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skills/opentargets-database/SKILL.md

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+---
+name: opentargets-database
+description: "Query Open Targets Platform for target-disease associations, drug target discovery, tractability/safety data, genetics/omics evidence, known drugs, for therapeutic target identification."
+---
+
+# Open Targets Database
+
+## Overview
+
+The Open Targets Platform is a comprehensive resource for systematic identification and prioritization of potential therapeutic drug targets. It integrates publicly available datasets including human genetics, omics, literature, and chemical data to build and score target-disease associations.
+
+**Key capabilities:**
+- Query target (gene) annotations including tractability, safety, expression
+- Search for disease-target associations with evidence scores
+- Retrieve evidence from multiple data types (genetics, pathways, literature, etc.)
+- Find known drugs for diseases and their mechanisms
+- Access drug information including clinical trial phases and adverse events
+- Evaluate target druggability and therapeutic potential
+
+**Data access:** The platform provides a GraphQL API, web interface, data downloads, and Google BigQuery access. This skill focuses on the GraphQL API for programmatic access.
+
+## When to Use This Skill
+
+This skill should be used when:
+
+- **Target discovery:** Finding potential therapeutic targets for a disease
+- **Target assessment:** Evaluating tractability, safety, and druggability of genes
+- **Evidence gathering:** Retrieving supporting evidence for target-disease associations
+- **Drug repurposing:** Identifying existing drugs that could be repurposed for new indications
+- **Competitive intelligence:** Understanding clinical precedence and drug development landscape
+- **Target prioritization:** Ranking targets based on genetic evidence and other data types
+- **Mechanism research:** Investigating biological pathways and gene functions
+- **Biomarker discovery:** Finding genes differentially expressed in disease
+- **Safety assessment:** Identifying potential toxicity concerns for drug targets
+
+## Core Workflow
+
+### 1. Search for Entities
+
+Start by finding the identifiers for targets, diseases, or drugs of interest.
+
+**For targets (genes):**
+```python
+from scripts.query_opentargets import search_entities
+
+# Search by gene symbol or name
+results = search_entities("BRCA1", entity_types=["target"])
+# Returns: [{"id": "ENSG00000012048", "name": "BRCA1", ...}]
+```
+
+**For diseases:**
+```python
+# Search by disease name
+results = search_entities("alzheimer", entity_types=["disease"])
+# Returns: [{"id": "EFO_0000249", "name": "Alzheimer disease", ...}]
+```
+
+**For drugs:**
+```python
+# Search by drug name
+results = search_entities("aspirin", entity_types=["drug"])
+# Returns: [{"id": "CHEMBL25", "name": "ASPIRIN", ...}]
+```
+
+**Identifiers used:**
+- Targets: Ensembl gene IDs (e.g., `ENSG00000157764`)
+- Diseases: EFO (Experimental Factor Ontology) IDs (e.g., `EFO_0000249`)
+- Drugs: ChEMBL IDs (e.g., `CHEMBL25`)
+
+### 2. Query Target Information
+
+Retrieve comprehensive target annotations to assess druggability and biology.
+
+```python
+from scripts.query_opentargets import get_target_info
+
+target_info = get_target_info("ENSG00000157764", include_diseases=True)
+
+# Access key fields:
+# - approvedSymbol: HGNC gene symbol
+# - approvedName: Full gene name
+# - tractability: Druggability assessments across modalities
+# - safetyLiabilities: Known safety concerns
+# - geneticConstraint: Constraint scores from gnomAD
+# - associatedDiseases: Top disease associations with scores
+```
+
+**Key annotations to review:**
+- **Tractability:** Small molecule, antibody, PROTAC druggability predictions
+- **Safety:** Known toxicity concerns from multiple databases
+- **Genetic constraint:** pLI and LOEUF scores indicating essentiality
+- **Disease associations:** Diseases linked to the target with evidence scores
+
+Refer to `references/target_annotations.md` for detailed information about all target features.
+
+### 3. Query Disease Information
+
+Get disease details and associated targets/drugs.
+
+```python
+from scripts.query_opentargets import get_disease_info
+
+disease_info = get_disease_info("EFO_0000249", include_targets=True)
+
+# Access fields:
+# - name: Disease name
+# - description: Disease description
+# - therapeuticAreas: High-level disease categories
+# - associatedTargets: Top targets with association scores
+```
+
+### 4. Retrieve Target-Disease Evidence
+
+Get detailed evidence supporting a target-disease association.
+
+```python
+from scripts.query_opentargets import get_target_disease_evidence
+
+# Get all evidence
+evidence = get_target_disease_evidence(
+    ensembl_id="ENSG00000157764",
+    efo_id="EFO_0000249"
+)
+
+# Filter by evidence type
+genetic_evidence = get_target_disease_evidence(
+    ensembl_id="ENSG00000157764",
+    efo_id="EFO_0000249",
+    data_types=["genetic_association"]
+)
+
+# Each evidence record contains:
+# - datasourceId: Specific data source (e.g., "gwas_catalog", "chembl")
+# - datatypeId: Evidence category (e.g., "genetic_association", "known_drug")
+# - score: Evidence strength (0-1)
+# - studyId: Original study identifier
+# - literature: Associated publications
+```
+
+**Major evidence types:**
+1. **genetic_association:** GWAS, rare variants, ClinVar, gene burden
+2. **somatic_mutation:** Cancer Gene Census, IntOGen, cancer biomarkers
+3. **known_drug:** Clinical precedence from approved/clinical drugs
+4. **affected_pathway:** CRISPR screens, pathway analyses, gene signatures
+5. **rna_expression:** Differential expression from Expression Atlas
+6. **animal_model:** Mouse phenotypes from IMPC
+7. **literature:** Text-mining from Europe PMC
+
+Refer to `references/evidence_types.md` for detailed descriptions of all evidence types and interpretation guidelines.
+
+### 5. Find Known Drugs
+
+Identify drugs used for a disease and their targets.
+
+```python
+from scripts.query_opentargets import get_known_drugs_for_disease
+
+drugs = get_known_drugs_for_disease("EFO_0000249")
+
+# drugs contains:
+# - uniqueDrugs: Total number of unique drugs
+# - uniqueTargets: Total number of unique targets
+# - rows: List of drug-target-indication records with:
+#   - drug: {name, drugType, maximumClinicalTrialPhase}
+#   - targets: Genes targeted by the drug
+#   - phase: Clinical trial phase for this indication
+#   - status: Trial status (active, completed, etc.)
+#   - mechanismOfAction: How drug works
+```
+
+**Clinical phases:**
+- Phase 4: Approved drug
+- Phase 3: Late-stage clinical trials
+- Phase 2: Mid-stage trials
+- Phase 1: Early safety trials
+
+### 6. Get Drug Information
+
+Retrieve detailed drug information including mechanisms and indications.
+
+```python
+from scripts.query_opentargets import get_drug_info
+
+drug_info = get_drug_info("CHEMBL25")
+
+# Access:
+# - name, synonyms: Drug identifiers
+# - drugType: Small molecule, antibody, etc.
+# - maximumClinicalTrialPhase: Development stage
+# - mechanismsOfAction: Target and action type
+# - indications: Diseases with trial phases
+# - withdrawnNotice: If withdrawn, reasons and countries
+```
+
+### 7. Get All Associations for a Target
+
+Find all diseases associated with a target, optionally filtering by score.
+
+```python
+from scripts.query_opentargets import get_target_associations
+
+# Get associations with score >= 0.5
+associations = get_target_associations(
+    ensembl_id="ENSG00000157764",
+    min_score=0.5
+)
+
+# Each association contains:
+# - disease: {id, name}
+# - score: Overall association score (0-1)
+# - datatypeScores: Breakdown by evidence type
+```
+
+**Association scores:**
+- Range: 0-1 (higher = stronger evidence)
+- Aggregate evidence across all data types using harmonic sum
+- NOT confidence scores but relative ranking metrics
+- Under-studied diseases may have lower scores despite good evidence
+
+## GraphQL API Details
+
+**For custom queries beyond the provided helper functions**, use the GraphQL API directly or modify `scripts/query_opentargets.py`.
+
+Key information:
+- **Endpoint:** `https://api.platform.opentargets.org/api/v4/graphql`
+- **Interactive browser:** `https://api.platform.opentargets.org/api/v4/graphql/browser`
+- **No authentication required**
+- **Request only needed fields** to minimize response size
+- **Use pagination** for large result sets: `page: {size: N, index: M}`
+
+Refer to `references/api_reference.md` for:
+- Complete endpoint documentation
+- Example queries for all entity types
+- Error handling patterns
+- Best practices for API usage
+
+## Best Practices
+
+### Target Prioritization Strategy
+
+When prioritizing drug targets:
+
+1. **Start with genetic evidence:** Human genetics (GWAS, rare variants) provides strongest disease relevance
+2. **Check tractability:** Prefer targets with clinical or discovery precedence
+3. **Assess safety:** Review safety liabilities, expression patterns, and genetic constraint
+4. **Evaluate clinical precedence:** Known drugs indicate druggability and therapeutic window
+5. **Consider multiple evidence types:** Convergent evidence from different sources increases confidence
+6. **Validate mechanistically:** Pathway evidence and biological plausibility
+7. **Review literature manually:** For critical decisions, examine primary publications
+
+### Evidence Interpretation
+
+**Strong evidence indicators:**
+- Multiple independent evidence sources
+- High genetic association scores (especially GWAS with L2G > 0.5)
+- Clinical precedence from approved drugs
+- ClinVar pathogenic variants with disease match
+- Mouse models with relevant phenotypes
+
+**Caution flags:**
+- Single evidence source only
+- Text-mining as sole evidence (requires manual validation)
+- Conflicting evidence across sources
+- High essentiality + ubiquitous expression (poor therapeutic window)
+- Multiple safety liabilities
+
+**Score interpretation:**
+- Scores rank relative strength, not absolute confidence
+- Under-studied diseases have lower scores despite potentially valid targets
+- Weight expert-curated sources higher than computational predictions
+- Check evidence breakdown, not just overall score
+
+### Common Workflows
+
+**Workflow 1: Target Discovery for a Disease**
+1. Search for disease → get EFO ID
+2. Query disease info with `include_targets=True`
+3. Review top targets sorted by association score
+4. For promising targets, get detailed target info
+5. Examine evidence types supporting each association
+6. Assess tractability and safety for prioritized targets
+
+**Workflow 2: Target Validation**
+1. Search for target → get Ensembl ID
+2. Get comprehensive target info
+3. Check tractability (especially clinical precedence)
+4. Review safety liabilities and genetic constraint
+5. Examine disease associations to understand biology
+6. Look for chemical probes or tool compounds
+7. Check known drugs targeting gene for mechanism insights
+
+**Workflow 3: Drug Repurposing**
+1. Search for disease → get EFO ID
+2. Get known drugs for disease
+3. For each drug, get detailed drug info
+4. Examine mechanisms of action and targets
+5. Look for related disease indications
+6. Assess clinical trial phases and status
+7. Identify repurposing opportunities based on mechanism
+
+**Workflow 4: Competitive Intelligence**
+1. Search for target of interest
+2. Get associated diseases with evidence
+3. For each disease, get known drugs
+4. Review clinical phases and development status
+5. Identify competitors and their mechanisms
+6. Assess clinical precedence and market landscape
+
+## Resources
+
+### Scripts
+
+**scripts/query_opentargets.py**
+Helper functions for common API operations:
+- `search_entities()` - Search for targets, diseases, or drugs
+- `get_target_info()` - Retrieve target annotations
+- `get_disease_info()` - Retrieve disease information
+- `get_target_disease_evidence()` - Get supporting evidence
+- `get_known_drugs_for_disease()` - Find drugs for a disease
+- `get_drug_info()` - Retrieve drug details
+- `get_target_associations()` - Get all associations for a target
+- `execute_query()` - Execute custom GraphQL queries
+
+### References
+
+**references/api_reference.md**
+Complete GraphQL API documentation including:
+- Endpoint details and authentication
+- Available query types (target, disease, drug, search)
+- Example queries for all common operations
+- Error handling and best practices
+- Data licensing and citation requirements
+
+**references/evidence_types.md**
+Comprehensive guide to evidence types and data sources:
+- Detailed descriptions of all 7 major evidence types
+- Scoring methodologies for each source
+- Evidence interpretation guidelines
+- Strengths and limitations of each evidence type
+- Quality assessment recommendations
+
+**references/target_annotations.md**
+Complete target annotation reference:
+- 12 major annotation categories explained
+- Tractability assessment details
+- Safety liability sources
+- Expression, essentiality, and constraint data
+- Interpretation guidelines for target prioritization
+- Red flags and green flags for target assessment
+
+## Data Updates and Versioning
+
+The Open Targets Platform is updated **quarterly** with new data releases. The current release (as of October 2025) is available at the API endpoint.
+
+**Release information:** Check https://platform-docs.opentargets.org/release-notes for the latest updates.
+
+**Citation:** When using Open Targets data, cite:
+Ochoa, D. et al. (2025) Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery. Nucleic Acids Research, 53(D1):D1467-D1477.
+
+## Limitations and Considerations
+
+1. **API is for exploratory queries:** For systematic analyses of many targets/diseases, use data downloads or BigQuery
+2. **Scores are relative, not absolute:** Association scores rank evidence strength but don't predict clinical success
+3. **Under-studied diseases score lower:** Novel or rare diseases may have strong evidence but lower aggregate scores
+4. **Evidence quality varies:** Weight expert-curated sources higher than computational predictions
+5. **Requires biological interpretation:** Scores and evidence must be interpreted in biological and clinical context
+6. **No authentication required:** All data is freely accessible, but cite appropriately

skills/opentargets-database/references/api_reference.md

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+# Open Targets Platform API Reference
+
+## API Endpoint
+
+```
+https://api.platform.opentargets.org/api/v4/graphql
+```
+
+Interactive GraphQL playground with documentation:
+```
+https://api.platform.opentargets.org/api/v4/graphql/browser
+```
+
+## Access Methods
+
+The Open Targets Platform provides multiple access methods:
+
+1. **GraphQL API** - Best for single entity queries and flexible data retrieval
+2. **Web Interface** - Interactive platform at https://platform.opentargets.org
+3. **Data Downloads** - FTP at https://ftp.ebi.ac.uk/pub/databases/opentargets/platform/
+4. **Google BigQuery** - For large-scale systematic queries
+
+## Authentication
+
+No authentication is required for the GraphQL API. All data is freely accessible.
+
+## Rate Limits
+
+For systematic queries involving multiple targets or diseases, use dataset downloads or BigQuery instead of repeated API calls. The API is optimized for single-entity and exploratory queries.
+
+## GraphQL Query Structure
+
+GraphQL queries consist of:
+1. Query operation with optional variables
+2. Field selection (request only needed fields)
+3. Nested entity traversal
+
+### Basic Python Example
+
+```python
+import requests
+import json
+
+# Define the query
+query_string = """
+  query target($ensemblId: String!){
+    target(ensemblId: $ensemblId){
+      id
+      approvedSymbol
+      biotype
+      geneticConstraint {
+        constraintType
+        exp
+        obs
+        score
+      }
+    }
+  }
+"""
+
+# Define variables
+variables = {"ensemblId": "ENSG00000169083"}
+
+# Make the request
+base_url = "https://api.platform.opentargets.org/api/v4/graphql"
+response = requests.post(base_url, json={"query": query_string, "variables": variables})
+data = json.loads(response.text)
+print(data)
+```
+
+## Available Query Endpoints
+
+### /target
+Retrieve gene annotations, tractability assessments, and disease associations.
+
+**Common fields:**
+- `id` - Ensembl gene ID
+- `approvedSymbol` - HGNC gene symbol
+- `approvedName` - Full gene name
+- `biotype` - Gene type (protein_coding, etc.)
+- `tractability` - Druggability assessment
+- `safetyLiabilities` - Safety information
+- `expressions` - Baseline expression data
+- `knownDrugs` - Approved/clinical drugs
+- `associatedDiseases` - Disease associations with evidence
+
+### /disease
+Retrieve disease/phenotype data, known drugs, and clinical information.
+
+**Common fields:**
+- `id` - EFO disease identifier
+- `name` - Disease name
+- `description` - Disease description
+- `therapeuticAreas` - High-level disease categories
+- `synonyms` - Alternative names
+- `knownDrugs` - Drugs indicated for disease
+- `associatedTargets` - Target associations with evidence
+
+### /drug
+Retrieve compound details, mechanisms of action, and pharmacovigilance data.
+
+**Common fields:**
+- `id` - ChEMBL identifier
+- `name` - Drug name
+- `drugType` - Small molecule, antibody, etc.
+- `maximumClinicalTrialPhase` - Development stage
+- `indications` - Disease indications
+- `mechanismsOfAction` - Target mechanisms
+- `adverseEvents` - Pharmacovigilance data
+
+### /search
+Search across all entities (targets, diseases, drugs).
+
+**Parameters:**
+- `queryString` - Search term
+- `entityNames` - Filter by entity type(s)
+- `page` - Pagination
+
+### /associationDiseaseIndirect
+Retrieve target-disease associations including indirect evidence from disease descendants in ontology.
+
+**Key fields:**
+- `rows` - Association records with scores
+- `aggregations` - Aggregated statistics
+
+## Example Queries
+
+### Query 1: Get target information with disease associations
+
+```python
+query = """
+  query targetInfo($ensemblId: String!) {
+    target(ensemblId: $ensemblId) {
+      approvedSymbol
+      approvedName
+      tractability {
+        label
+        modality
+        value
+      }
+      associatedDiseases(page: {size: 10}) {
+        rows {
+          disease {
+            name
+          }
+          score
+          datatypeScores {
+            componentId
+            score
+          }
+        }
+      }
+    }
+  }
+"""
+variables = {"ensemblId": "ENSG00000157764"}
+```
+
+### Query 2: Search for diseases
+
+```python
+query = """
+  query searchDiseases($queryString: String!) {
+    search(queryString: $queryString, entityNames: ["disease"]) {
+      hits {
+        id
+        entity
+        name
+        description
+      }
+    }
+  }
+"""
+variables = {"queryString": "alzheimer"}
+```
+
+### Query 3: Get evidence for target-disease pair
+
+```python
+query = """
+  query evidences($ensemblId: String!, $efoId: String!) {
+    disease(efoId: $efoId) {
+      evidences(ensemblIds: [$ensemblId], size: 100) {
+        rows {
+          datasourceId
+          datatypeId
+          score
+          studyId
+          literature
+        }
+      }
+    }
+  }
+"""
+variables = {"ensemblId": "ENSG00000157764", "efoId": "EFO_0000249"}
+```
+
+### Query 4: Get known drugs for a disease
+
+```python
+query = """
+  query knownDrugs($efoId: String!) {
+    disease(efoId: $efoId) {
+      knownDrugs {
+        uniqueDrugs
+        rows {
+          drug {
+            name
+            id
+          }
+          targets {
+            approvedSymbol
+          }
+          phase
+          status
+        }
+      }
+    }
+  }
+"""
+variables = {"efoId": "EFO_0000249"}
+```
+
+## Error Handling
+
+GraphQL returns status code 200 even for errors. Check the response structure:
+
+```python
+if 'errors' in response_data:
+    print(f"GraphQL errors: {response_data['errors']}")
+else:
+    print(f"Data: {response_data['data']}")
+```
+
+## Best Practices
+
+1. **Request only needed fields** - Minimize data transfer and improve response time
+2. **Use variables** - Make queries reusable and safer
+3. **Handle pagination** - Most list fields support pagination with `page: {size: N, index: M}`
+4. **Explore the schema** - Use the GraphQL browser to discover available fields
+5. **Batch related queries** - Combine multiple entity fetches in a single query when possible
+6. **Cache results** - Store frequently accessed data locally to reduce API calls
+7. **Use BigQuery for bulk** - Switch to BigQuery/downloads for systematic analyses
+
+## Data Licensing
+
+All Open Targets Platform data is freely available. When using the data in research or commercial products, cite the latest publication:
+
+Ochoa, D. et al. (2025) Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery. Nucleic Acids Research, 53(D1):D1467-D1477.

skills/opentargets-database/references/evidence_types.md

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+# Evidence Types and Data Sources
+
+## Overview
+
+Evidence represents any event or set of events that identifies a target as a potential causal gene or protein for a disease. Evidence is standardized and mapped to:
+- **Ensembl gene IDs** for targets
+- **EFO (Experimental Factor Ontology)** for diseases/phenotypes
+
+Evidence is organized into **data types** (broader categories) and **data sources** (specific databases/studies).
+
+## Evidence Data Types
+
+### 1. Genetic Association
+
+Evidence from human genetics linking genetic variants to disease phenotypes.
+
+#### Data Sources:
+
+**GWAS (Genome-Wide Association Studies)**
+- Population-level common variant associations
+- Filtered with Locus-to-Gene (L2G) scores >0.05
+- Includes fine-mapping and colocalization data
+- Sources: GWAS Catalog, FinnGen, UK Biobank, EBI GWAS
+
+**Gene Burden Tests**
+- Rare variant association analyses
+- Aggregate effects of multiple rare variants in a gene
+- Particularly relevant for Mendelian and rare diseases
+
+**ClinVar Germline**
+- Clinical variant interpretations
+- Classifications: pathogenic, likely pathogenic, VUS, benign
+- Expert-reviewed variant-disease associations
+
+**Genomics England PanelApp**
+- Expert gene-disease ratings
+- Green (confirmed), amber (probable), red (no evidence)
+- Focus on rare diseases and cancer
+
+**Gene2Phenotype**
+- Curated gene-disease relationships
+- Allelic requirements and inheritance patterns
+- Clinical validity assessments
+
+**UniProt Literature & Variants**
+- Literature-based gene-disease associations
+- Expert-curated from scientific publications
+
+**Orphanet**
+- Rare disease gene associations
+- Expert-reviewed and maintained
+
+**ClinGen**
+- Clinical genome resource classifications
+- Gene-disease validity assertions
+
+### 2. Somatic Mutations
+
+Evidence from cancer genomics identifying driver genes and therapeutic targets.
+
+#### Data Sources:
+
+**Cancer Gene Census**
+- Expert-curated cancer genes
+- Tier classifications (1 = strong evidence, 2 = emerging)
+- Mutation types and cancer types
+
+**IntOGen**
+- Computational driver gene predictions
+- Aggregated from large cohort studies
+- Statistical significance of mutations
+
+**ClinVar Somatic**
+- Somatic clinical variant interpretations
+- Oncogenic/likely oncogenic classifications
+
+**Cancer Biomarkers**
+- FDA/EMA approved biomarkers
+- Clinical trial biomarkers
+- Prognostic and predictive markers
+
+### 3. Known Drugs
+
+Evidence from clinical precedence showing drugs targeting genes for disease indications.
+
+#### Data Source:
+
+**ChEMBL**
+- Approved drugs (Phase 4)
+- Clinical candidates (Phase 1-3)
+- Withdrawn drugs
+- Drug-target-indication triplets with mechanism of action
+
+**Clinical Trial Information:**
+- `phase`: Maximum clinical trial phase (1, 2, 3, 4)
+- `status`: Active, terminated, completed, withdrawn
+- `mechanismOfAction`: How drug affects target
+
+### 4. Affected Pathways
+
+Evidence linking genes to disease through pathway perturbations and functional screens.
+
+#### Data Sources:
+
+**CRISPR Screens**
+- Genome-scale knockout screens
+- Cancer dependency and essentiality data
+
+**Project Score (Cancer Dependency Map)**
+- CRISPR-Cas9 fitness screens across cancer cell lines
+- Gene essentiality profiles
+
+**SLAPenrich**
+- Pathway enrichment analysis
+- Somatic mutation pathway impacts
+
+**PROGENy**
+- Pathway activity inference
+- Signaling pathway perturbations
+
+**Reactome**
+- Expert-curated pathway annotations
+- Biological pathway representations
+
+**Gene Signatures**
+- Expression-based signatures
+- Pathway activity patterns
+
+### 5. RNA Expression
+
+Evidence from differential gene expression in disease vs. control tissues.
+
+#### Data Source:
+
+**Expression Atlas**
+- Differential expression data
+- Baseline expression across tissues/conditions
+- RNA-Seq and microarray studies
+- Log2 fold-change and p-values
+
+### 6. Animal Models
+
+Evidence from in vivo studies showing phenotypes associated with gene perturbations.
+
+#### Data Source:
+
+**IMPC (International Mouse Phenotyping Consortium)**
+- Systematic mouse knockout phenotypes
+- Phenotype-disease mappings via ontologies
+- Standardized phenotyping procedures
+
+### 7. Literature
+
+Evidence from text-mining of biomedical literature.
+
+#### Data Source:
+
+**Europe PMC**
+- Co-occurrence of genes and diseases in abstracts
+- Normalized citation counts
+- Weighted by publication type and recency
+
+## Evidence Scoring
+
+Each evidence source has its own scoring methodology:
+
+### Score Ranges
+- Most scores normalized to 0-1 range
+- Higher scores indicate stronger evidence
+- Scores are NOT confidence levels but relative strength indicators
+
+### Common Scoring Approaches:
+
+**Binary Classifications:**
+- ClinVar: Pathogenic (1.0), Likely pathogenic (0.99), etc.
+- Gene2Phenotype: Confirmed/probable ratings
+- PanelApp: Green/amber/red classifications
+
+**Statistical Measures:**
+- GWAS: L2G scores incorporating multiple lines of evidence
+- Gene Burden: Statistical significance of variant aggregation
+- Expression: Adjusted p-values and fold-changes
+
+**Clinical Precedence:**
+- Known Drugs: Phase weights (Phase 4 = 1.0, Phase 3 = 0.8, etc.)
+- Clinical status modifiers
+
+**Computational Predictions:**
+- IntOGen: Q-values from driver mutation analysis
+- PROGENy/SLAPenrich: Pathway activity/enrichment scores
+
+## Evidence Interpretation Guidelines
+
+### Strengths by Data Type
+
+**Genetic Association** - Strongest human genetic evidence
+- Direct link between genetic variation and disease
+- Mendelian diseases: high confidence
+- GWAS: requires L2G to identify causal gene
+- Consider ancestry and population-specific effects
+
+**Somatic Mutations** - Direct evidence in cancer
+- Strong for oncology indications
+- Driver mutations indicate therapeutic potential
+- Consider cancer type specificity
+
+**Known Drugs** - Clinical validation
+- Highest confidence: approved drugs (Phase 4)
+- Consider mechanism relevance to new indication
+- Phase 1-2: early evidence, higher risk
+
+**Affected Pathways** - Mechanistic insights
+- Supports biological plausibility
+- May not predict clinical success
+- Useful for hypothesis generation
+
+**RNA Expression** - Observational evidence
+- Correlation, not causation
+- May reflect disease consequence vs. cause
+- Useful for biomarker identification
+
+**Animal Models** - Translational evidence
+- Strong for understanding biology
+- Variable translation to human disease
+- Most useful when phenotype matches human disease
+
+**Literature** - Exploratory signal
+- Text-mining captures research focus
+- May reflect publication bias
+- Requires manual literature review for validation
+
+### Important Considerations
+
+1. **Multiple evidence types strengthen confidence** - Convergent evidence from different data types provides stronger support
+
+2. **Under-studied diseases score lower** - Novel or rare diseases may have strong evidence but lower aggregate scores due to limited research
+
+3. **Association scores are not probabilities** - Scores rank relative evidence strength, not success probability
+
+4. **Context matters** - Evidence strength depends on:
+   - Disease mechanism understanding
+   - Target biology and druggability
+   - Clinical precedence in related indications
+   - Safety considerations
+
+5. **Data source reliability varies** - Weight expert-curated sources (ClinGen, Gene2Phenotype) higher than computational predictions
+
+## Using Evidence in Queries
+
+### Filtering by Data Type
+
+```python
+query = """
+  query evidenceByType($ensemblId: String!, $efoId: String!, $dataTypes: [String!]) {
+    disease(efoId: $efoId) {
+      evidences(ensemblIds: [$ensemblId], datatypes: $dataTypes) {
+        rows {
+          datasourceId
+          score
+        }
+      }
+    }
+  }
+"""
+variables = {
+    "ensemblId": "ENSG00000157764",
+    "efoId": "EFO_0000249",
+    "dataTypes": ["genetic_association", "somatic_mutation"]
+}
+```
+
+### Accessing Data Type Scores
+
+Data type scores aggregate all source scores within that type:
+
+```python
+query = """
+  query associationScores($ensemblId: String!, $efoId: String!) {
+    target(ensemblId: $ensemblId) {
+      associatedDiseases(efoIds: [$efoId]) {
+        rows {
+          disease {
+            name
+          }
+          score
+          datatypeScores {
+            componentId
+            score
+          }
+        }
+      }
+    }
+  }
+"""
+```
+
+## Evidence Quality Assessment
+
+When evaluating evidence:
+
+1. **Check multiple sources** - Single source may be unreliable
+2. **Prioritize human genetic evidence** - Strongest disease relevance
+3. **Consider clinical precedence** - Known drugs indicate druggability
+4. **Assess mechanistic support** - Pathway evidence supports biology
+5. **Review literature manually** - For critical decisions, read primary publications
+6. **Validate in primary databases** - Cross-reference with ClinVar, ClinGen, etc.

skills/opentargets-database/references/target_annotations.md

@@ -0,0 +1,401 @@
+# Target Annotations and Features
+
+## Overview
+
+Open Targets defines a target as "any naturally-occurring molecule that can be targeted by a medicinal product." Targets are primarily protein-coding genes identified by Ensembl gene IDs, but also include RNAs and pseudogenes from canonical chromosomes.
+
+## Core Target Annotations
+
+### 1. Tractability Assessment
+
+Tractability evaluates the druggability potential of a target across different modalities.
+
+#### Modalities Assessed:
+
+**Small Molecule**
+- Prediction of small molecule druggability
+- Based on structural features, chemical precedence
+- Buckets: Clinical precedence, Discovery precedence, Predicted tractable
+
+**Antibody**
+- Likelihood of antibody-based therapeutic success
+- Cell surface/secreted protein location
+- Precedence categories similar to small molecules
+
+**PROTAC (Protein Degradation)**
+- Assessment for targeted protein degradation
+- E3 ligase compatibility
+- Emerging modality category
+
+**Other Modalities**
+- Gene therapy, RNA-based therapeutics
+- Oligonucleotide approaches
+
+#### Tractability Levels:
+
+1. **Clinical Precedence** - Target of approved/clinical drug with similar mechanism
+2. **Discovery Precedence** - Target of tool compounds or compounds in preclinical development
+3. **Predicted Tractable** - Computational predictions suggest druggability
+4. **Unknown** - Insufficient data to assess
+
+### 2. Safety Liabilities
+
+Safety information aggregated from multiple sources to identify potential toxicity concerns.
+
+#### Data Sources:
+
+**ToxCast**
+- High-throughput toxicology screening data
+- In vitro assay results
+- Toxicity pathway activation
+
+**AOPWiki (Adverse Outcome Pathways)**
+- Mechanistic pathways from molecular initiating event to adverse outcome
+- Systems toxicology frameworks
+
+**PharmGKB**
+- Pharmacogenomic relationships
+- Genetic variants affecting drug response and toxicity
+
+**Published Literature**
+- Expert-curated safety concerns from publications
+- Clinical trial adverse events
+
+#### Safety Flags:
+
+- **Organ toxicity** - Liver, kidney, cardiac effects
+- **Target safety liability** - Known on-target toxic effects
+- **Off-target effects** - Unintended activity concerns
+- **Clinical observations** - Adverse events from drugs targeting gene
+
+### 3. Baseline Expression
+
+Gene/protein expression across tissues and cell types from multiple sources.
+
+#### Data Sources:
+
+**Expression Atlas**
+- RNA-Seq expression across tissues/conditions
+- Normalized expression levels (TPM, FPKM)
+- Differential expression studies
+
+**GTEx (Genotype-Tissue Expression)**
+- Comprehensive tissue expression from healthy donors
+- Median TPM across 53 tissues
+- Expression variation analysis
+
+**Human Protein Atlas**
+- Protein expression via immunohistochemistry
+- Subcellular localization
+- Tissue specificity classifications
+
+#### Expression Metrics:
+
+- **TPM (Transcripts Per Million)** - Normalized RNA abundance
+- **Tissue specificity** - Enrichment in specific tissues
+- **Protein level** - Correlation with RNA expression
+- **Subcellular location** - Where protein is found in cell
+
+### 4. Molecular Interactions
+
+Protein-protein interactions, complex memberships, and molecular partnerships.
+
+#### Interaction Types:
+
+**Physical Interactions**
+- Direct protein-protein binding
+- Complex components
+- Sources: IntAct, BioGRID, STRING
+
+**Pathway Membership**
+- Biological pathways from Reactome
+- Functional relationships
+- Upstream/downstream regulators
+
+**Target Interactors**
+- Direct interactors relevant to disease associations
+- Context-specific interactions
+
+### 5. Gene Essentiality
+
+Dependency data indicating if gene is essential for cell survival.
+
+#### Data Sources:
+
+**Project Score**
+- CRISPR-Cas9 fitness screens
+- 300+ cancer cell lines
+- Scaled essentiality scores (0-1)
+
+**DepMap Portal**
+- Large-scale cancer dependency data
+- Genetic and pharmacological perturbations
+- Common essential genes identification
+
+#### Essentiality Metrics:
+
+- **Score range**: 0 (non-essential) to 1 (essential)
+- **Context**: Cell line specific vs. pan-essential
+- **Therapeutic window**: Selectivity between disease and normal cells
+
+### 6. Chemical Probes and Tool Compounds
+
+High-quality small molecules for target validation.
+
+#### Sources:
+
+**Probes & Drugs Portal**
+- Chemical probes with characterized selectivity
+- Quality ratings and annotations
+- Target engagement data
+
+**Structural Genomics Consortium (SGC)**
+- Target Enabling Packages (TEPs)
+- Comprehensive target reagents
+- Freely available to academia
+
+**Probe Criteria:**
+- Potency (typically IC50 < 100 nM)
+- Selectivity (>30-fold vs. off-targets)
+- Cell activity demonstrated
+- Negative control available
+
+### 7. Pharmacogenetics
+
+Genetic variants affecting drug response for drugs targeting the gene.
+
+#### Data Source: ClinPGx
+
+**Information Included:**
+- Variant-drug pairs
+- Clinical annotations (dosing, efficacy, toxicity)
+- Evidence level and sources
+- PharmGKB cross-references
+
+**Clinical Utility:**
+- Dosing adjustments based on genotype
+- Contraindications for specific variants
+- Efficacy predictors
+
+### 8. Genetic Constraint
+
+Measures of negative selection against variants in the gene.
+
+#### Data Source: gnomAD
+
+**Metrics:**
+
+**pLI (probability of Loss-of-function Intolerance)**
+- Range: 0-1
+- pLI > 0.9 indicates intolerant to LoF variants
+- High pLI suggests essentiality
+
+**LOEUF (Loss-of-function Observed/Expected Upper bound Fraction)**
+- Lower values indicate greater constraint
+- More interpretable than pLI across range
+
+**Missense Constraint**
+- Z-scores for missense depletion
+- O/E ratios for missense variants
+
+**Interpretation:**
+- High constraint suggests important biological function
+- May indicate safety concerns if inhibited
+- Essential genes often show high constraint
+
+### 9. Comparative Genomics
+
+Cross-species gene conservation and ortholog information.
+
+#### Data Source: Ensembl Compara
+
+**Ortholog Data:**
+- Mouse, rat, zebrafish, other model organisms
+- Orthology confidence (1:1, 1:many, many:many)
+- Percent identity and similarity
+
+**Utility:**
+- Model organism studies transferability
+- Functional conservation assessment
+- Evolution and selective pressure
+
+### 10. Cancer Annotations
+
+Cancer-specific target features for oncology indications.
+
+#### Data Sources:
+
+**Cancer Gene Census**
+- Role in cancer (oncogene, TSG, fusion)
+- Tier classification (1 = established, 2 = emerging)
+- Tumor types and mutation types
+
+**Cancer Hallmarks**
+- Functional roles in cancer biology
+- Hallmarks: proliferation, apoptosis evasion, metastasis, etc.
+- Links to specific cancer processes
+
+**Oncology Clinical Trials**
+- Drugs in development targeting gene for cancer
+- Trial phases and indications
+
+### 11. Mouse Phenotypes
+
+Phenotypes from mouse knockout/mutation studies.
+
+#### Data Source: MGI (Mouse Genome Informatics)
+
+**Phenotype Data:**
+- Knockout phenotypes
+- Disease model associations
+- Mammalian Phenotype Ontology (MP) terms
+
+**Utility:**
+- Predict on-target effects
+- Safety liability identification
+- Mechanism of action insights
+
+### 12. Pathways
+
+Biological pathway annotations placing target in functional context.
+
+#### Data Source: Reactome
+
+**Pathway Information:**
+- Curated biological pathways
+- Hierarchical organization
+- Pathway diagrams with target position
+
+**Applications:**
+- Mechanism hypothesis generation
+- Related target identification
+- Systems biology analysis
+
+## Using Target Annotations in Queries
+
+### Query Template: Comprehensive Target Profile
+
+```python
+query = """
+  query targetProfile($ensemblId: String!) {
+    target(ensemblId: $ensemblId) {
+      id
+      approvedSymbol
+      approvedName
+      biotype
+
+      # Tractability
+      tractability {
+        label
+        modality
+        value
+      }
+
+      # Safety
+      safetyLiabilities {
+        event
+        effects {
+          dosing
+          organsAffected
+        }
+      }
+
+      # Expression
+      expressions {
+        tissue {
+          label
+        }
+        rna {
+          value
+          level
+        }
+        protein {
+          level
+        }
+      }
+
+      # Chemical probes
+      chemicalProbes {
+        id
+        probeminer
+        origin
+      }
+
+      # Known drugs
+      knownDrugs {
+        uniqueDrugs
+        rows {
+          drug {
+            name
+            maximumClinicalTrialPhase
+          }
+          phase
+          status
+        }
+      }
+
+      # Genetic constraint
+      geneticConstraint {
+        constraintType
+        score
+        exp
+        obs
+      }
+
+      # Pathways
+      pathways {
+        pathway
+        pathwayId
+      }
+    }
+  }
+"""
+
+variables = {"ensemblId": "ENSG00000157764"}
+```
+
+## Annotation Interpretation Guidelines
+
+### For Target Prioritization:
+
+1. **Druggability (Tractability):**
+   - Clinical precedence >> Discovery precedence > Predicted
+   - Consider modality relevant to therapeutic approach
+   - Check for existing tool compounds
+
+2. **Safety Assessment:**
+   - Review organ toxicity signals
+   - Check expression in critical tissues
+   - Assess genetic constraint (high = safety concern if inhibited)
+   - Evaluate clinical adverse events from drugs
+
+3. **Disease Relevance:**
+   - Combine with association scores
+   - Check expression in disease-relevant tissues
+   - Review pathway context
+
+4. **Validation Readiness:**
+   - Chemical probes available?
+   - Model organism data supportive?
+   - Known drugs provide mechanism insight?
+
+5. **Clinical Path Considerations:**
+   - Pharmacogenetic factors
+   - Expression pattern (tissue-specific is better for selectivity)
+   - Essentiality (non-essential better for safety)
+
+### Red Flags:
+
+- **High essentiality + ubiquitous expression** - Poor therapeutic window
+- **Multiple safety liabilities** - Toxicity concerns
+- **High genetic constraint (pLI > 0.9)** - Critical gene, inhibition may be harmful
+- **No tractability precedence** - Higher risk, longer development
+- **Conflicting evidence** - Requires deeper investigation
+
+### Green Flags:
+
+- **Clinical precedence + related indication** - De-risked mechanism
+- **Tissue-specific expression** - Better selectivity
+- **Chemical probes available** - Faster validation
+- **Low essentiality + disease relevance** - Good therapeutic window
+- **Multiple evidence types converge** - Higher confidence

skills/opentargets-database/scripts/query_opentargets.py

@@ -0,0 +1,403 @@
+#!/usr/bin/env python3
+"""
+Open Targets Platform GraphQL Query Helper
+
+This script provides reusable functions for querying the Open Targets Platform
+GraphQL API. Use these functions to retrieve target, disease, drug, and
+association data.
+
+Dependencies: requests (pip install requests)
+"""
+
+import requests
+import json
+from typing import Dict, List, Optional, Any
+
+
+# API endpoint
+BASE_URL = "https://api.platform.opentargets.org/api/v4/graphql"
+
+
+def execute_query(query: str, variables: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
+    """
+    Execute a GraphQL query against the Open Targets Platform API.
+
+    Args:
+        query: GraphQL query string
+        variables: Optional dictionary of variables for the query
+
+    Returns:
+        Dictionary containing the API response data
+
+    Raises:
+        Exception if the API request fails or returns errors
+    """
+    payload = {"query": query}
+    if variables:
+        payload["variables"] = variables
+
+    try:
+        response = requests.post(BASE_URL, json=payload, timeout=30)
+        response.raise_for_status()
+        data = response.json()
+
+        if "errors" in data:
+            raise Exception(f"GraphQL errors: {data['errors']}")
+
+        return data.get("data", {})
+
+    except requests.exceptions.RequestException as e:
+        raise Exception(f"API request failed: {str(e)}")
+
+
+def search_entities(query_string: str, entity_types: Optional[List[str]] = None) -> List[Dict[str, Any]]:
+    """
+    Search for targets, diseases, or drugs by name or identifier.
+
+    Args:
+        query_string: Search term (e.g., "BRCA1", "alzheimer", "aspirin")
+        entity_types: Optional list to filter by entity type ["target", "disease", "drug"]
+
+    Returns:
+        List of search results with id, name, entity type, and description
+    """
+    query = """
+      query search($queryString: String!, $entityNames: [String!]) {
+        search(queryString: $queryString, entityNames: $entityNames, page: {size: 10}) {
+          hits {
+            id
+            entity
+            name
+            description
+          }
+        }
+      }
+    """
+
+    variables = {"queryString": query_string}
+    if entity_types:
+        variables["entityNames"] = entity_types
+
+    result = execute_query(query, variables)
+    return result.get("search", {}).get("hits", [])
+
+
+def get_target_info(ensembl_id: str, include_diseases: bool = False) -> Dict[str, Any]:
+    """
+    Retrieve comprehensive information about a target gene.
+
+    Args:
+        ensembl_id: Ensembl gene ID (e.g., "ENSG00000157764")
+        include_diseases: Whether to include top associated diseases
+
+    Returns:
+        Dictionary with target information including tractability, safety, expression
+    """
+    disease_fragment = """
+      associatedDiseases(page: {size: 10}) {
+        rows {
+          disease {
+            id
+            name
+          }
+          score
+          datatypeScores {
+            componentId
+            score
+          }
+        }
+      }
+    """ if include_diseases else ""
+
+    query = f"""
+      query targetInfo($ensemblId: String!) {{
+        target(ensemblId: $ensemblId) {{
+          id
+          approvedSymbol
+          approvedName
+          biotype
+          functionDescriptions
+
+          tractability {{
+            label
+            modality
+            value
+          }}
+
+          safetyLiabilities {{
+            event
+            effects {{
+              dosing
+              organsAffected
+            }}
+            biosamples {{
+              tissue {{
+                label
+              }}
+            }}
+          }}
+
+          geneticConstraint {{
+            constraintType
+            score
+            exp
+            obs
+          }}
+
+          {disease_fragment}
+        }}
+      }}
+    """
+
+    result = execute_query(query, {"ensemblId": ensembl_id})
+    return result.get("target", {})
+
+
+def get_disease_info(efo_id: str, include_targets: bool = False) -> Dict[str, Any]:
+    """
+    Retrieve information about a disease.
+
+    Args:
+        efo_id: EFO disease identifier (e.g., "EFO_0000249")
+        include_targets: Whether to include top associated targets
+
+    Returns:
+        Dictionary with disease information
+    """
+    target_fragment = """
+      associatedTargets(page: {size: 10}) {
+        rows {
+          target {
+            id
+            approvedSymbol
+            approvedName
+          }
+          score
+          datatypeScores {
+            componentId
+            score
+          }
+        }
+      }
+    """ if include_targets else ""
+
+    query = f"""
+      query diseaseInfo($efoId: String!) {{
+        disease(efoId: $efoId) {{
+          id
+          name
+          description
+          therapeuticAreas {{
+            id
+            name
+          }}
+          synonyms {{
+            terms
+          }}
+          {target_fragment}
+        }}
+      }}
+    """
+
+    result = execute_query(query, {"efoId": efo_id})
+    return result.get("disease", {})
+
+
+def get_target_disease_evidence(ensembl_id: str, efo_id: str,
+                                  data_types: Optional[List[str]] = None) -> List[Dict[str, Any]]:
+    """
+    Retrieve evidence linking a target to a disease.
+
+    Args:
+        ensembl_id: Ensembl gene ID
+        efo_id: EFO disease identifier
+        data_types: Optional filter for evidence types (e.g., ["genetic_association", "known_drug"])
+
+    Returns:
+        List of evidence records with scores and sources
+    """
+    query = """
+      query evidences($ensemblId: String!, $efoId: String!, $dataTypes: [String!]) {
+        disease(efoId: $efoId) {
+          evidences(ensemblIds: [$ensemblId], datatypes: $dataTypes, size: 100) {
+            rows {
+              datasourceId
+              datatypeId
+              score
+              targetFromSourceId
+              studyId
+              literature
+              cohortPhenotypes
+            }
+          }
+        }
+      }
+    """
+
+    variables = {"ensemblId": ensembl_id, "efoId": efo_id}
+    if data_types:
+        variables["dataTypes"] = data_types
+
+    result = execute_query(query, variables)
+    return result.get("disease", {}).get("evidences", {}).get("rows", [])
+
+
+def get_known_drugs_for_disease(efo_id: str) -> Dict[str, Any]:
+    """
+    Get drugs known to be used for a disease.
+
+    Args:
+        efo_id: EFO disease identifier
+
+    Returns:
+        Dictionary with drug information including phase, targets, and status
+    """
+    query = """
+      query knownDrugs($efoId: String!) {
+        disease(efoId: $efoId) {
+          knownDrugs {
+            uniqueDrugs
+            uniqueTargets
+            rows {
+              drug {
+                id
+                name
+                drugType
+                maximumClinicalTrialPhase
+              }
+              targets {
+                id
+                approvedSymbol
+              }
+              phase
+              status
+              mechanismOfAction
+            }
+          }
+        }
+      }
+    """
+
+    result = execute_query(query, {"efoId": efo_id})
+    return result.get("disease", {}).get("knownDrugs", {})
+
+
+def get_drug_info(chembl_id: str) -> Dict[str, Any]:
+    """
+    Retrieve information about a drug.
+
+    Args:
+        chembl_id: ChEMBL identifier (e.g., "CHEMBL25")
+
+    Returns:
+        Dictionary with drug information
+    """
+    query = """
+      query drugInfo($chemblId: String!) {
+        drug(chemblId: $chemblId) {
+          id
+          name
+          synonyms
+          drugType
+          maximumClinicalTrialPhase
+          hasBeenWithdrawn
+          withdrawnNotice {
+            reasons
+            countries
+          }
+          mechanismsOfAction {
+            actionType
+            mechanismOfAction
+            targetName
+            targets {
+              id
+              approvedSymbol
+            }
+          }
+          indications {
+            disease
+            efoId
+            maxPhaseForIndication
+          }
+        }
+      }
+    """
+
+    result = execute_query(query, {"chemblId": chembl_id})
+    return result.get("drug", {})
+
+
+def get_target_associations(ensembl_id: str, min_score: float = 0.0) -> List[Dict[str, Any]]:
+    """
+    Get all disease associations for a target, filtered by minimum score.
+
+    Args:
+        ensembl_id: Ensembl gene ID
+        min_score: Minimum association score (0-1) to include
+
+    Returns:
+        List of disease associations with scores
+    """
+    query = """
+      query targetAssociations($ensemblId: String!) {
+        target(ensemblId: $ensemblId) {
+          associatedDiseases(page: {size: 100}) {
+            count
+            rows {
+              disease {
+                id
+                name
+              }
+              score
+              datatypeScores {
+                componentId
+                score
+              }
+            }
+          }
+        }
+      }
+    """
+
+    result = execute_query(query, {"ensemblId": ensembl_id})
+    associations = result.get("target", {}).get("associatedDiseases", {}).get("rows", [])
+
+    # Filter by minimum score
+    return [assoc for assoc in associations if assoc.get("score", 0) >= min_score]
+
+
+# Example usage
+if __name__ == "__main__":
+    # Example 1: Search for a gene
+    print("Searching for BRCA1...")
+    results = search_entities("BRCA1", entity_types=["target"])
+    for result in results[:3]:
+        print(f"  {result['name']} ({result['id']})")
+
+    # Example 2: Get target information
+    if results:
+        ensembl_id = results[0]['id']
+        print(f"\nGetting info for {ensembl_id}...")
+        target_info = get_target_info(ensembl_id, include_diseases=True)
+        print(f"  Symbol: {target_info.get('approvedSymbol')}")
+        print(f"  Name: {target_info.get('approvedName')}")
+
+        # Show top diseases
+        diseases = target_info.get('associatedDiseases', {}).get('rows', [])
+        if diseases:
+            print(f"\n  Top associated diseases:")
+            for disease in diseases[:3]:
+                print(f"    - {disease['disease']['name']} (score: {disease['score']:.2f})")
+
+    # Example 3: Search for a disease
+    print("\n\nSearching for Alzheimer's disease...")
+    disease_results = search_entities("alzheimer", entity_types=["disease"])
+    if disease_results:
+        efo_id = disease_results[0]['id']
+        print(f"  Found: {disease_results[0]['name']} ({efo_id})")
+
+        # Get known drugs
+        print(f"\n  Known drugs for {disease_results[0]['name']}:")
+        drugs = get_known_drugs_for_disease(efo_id)
+        for drug in drugs.get('rows', [])[:5]:
+            print(f"    - {drug['drug']['name']} (Phase {drug['phase']})")