Open Targets
Identifier
Section titled “Identifier”License
Section titled “License”CC0 1.0
Citation
Section titled “Citation”Overview
Section titled “Overview”The Open Targets Platform integrates publicly available datasets to support systematic identification and prioritisation of potential therapeutic drug targets, covering target-disease associations, evidence from genetics, drugs, expression, literature and more, plus core annotations for targets, diseases, drugs and variants.
Domain: genetics.
Source: https://platform.opentargets.org.
Tables mirror Open Targets’ published datasets 1:1 (one table per dataset under their data downloads), with column names kept verbatim in Open Targets’ original camelCase (e.g. targetId, diseaseId, studyLocusId) and nested List/Struct columns preserved as-is.
The only column added during ingest is release (the Open Targets release version, e.g. 26.06); it is the sole partition column and the only required query filter.
Association data lives in the six association_* tables (association_overall_{direct,indirect}, association_by_datatype_{direct,indirect}, association_by_datasource_{direct,indirect}); evidence is split one table per data source (evidence_<source>).
Identifier conventions follow Open Targets: target identifiers are Ensembl gene IDs (ENSG*, the id column of the target table); disease identifiers are EFO/MONDO/HPO/Orphanet IDs (e.g. EFO_0000400); drug identifiers are ChEMBL IDs (CHEMBL*).
To go from a gene symbol to associations, resolve the Ensembl ID via target.approvedSymbol, then filter an association_* table by targetId.
To resolve a credible set to a likely causal gene, join credible_set with l2g_prediction on studyLocusId.
Nested List/Struct columns can be read with R2 SQL struct/array functions but cannot be exploded into one row per element (no UNNEST); use the DuckDB-based R/Python clients’ unnest for row-per-element access.
Tables
Section titled “Tables”open_targets.association_by_datasource_direct— Data source-specific metrics for direct target-disease associations and its development over time implying association novelty.open_targets.association_by_datasource_indirect— Data source-specific metrics for indirect target-disease associations and its development over time implying association novelty.open_targets.association_by_datatype_direct— Data type-specific metrics for direct target-disease associations and its development over time implying association novelty.open_targets.association_by_datatype_indirect— Data type-specific metrics for indirect target-disease associations and its development over time implying association novelty.open_targets.association_overall_direct— Overall metrics for direct target-disease associations and its development over time implying association novelty.open_targets.association_overall_indirect— Overall metrics for indirect target-disease associations and its development over time implying association novelty.open_targets.baseline_expression— Aggregated expression data generated from RNA-seq and mass spectrometry proteomic data [Target]open_targets.biosample— Integration of metadata related to tissues, cell types or cell lines (biosamples) derived from the UBERON, CL and EFO ontologies [Ontology]open_targets.clinical_indication— Approved and investigational indications for drug molecules, capturing the maximum clinical stage reached for each drug-disease pair [Drug, Disease]open_targets.clinical_report— Clinical trial and regulatory reports from multiple sources, capturing trial metadata, associated drugs, diseases, side effects, and trial outcomes [Drug, Disease]open_targets.clinical_target— Target-drug associations derived from clinical reports, capturing the maximum clinical stage and associated diseases for each target-drug pair [Drug, Target]open_targets.colocalisation— GWAS-GWAS and GWAS-molQTL credible set colocalisation results using colocPIP and eCAVIAR methods.open_targets.credible_set— 95% credible sets for GWAS and molQTL studies.open_targets.disease— Core annotation for diseases or phenotypes.open_targets.disease_hpo— Human Phenotype Ontology subset of information included in the Platform [Ontology]open_targets.disease_phenotype— Clinical signs and symptoms observed in diseases or phenotypes.open_targets.drug_mechanism_of_action— Mechanisms of action for drug or clinical candidate molecules included in ChEMBL database.open_targets.drug_molecule— Core annotation for drug or clinical candidate molecules.open_targets.drug_warning— Blackbox and withdrawn information for drug molecules included in ChEMBL database [Drug]open_targets.enhancer_to_gene— Genomic regions associated with genes based on epigenetics evidence [Target, Genetics]open_targets.evidence_cancer_biomarkers— Disease-target evidence based on cancer biomarkers from the Cancer Genome Interpreter [Target-Disease]open_targets.evidence_cancer_gene_census— Disease-target evidence based on Cancer Gene Census provided by COSMIC [Target-Disease]open_targets.evidence_clingen— Disease-target evidence from Gene Disease Validity Curation of ClinGen [Target-Disease]open_targets.evidence_clinical_precedence— Disease-target evidence based on clinical trials, drug indications, and mechanism of action [Target-Disease]open_targets.evidence_crispr— Disease-target evidence from unified analysis of whole-genome CRISPR/Cas9 screens from ProjectScore project [Target-Disease]open_targets.evidence_crispr_screen— Disease-target evidence derived from systematic gene perturation assays of CRISPR knock-out screens [Target-Disease]open_targets.evidence_europepmc— Disease-target evidence derived from literature mining provided by Europe PMC [Target-Disease]open_targets.evidence_eva— Disease-target evidence based on germline variation from ClinVar, provided by the European Variation Archive [Target-Disease]open_targets.evidence_eva_somatic— Disease-target evidence based on somatic variation from ClinVar, provided by the European Variation Archive [Target-Disease]open_targets.evidence_expression_atlas— Disease-target evidence based on differential expression data provided by Expression Atlas [Target-Disease]open_targets.evidence_gene2phenotype— Disease-target evidence curated by the Gene2Phenotype consortium [Target-Disease]open_targets.evidence_gene_burden— Disease-target evidence based on testing aggregated effect of variants, curated from the literature by the Open Targets team [Target-Disease]open_targets.evidence_genomics_england— Curated disease-target evidence provided by Genomics England PanelApp [Target-Disease]open_targets.evidence_gwas_credible_sets— Disease-target evidence based on Locus-to-Gene prediction on GWAS credible sets [Target-Disease]open_targets.evidence_impc— Disease-target evidence based on mouse knock-out models, where mouse phenotypes are mapped to human diseases by PhenoDigm algorithm.open_targets.evidence_intogen— Cancer-driver gene evidence based on the analysis of large-scale mutational data from IntOGen [Target-Disease]open_targets.evidence_orphanet— Curated collection of rare disase-target evidence from Orphanet [Target-Disease]open_targets.evidence_reactome— Manually curated disease-target evidence based on reaction pathways sourced from Reactome [Target-Disease]open_targets.evidence_uniprot_literature— Protein-disease evidence based on literature curated by the Uniprot team [Target-Disease]open_targets.evidence_uniprot_variants— Disease-target evidence based on disease causing protein sequence alteration curated by the Uniprot team [Target-Disease]open_targets.go— This dataset contains the mapping between Gene Ontology (GO) identifiers and their terms, including hierarchy, relationships, and metadata [Ontology]open_targets.interaction— Integration of molecular interactions reporting experimental or functional interactions between molecules represented as Platform targets.open_targets.interaction_evidence— Experimental evidence supporting molecular interactions (‘interaction’) dataset [Target]open_targets.l2g_prediction— Predictions from our Locus-to-Gene (L2G) gene assignment model.open_targets.literature_entity_lut— Unique list of entities identified for each literature entry [Literature]open_targets.literature_vector— Word to Vector representation of entity matches in literature [Literature]open_targets.mouse_phenotype— Phenotypes of mouse models mapped to human targets [Target]open_targets.openfda_significant_adverse_drug_reactions— Significant adverse drug reactions (ADRs) associated with drugs.open_targets.pharmacogenomics— Genetic variants influencing individual drug responses.open_targets.reactome— Pathway metadata from Reactome pathway database [Ontology]open_targets.so— Sequence ontology lookup table [Ontology]open_targets.study— Metadata for all complex trait GWAS and molecular QTL studies in the Platform.open_targets.target— Core annotation for drug targets (gene/proteins).open_targets.target_essentiality— Target essentiality assessments based on dependencies exhibited when knocking-out genes in cancer cellular models using CRISPR screenings as reported in by the Cancer DepMap Project.open_targets.target_prioritisation— List of target-specific attributes from the Target Engine project [Target]open_targets.variant— Core variant information for all variants in the Platform.