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npx skills add davila7/claude-code-templates --skill opentargets-database
Query Open Targets Platform for target-disease associations, drug target discovery, tractability/safety data, genetics/omics evidence, known drugs, for therapeutic target identification.
npx skills add davila7/claude-code-templates --skill opentargets-database
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:
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.
This skill should be used when:
Start by finding the identifiers for targets, diseases, or drugs of interest.
For targets (genes):
For diseases:
For drugs:
Identifiers used:
ENSG00000157764)EFO_0000249)CHEMBL25)Retrieve comprehensive target annotations to assess druggability and biology.
Key annotations to review:
Refer to references/target_annotations.md for detailed information about all target features.
Get disease details and associated targets/drugs.
Get detailed evidence supporting a target-disease association.
Major evidence types:
Refer to references/evidence_types.md for detailed descriptions of all evidence types and interpretation guidelines.
Identify drugs used for a disease and their targets.
Clinical phases:
Retrieve detailed drug information including mechanisms and indications.
Find all diseases associated with a target, optionally filtering by score.
Association scores:
For custom queries beyond the provided helper functions, use the GraphQL API directly or modify scripts/query_opentargets.py.
Key information:
https://api.platform.opentargets.org/api/v4/graphqlhttps://api.platform.opentargets.org/api/v4/graphql/browserpage: {size: N, index: M}Refer to references/api_reference.md for:
When prioritizing drug targets:
Strong evidence indicators:
Caution flags:
Score interpretation:
Workflow 1: Target Discovery for a Disease
include_targets=TrueWorkflow 2: Target Validation
Workflow 3: Drug Repurposing
Workflow 4: Competitive Intelligence
scripts/query_opentargets.py Helper functions for common API operations:
search_entities() - Search for targets, diseases, or drugsget_target_info() - Retrieve target annotationsget_disease_info() - Retrieve disease informationget_target_disease_evidence() - Get supporting evidenceget_known_drugs_for_disease() - Find drugs for a diseaseget_drug_info() - Retrieve drug detailsget_target_associations() - Get all associations for a targetexecute_query() - Execute custom GraphQL queriesreferences/api_reference.md Complete GraphQL API documentation including:
references/evidence_types.md Comprehensive guide to evidence types and data sources:
references/target_annotations.md Complete target annotation reference:
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.
# Search by disease name
results = search_entities("alzheimer", entity_types=["disease"])
# Returns: [{"id": "EFO_0000249", "name": "Alzheimer disease", ...}]# Search by drug name
results = search_entities("aspirin", entity_types=["drug"])
# Returns: [{"id": "CHEMBL25", "name": "ASPIRIN", ...}]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 scoresfrom 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 scoresfrom 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 publicationsfrom 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 worksfrom 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 countriesfrom 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