_get_median_gene_expression

gencode_id , operation

Try versioned ID if empty

OpenTargets_*

ensemblId

camelCase, not ensemblID

STRING_get_protein_interactions

protein_ids , species

List format for IDs

intact_get_interactions

identifier

UniProt accession

When NOT to Use This Skill

• Simple protein lookup → Use UniProt_get_entry_by_accession directly

• Drug information only → Use drug-focused tools

• Disease-centric query → Use disease-intelligence-gatherer skill

• Sequence retrieval → Use sequence-retrieval skill

• Structure download → Use protein-structure-retrieval skill

Use this skill for comprehensive, multi-angle target analysis with guaranteed data completeness.