opentargets-database

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):

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:

# Search by disease name
results = search_entities("alzheimer", entity_types=["disease"])
# Returns: [{"id": "EFO_0000249", "name": "Alzheimer disease", ...}]

For drugs:

# 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.

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.

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.

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.

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.

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.

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 e

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