Pharma AI glossary
Knowledge graph
A data structure representing entities (drugs, targets, adverse events, batches, suppliers) and the relationships between them, so machines can traverse connections the way an expert's intuition does.
Definition
A data structure representing entities (drugs, targets, adverse events, batches, suppliers) and the relationships between them, so machines can traverse connections the way an expert's intuition does. In pharma, knowledge graphs power target discovery (connecting genes, pathways, and disease evidence), pharmacovigilance signal work, and increasingly quality intelligence: linking deviations, materials, equipment, and CAPAs across sites reveals recurrence patterns invisible in siloed records. Often paired with RAG so a language model can cite the graph instead of hallucinating relationships.
Entities and relationships
A knowledge graph models things and how they connect: products, batches, materials, equipment, sites, suppliers, investigations, and CAPA actions. Unlike a folder of PDFs, the graph answers questions about lineage, recurrence, and impact by traversing relationships. In discovery, graphs link targets and pathways; in GMP quality intelligence, they link quality events across systems that siloed reports hide.
In GMP quality intelligence
Common uses include spotting repeat deviations tied to the same material lot, tracing equipment involved across investigations, connecting supplier changes to downstream batch events, and preparing impact assessments for change control. These are read-heavy, analysis workflows inside a governed agent or application: scoped queries, no unapproved writes to QMS, and human interpretation before any official record changes.
Graph plus agent harness
Graphs are often paired with RAG or agents so staff can ask natural-language questions and get answers grounded in structured paths, not only text chunks. Governance covers which source systems feed the graph, how often it syncs, who may query which subgraph, and what gets logged (query, entities returned, user, timestamp). Validation focuses on entity resolution, relationship accuracy, and change control when the underlying data model shifts.
Frequently asked questions
How is a knowledge graph different from RAG?
RAG retrieves document passages; a knowledge graph stores typed entities and relationships you can traverse programmatically. Many production systems combine both: the graph supplies structure and lineage, RAG supplies narrative context from SOPs and records. The harness must log which path was used.
Where do knowledge graphs fit in GMP operations?
Quality analytics and decision support: cross-site trending, investigation and CAPA research, change-control impact analysis, supplier quality linkage, and training on how systems connect. They support qualified human judgment; they do not replace batch release, inspection accept/reject, or other critical decisions unless your validation package and rulebooks explicitly allow it.
What must be auditable in a GMP knowledge graph?
Source-system provenance, sync and version history, user access, queries run, entities and relationships returned, and any export or draft sent to a human reviewer. Graph updates that affect regulated analysis need change control like any other qualified system component.
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Back to glossary hub →Educational content only, not legal or regulatory advice. Regulatory guidance cited here includes drafts (FDA AI credibility guidance; EU GMP Annex 22) as of August 2026; verify against final texts before relying on them in submissions. Company-reported figures (Merck CSR timings, Sanofi results) are labeled where used. MIT's ~95% pilot figure carries its own caveat (~150 interviews, contested definitions, not peer-reviewed). Re-check sources on module finalization.