The platform (Open Targets)

Portfolio Intelligence System uses the Open Targets Platform as its primary public evidence layer when biology, disease, drug, and association context are in scope. This page is technical depth for that offer—not a substitute for the three-offer value proposition (What we do).

How this fits: Open Targets answers “what does the integrated evidence say about targets, diseases, and drugs?” Orange Book (FDA) answers “what is already approved in this class?”—see Structure-informed diligence when both are fused. Visibility & Narrative and Strategic Capital Options generally do not lead with Open Targets.

Why Open Targets matters for Portfolio Intelligence

The Platform is the same evidence base that industry and academia use for target validation and drug discovery. Its value for our work comes from:

Public and trusted — Non-proprietary; targets, diseases, drugs, variants, and studies are annotated and linked from curated and experimental sources (genetics, literature, pathways, tractability, safety).
Integrated model — One framework connects targets, diseases and phenotypes, evidence, and scored target–disease associations, so you get a consistent picture instead of scattered databases.
Evidence in one place — Associations are generated and scored from many datasources; the Platform aggregates and weights this so you can interrogate it interactively or via API.
Single API, reproducible — One GraphQL API delivers the full evidence picture. Same query, same result; you can re-run or audit as data updates.

We use this so Portfolio Intelligence outputs are traceable, updatable, and aligned with the same standard the rest of the field uses—then we interpret and map opportunities for your decision question.

What the Platform provides

The Open Targets Platform exposes a GraphQL API that delivers:

Targets — Gene identity, pathways (e.g. Reactome), Gene Ontology, cancer hallmarks (COSMIC), tractability, target class, chemical probes, known drugs.
Diseases — Ontologies (EFO, MONDO), therapeutic areas.
Target–disease associations — Scored associations with evidence from genetics, literature, pathways, and other datasources (EVA, Europe PMC, Genomics England, IMPC, Cancer Gene Census, Reactome, and more).
Prioritisation — Composite scores (genetic constraint, cancer driver status, DepMap essentiality, mouse phenotypes, tissue specificity).
Safety — Target-level safety/liability data where available.
Expression — Tissue and biosample expression (e.g. Expression Atlas, Human Protein Atlas).
Literature — Publication counts and metadata (e.g. Europe PMC).

We don’t own or host it—we query it, interpret it, and turn it into strategy and diligence narratives inside Portfolio Intelligence.

Benchmarking drug, target, and indication potential

A valuable way to use the Platform is to benchmark drug, gene (target), or indication potential and evaluations on the same evidence base. Because the API delivers consistent entities (targets, diseases, drugs), scored associations, prioritisation metrics, tractability, and known drugs per indication, you can:

Compare targets — Same query shape for any gene; prioritisation scores, association scores, and tractability let you rank or compare targets within a therapeutic area or pipeline.
Compare indications — Disease-level data (associated targets, known drugs, therapeutic area) supports comparing indications for a given drug or target, or benchmarking one indication against the landscape.
Compare drugs — Drug-centric queries (mechanisms, indications, targets) give a consistent basis to evaluate repurposing potential or positioning across a shortlist.

We use this in practice as pipeline prioritisation and competitive context for Portfolio Intelligence: compare targets and indications on the same evidence base, see how a thesis sits relative to the Platform landscape, and re-run as data updates.

Entities and query space

The Platform’s data model is built around five main entities. The GraphQL API lets you query by entity and then traverse to related data—so you can start from a target, a disease, or a drug and get the full linked picture.

Entity	What it is	Query entry (example)	What you can ask for from it
Target	Candidate drug-binding molecule (e.g. gene/product)	`target(ensemblId: "ENSG00000139618")`	Pathways, GO, hallmarks, tractability, associatedDiseases, knownDrugs, evidence, prioritisation, safety, expression, interactions
Disease / Phenotype	Disease indication, phenotype, or trait (EFO/MONDO)	`disease(efoId: "EFO_0000616")`	Name, ontology; associatedTargets, knownDrugs; therapeutic area
Drug	Molecule as medicinal product (ChEMBL)	`drug(chemblId: "CHEMBL192")`	Indications, mechanisms, targets, phases, drug type
Variant	DNA variant associated with disease or trait	via API	Links to studies, traits, and targets; population frequencies, variant effect, transcript consequences
Study	Source of evidence (e.g. GWAS, molQTL) linking variants to traits	via API	Links to variants, traits, molecular phenotypes; GWAS/molQTL credible sets

Which entities the pipeline uses today

The report pipeline and Open Targets provider currently use three of the five entities:

Target — requestTarget (target-summary, oncology-target-assessment).
Disease / Phenotype — requestDisease (disease-landscape, disease-dry-eye).
Drug — requestDrug (drug-assessment).

Variant and Study are first-class in the Platform (variant pages, GWAS/molQTL studies, credible sets, Locus-to-Gene) but are not yet exposed in our provider or report pipeline—no GraphQL query loaders or requestVariant / requestStudy methods. Adding them would mean new queries and section builders (e.g. genetics evidence or study-backed associations) for reports that need variant- or study-centric views.

In practice, Biotica Bio assessments today start from a target, disease, or drug: one query then pulls associations, known drugs, evidence, and—when we need landscape context—related diseases and their associated targets and drugs. The same API supports target-centric, disease-centric, or drug-centric views so the query space matches how you think about the biology or the deal.

Why it matters for Portfolio Intelligence

Credibility — Claims in evidence-backed deliverables can be traced to the Platform and its upstream sources.
Reproducibility — Same query, same API; you can re-run or audit as data updates.
Speed — One structured query replaces ad hoc literature and database diving for a first-pass assessment.

To see how this evidence is translated into decisions, read How it’s built, then contact us for scoped Portfolio Intelligence.

Powered by Open Targets. Interpretation and product design by Biotica Bio.

Biotica Bio is not affiliated with Open Targets. We use the Platform as our primary evidence source for many Portfolio Intelligence scopes; upstream data is maintained and documented by the Open Targets project and the respective databases.