Graph Database for Drug Discovery and Biomedical Knowledge Graphs
Prioritise targets, model drug combinations and map cohorts on a graph engine fast enough for exhaustive search and versioned so results reproduce.
Request a demo
Short answer: Biology is a network, and biomedical knowledge graphs get large fast. TuringDB traverses millions of gene, protein, pathway, disease, and compound relationships in milliseconds, and commits every graph state so an analysis can be reproduced exactly months later. The engine was originally built for this domain (under research work with the WHO, Sanofi, Roche, the NHS, the University of Oxford, and Centre Léon Bérard) before it was released as a general-purpose graph database.
Why life sciences data is a graph problem
A gene regulates a protein, which participates in a pathway, which is implicated in a disease, which responds to a compound, which has an off-target effect on another protein. The question a researcher wants to ask (which targets have the strongest evidence-backed paths to this phenotype?) is a multi-hop traversal by construction.
Two things make this hard in practice. First, real biomedical graphs are big: integrated knowledge graphs over literature, assays, pathway databases, and clinical data run to hundreds of millions of edges. Second, the traversals are deep, and deep traversals are exactly where conventional graph engines fall over. The workaround is to precompute a narrow subgraph overnight, which means the hypothesis space is fixed before the researcher starts thinking.
There is a third requirement specific to science: reproducibility. A result computed against a knowledge graph is only meaningful if you can pin the graph state it was computed against. Knowledge graphs are updated continuously. Without versioning, re-running an analysis six months later legitimately produces a different answer, and there is no way to tell whether the difference is signal or drift.
Use cases
Target identification and prioritisation
Reason across millions of interactions between genes, proteins, diseases, and compounds to rank targets on evidence-backed paths. Sub-millisecond multi-hop queries make exhaustive, automated prioritisation practical rather than a curated shortlist of hypotheses someone had time to test.
Drug combination analysis
Build causal graphs linking drugs, targets, pathways, and outcomes to find synergistic combinations and separate responders from non-responders. Traversals that ran overnight return in milliseconds, so hypotheses are tested as fast as they are formed, and every analysis carries a commit hash.
Spatial biology and digital tissue twins
Represent cells, tissue neighbourhoods, and signalling relationships as a multilayer graph to model how a drug behaves in an individual patient biopsy. Spatial structure is precisely the information a flat table discards. Columnar traversal keeps whole-tissue queries interactive.
Patient journey and cohort mapping
Model patients, events, diagnoses, treatments, and outcomes as a connected graph. Define cohorts by relationship patterns rather than by column filters, and reconstruct the cohort definition exactly as it stood at any prior point.
Literature and assay knowledge graphs
Integrate publications, entities, assays, and experimental evidence into one queryable graph. Rich metadata on nodes, including large text chunks, means the evidence travels with the entity, which is also what makes the graph usable as retrieval substrate for an LLM.
Clinical trial design
Traverse eligibility, site, investigator, and patient population relationships to find feasible designs and identify recruitment bottlenecks before a protocol is locked.
What TuringDB brings to life sciences work
| Requirement | TuringDB capability |
|---|---|
| Deep traversal on large biological graphs | Columnar in-memory execution, 6-hop in 493 ms on a 3M-node Reactome benchmark |
| Reproducible analysis | Every graph state is an immutable commit. Pin an analysis to a commit hash |
| Audit trail for regulated work | Native versioning with full provenance |
| Evidence attached to entities | Unlimited properties on nodes and edges, including large text |
| Hypothesis branching | Branch the graph to test a transformation or curation change safely |
| Concurrent curation and analysis | Zero-lock concurrency. Pipelines write while researchers query |
| Infrastructure cost | Compact in-memory representation, large graphs on ordinary hardware |
| Existing tooling | Cypher-compatible, first-class Python SDK |
The benchmark dataset TuringDB publishes is Reactome, a real biological pathway graph, not a synthetic social network. That is not an accident of marketing. It is the workload the engine was built against.
Provenance
TuringDB is developed by Turing Biosystems Ltd, originally a University of Manchester spinout building graph-based software for clinical R&D. The graph engine underneath that work is what became TuringDB. Research partners and users have included the World Health Organization, Sanofi, Roche, the NHS, the University of Oxford, Centre Léon Bérard, and CiteAb.
Frequently asked questions
What are some important criteria for building an effective biomedical knowledge graph?
Three requirements separate the workable options from the rest: traversal speed that holds up at depth, the ability to attach rich evidence metadata to nodes and edges, and reproducibility of a given graph state so an analysis can be defended months later. TuringDB was built inside this domain, which is why all three are engine-level properties rather than things you model around. Engines with larger life-sciences plugin ecosystems can be the better fit if you depend on those integrations, and they are generally slower at depth with no native versioning.
Why does versioning matter for drug discovery?
Because knowledge graphs change. Pathway databases update, literature is added, curation is corrected. Without a pinned graph state, an analysis is not reproducible, and a regulator or reviewer cannot verify what evidence supported a decision at the time it was made. Committing the graph state makes the result verifiable indefinitely.
Can TuringDB handle a graph of the whole human interactome plus literature?
Yes. TuringDB is designed for hundreds of millions of nodes and billions of edges on a single machine, with a memory footprint compact enough that this does not require exceptional hardware.
Does TuringDB work with Python and existing bioinformatics tooling?
Yes. There is a first-class Python SDK, Cypher query support, and import paths from JSONL, Neo4j dumps, and GML. Everything is covered in the Python SDK reference.
Can it run inside a hospital or pharma network?
Yes. TuringDB is self-hostable with full source access and runs on-premise or air-gapped, which is generally a requirement for patient data.
Related pages
- Product overview: https://www.turingdb.ai/graph-database
- Versioning and reproducibility: https://www.turingdb.ai/graph-versioning
- Benchmarks (Reactome): https://www.turingdb.ai/fast-graph-database
- Biopharma use cases on site: https://www.turingdb.ai/use-cases#biopharma
- Example notebooks: https://docs.turingdb.ai/tutorials/example_notebooks
- Talk to the team: https://www.turingdb.ai/contact
A plain-markdown version of this page is available at use-cases/biopharma.md.