Query the Open Targets Platform GraphQL API for target-disease associations, genetic and clinical evidence, tractability and safety liabilities, target prioritisation metrics, known drugs and mechanisms of action, and disease ontology. Use this skill for target identification and validation, target-disease evidence review, druggability assessment, drug repurposing, and resolving gene, disease, and drug names to Ensembl, MONDO, and ChEMBL identifiers. Also trigger when a query mentions Open Targets, platform.opentargets.org, association scores, tractability buckets, or api.platform.opentargets.org.
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name
open-targets
description
Query the Open Targets Platform GraphQL API for target-disease associations, genetic and clinical evidence, tractability and safety liabilities, target prioritisation metrics, known drugs and mechanisms of action, and disease ontology. Use this skill for target identification and validation, target-disease evidence review, druggability assessment, drug repurposing, and resolving gene, disease, and drug names to Ensembl, MONDO, and ChEMBL identifiers. Also trigger when a query mentions Open Targets, platform.opentargets.org, association scores, tractability buckets, or api.platform.opentargets.org.
license
MIT
allowed-tools
Read Write Edit Bash
compatibility
Requires Python 3.10+ and outbound HTTPS access to api.platform.opentargets.org. The bundled client uses only the Python standard library and needs no API key or account. Data is CC0; the API is a shared public resource, so batch requests rather than looping.
Open Targets aggregates genetic, somatic, clinical, pathway, expression, animal-model, and
literature evidence into scored target–disease associations, and attaches druggability and safety
annotation to every target. It answers the question that comes before any modelling work: is
this target worth working on for this disease, and what is already known about it?
Endpoint:https://api.platform.opentargets.org/api/v4/graphql — POST, JSON, no key.
Docs:platform-docs.opentargets.org ·
playgroundChecked against: the live API, August 2026 — meta reports API 26.6.3, data release 26.06.
Everything else fails downstream of getting these wrong.
Targets are Ensembl gene ids (ENSG00000146648) — never symbols, UniProt accessions, or
transcript ids.
Diseases are MONDO ids (MONDO_0005233) in almost all cases, even though the argument is
still named efoId. Most EFO_* ids from older tutorials now return nullsilently.
A few nodes legitimately keep EFO_, HP_, or OTAR_ ids, so you cannot rewrite the prefix —
resolve the name and use what comes back.
term id name entity score
EGFR ENSG00000146648 EGFR target 1
non-small cell lung carcinoma MONDO_0005233 non-small cell lung carcinoma disease 1
gefitinib CHEMBL2087361 ICOTINIB drug 1
gefitinib CHEMBL553 ERLOTINIB drug 1
gefitinib CHEMBL939 GEFITINIB drug 1
resolve uses mapIds (exact-ish); use search when the input is partial or misspelled. Hits
come back unsorted by score, so read the names rather than taking the first row. Note the drug
rows above: one term returned three molecules, all scored 1, with the one actually asked for last.
Taking [0] here silently hands the rest of the analysis a different drug.
Workflow
Resolve every name to a canonical id and report what matched.
Pull the target dossier — tractability, safety, prioritisation, essentiality — before looking
at associations. A target that is untractable or pan-essential ends the conversation early.
Pull associations, and immediately ask which datatype carries the score. An association
driven only by literature is a very different claim from one driven by genetic_association.
Drop to individual evidence records for anything you intend to act on.
State the release (26.06) and whether enableIndirect was on with any number you report.
Sections: core, tractability, safety, prioritisation, essentiality, probes,
pathways, all. Add --format json for the raw records.
## TRACTABILITY
modality label value
SM Approved Drug true
SM Structure with Ligand true
SM High-Quality Pocket true
AB UniProt loc high conf true
Reading these:
Tractability is a ladder, not a score. High-Quality Pocket without Structure with Ligand
means a pocket was predicted. AB buckets mostly assert the target is cell-surface or
secreted — reachable, not that a useful antibody exists.
Prioritisation values run −1 to +1, where +1 favours the target. hasSafetyEvent and
geneEssentiality are already signed so that bad news is negative; do not re-negate them.
DepMap gene effect is Chronos: ≤ −1 is a strong dependency, ≈ 0 is nothing. Essential
everywhere is a toxicity flag. The depmap skill has the full cell-line matrix.
Safety liabilities carry a direction. An event reported for inhibition does not apply to
an agonist programme.
Associations
# diseases for a target, with the per-datatype breakdown
python skills/open-targets/scripts/ot_associations.py target-diseases ENSG00000146648 --limit 25
# targets for a disease, tractability-annotated
python skills/open-targets/scripts/ot_associations.py disease-targets MONDO_0004979 \
--limit 100 --min-score 0.4
# what does the human genetics alone say?
python skills/open-targets/scripts/ot_associations.py target-diseases ENSG00000146648 \
--only-datasources gwas_credible_sets gene_burden eva --limit 25
Paging, the datatype flattening, and the datasource-weighting arithmetic are handled for you.
--indirect propagates evidence from ontology descendants and inflates counts substantially.
What the score is: a harmonic-sum aggregate in [0, 1]. Not a probability, not calibrated
across releases, only comparable within one result set. It is evidence-weighted rather than
literature-normalised, so well-studied targets score high partly because they are well studied.
A zero means "no evidence indexed here", never "evidence of no association".
Restricting to some datasources is a weighting operation, not a filter. Passing a settings
array resets the Platform's default weights, so keeping three sources means explicitly zeroing
all the others — get that wrong by hand and scores go up while looking restricted. Two ids were
renamed and silently match nothing under their old names: chembl → clinical_precedence,
ot_genetics_portal → gwas_credible_sets. Likewise the datatype known_drug → clinical.
datasourceId datatypeId score drug clinicalStage literature
clinical_precedence clinical 1 OSIMERTINIB PHASE_4 35343187
eva genetic_association 0.92 rs121913465
evidences is cursor-paginated, unlike everything else in the schema — the script follows the
cursor for you. Each row keeps its source's own fields, so a ClinVar row has variantRsId and
clinicalSignificances while a drug row has drug and clinicalStage.
Disease and drug records
python skills/open-targets/scripts/ot_query.py disease MONDO_0005233
python skills/open-targets/scripts/ot_query.py drug CHEMBL939 --section mechanisms
Disease output includes parents, children, and phenotypes — useful for deciding whether to query
a specific subtype or its parent. Drug output covers mechanism of action with resolved target
ids, indications by phase, and black-box warnings.
There is no isApproved field: approval is maximumClinicalStage == "APPROVAL". The stage
vocabulary is words (APPROVAL, PHASE_3, PHASE_1_2, PRECLINICAL), so map to ChEMBL's
numeric max_phase deliberately rather than string-matching.
Arbitrary queries
For anything the subcommands do not cover, write the GraphQL document and run it:
python skills/open-targets/scripts/ot_query.py raw dossier.graphql --var id=ENSG00000146648
Two failure modes to expect. GraphQL answers HTTP 200 with an errors array for a bad field
or a missing sub-selection, so a client that only checks the status code reports success on a
typo — the bundled client raises instead. And the plural root fields (targets, diseases,
drugs) exist so you can batch: one request for 200 ids rather than 200 requests.
Composing with the rest of the bundle
depmap — full DepMap cell-line dependency matrix behind the depMapEssentiality roll-up.
chembl — measured bioactivity for the compounds Open Targets names as known drugs.
uniprot-rcsb — turn proteinIds into sequences and structures for modelling.
primekg / ncats-arax — mechanistic paths and provenance for an association worth chasing.
target-safety — gnomAD constraint, which this API does not carry: whether healthy humans
who have lost the protein actually exist.
clinicaltrials — whether anyone has taken the genetic hypothesis into a trial.
Scope and honesty
Open Targets is an evidence aggregator, not an oracle. It reflects what has been published and
indexed, so it under-represents novel biology and over-represents fashionable targets. Report
scores with their release and their datatype breakdown, never as a probability of success, and
verify anything decision-relevant against the primary source the evidence record names.
