Industry: Pharma & Life Sciences

Knowledge graphs for pharma and life sciences.

Pharma knowledge graphs that surface repurposing hypotheses across ChEMBL/OpenTargets, detect adverse event signals weeks earlier than manual review, and give scientific and medical affairs assistants fact level citations to real studies. For pharma, biotech, and CROs: drug target disease modelling, RWE integration, and pharmacovigilance.

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Where pharma has been quietly ahead: and where AI is now making it obvious

A Decade of Biomedical Graph Investment

Pharma has been building biomedical knowledge graphs for a decade (OpenTargets, DrugBank, ChEBI, KEGG) because drug discovery is fundamentally about walking relationships between targets, compounds, pathways, and phenotypes.

Every Function Now Has an LLM Problem

What's new in 2026 is that every other function (clinical, safety, medical affairs, commercial) now has an LLM problem the same graph substrate can solve. The infrastructure R&D built for target discovery is exactly what clinical operations and medical affairs need for their LLM applications.

Grounded Assistants End Fabricated PMIDs

Grounding scientific and medical affairs assistants in a knowledge graph is the difference between an assistant that fabricates PMIDs and one that cites the specific page of the specific study report. Same substrate, different retrieval layer.

Six proven pharma and life sciences use cases

Drug target disease knowledge graph

Integrate internal R&D data with public sources (ChEMBL, OpenTargets, DrugBank, DisGeNET) to give discovery and development teams a queryable map of what targets what, for which indications, with what evidence.

•Faster target identification and prioritization
•Repurposing hypotheses grounded in traversal, not literature search luck
•Competitive intelligence on target indication white space

Clinical trial data harmonization

Every trial arrives with its own CRF quirks and vocabularies. Map to CDISC SDTM and internal common data model at the graph layer so cross trial analyses stop being multi quarter data engineering projects.

•Faster cross trial cohort assembly for meta analysis and safety signal work
•Reduced downstream statistical programming rework
•Reusable harmonization logic instead of per trial ETL

Real world evidence (RWE) integration

Link RWD sources (claims, EHR, registries, patient reported outcomes) to clinical, molecular, and outcomes data. The graph is where clinical, epidemiology, and HEOR teams meet.

•Faster RWE study feasibility assessment
•Unified cohort definitions that survive team boundaries
•Traceable lineage from raw RWD to submitted evidence

Pharmacovigilance and adverse event signal detection

Signals live at the intersection of patient, product, event, and time. A graph makes it trivial to walk from an incoming case to similar cases across products, sites, and populations.

•Earlier signal detection across FAERS, EudraVigilance, and internal case data
•Reduced false positives through graph context aware disproportionality
•Faster response to inspector and health authority queries

R&D literature graph

Papers, patents, and preprints connected by shared authors, targets, compounds, and citations: indexed by MeSH and internal ontologies. Grounded search that walks the network instead of just ranking hits.

•Reduced literature review time on new programs
•Emerging target and competitor activity monitoring at graph scale
•Foundation for GraphRAG assistants for scientific teams

GraphRAG for scientific and medical affairs assistants

Ground an LLM assistant in your internal study reports, publications, and the harmonized data: with citation to the specific report page or study record. No fabricated citations to made up papers.

•Field medical response drafting with cite checked evidence
•Faster protocol authoring with grounded historical precedent lookup
•Auditable answers for regulatory and compliance review

Frequently asked questions

How do you handle regulated data (GxP, 21 CFR Part 11, GDPR)?+

Every deployment is designed for GxP from day one: validated infrastructure, tamper evident audit trails, electronic signature workflows where required, on premises or validated cloud deployment, and full lineage from source system to graph node. GDPR and equivalent regimes are handled with property level ACLs and pseudonymization at ingestion.

Which ontologies and vocabularies do you use?+

Standard biomedical: MeSH, SNOMED CT, ICD 10, RxNorm, LOINC, UMLS, MedDRA, WHO Drug, ATC. Clinical trials: CDISC SDTM, ADaM, SEND. Chemistry/biology: ChEBI, Gene Ontology, HGNC, UniProt. We align internal ontologies to these standards so downstream analytics survive team changes.

Can you integrate our commercial and manufacturing data too?+

Yes. Field force, market access, tender, and supply chain data all fit the same graph model. Some clients start with R&D and expand, others start with commercial. The graph's advantage compounds as more of the enterprise joins it.

How does GraphRAG reduce risk for scientific assistants?+

Vanilla LLMs fabricate PMIDs, misattribute studies, and confuse similar sounding compounds. GraphRAG constrains the model to retrieve from your validated knowledge graph and linked source documents: every fact points to a specific paper, study record, or internal report. In eval harnesses we typically drive fabrication rates from double digits to near zero on domain questions.

What's a realistic first engagement?+

A 10 week target graph pilot combining internal R&D data with two public sources over one therapeutic area is a common start. Adverse event signal detection and RWE integration are usually phase 2.

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