# Open Targets > 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. - Skill: `k-dense-ai/open-targets` (Agent Skill, multi-file: 7 files) - Install (CLI): `npx skillmds@latest add k-dense-ai/open-targets` - Raw SKILL.md: https://api.skillmd.com/api/skills/k-dense-ai/open-targets/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Integrations & APIs - License: MIT - Author: K-Dense AI (https://skillmd.com/u/k-dense-ai) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/k-dense-ai/open-targets --- # Open Targets Platform 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](https://platform-docs.opentargets.org) · [playground](https://api.platform.opentargets.org/api/v4/graphql/browser) **Checked against:** the live API, August 2026 — `meta` reports API 26.6.3, data release 26.06. Read [references/graphql-schema.md](references/graphql-schema.md) before writing a query by hand, [references/datasources.md](references/datasources.md) before interpreting or filtering a score, and [references/query-cookbook.md](references/query-cookbook.md) for tested documents to adapt. ## Start here: three identifier rules Everything else fails downstream of getting these wrong. 1. **Targets are Ensembl gene ids** (`ENSG00000146648`) — never symbols, UniProt accessions, or transcript ids. 2. **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 `null` **silently**. A few nodes legitimately keep `EFO_`, `HP_`, or `OTAR_` ids, so you cannot rewrite the prefix — resolve the name and use what comes back. 3. **Drugs are ChEMBL molecule ids** (`CHEMBL939`). Always resolve first: ```bash python skills/open-targets/scripts/ot_query.py resolve EGFR "non-small cell lung carcinoma" gefitinib ``` ``` 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 1. Resolve every name to a canonical id and report what matched. 2. Pull the target dossier — tractability, safety, prioritisation, essentiality — before looking at associations. A target that is untractable or pan-essential ends the conversation early. 3. 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`. 4. Drop to individual evidence records for anything you intend to act on. 5. State the release (`26.06`) and whether `enableIndirect` was on with any number you report. ## Target dossier ```bash python skills/open-targets/scripts/ot_query.py target ENSG00000146648 # everything python skills/open-targets/scripts/ot_query.py target ENSG00000146648 --section tractability ``` 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 ```bash # 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`. ## Evidence records ```bash python skills/open-targets/scripts/ot_associations.py evidence ENSG00000146648 MONDO_0005233 \ --datasources clinical_precedence --limit 50 ``` ``` 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 ```bash 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: ```bash 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.