Data-driven drug discovery needs structured causal evidence to support mechanistic target and biomarker discovery, but this information is locked in the literature.
Galactic AI™ uncovers cause-and-effect relationships between drugs, genes, and diseases, building the mechanistic rationale that makes targets twice as likely to succeed[1].
For biology teams
For data science teams
Infer drug-target-disease cause-and-effect relationships to ID novel targets and validate existing hypotheses.
Large-scale target-prioritisation modelling using the full cause-and-effect corpus, to drive success and reduce attrition.
Rank hits by their cause-and-effect evidence to identify those for validation.
Mechanistic rationale for gene → disease links.
Use data annotations to identify diseases directly connected to a target based on well-established literature evidence.
Biomarker investigation: reported biomarkers plus causal-pathway inference of candidate biomarkers.
Early safety signals from chemical/drug → adverse effect cause-and-effect relationships across the literature.
Comprehensive literature review, including in less-studied areas.
1Unstructured documents
Galactic AI has processed >61M publications, preprints, patents, clinical trials, drug labels and grants.
2Galactic AI technology
NER and cross-sentence RE, directional and signed
8 relationship classes and 23 entity classes linked to 22 ontologies
Instant ontology mapping
3Delivery & access
Galactic AI provides the ontology-aligned layer of citable cause-and-effect evidence that gives any LLM the domain accuracy and evidence coverage foundation models alone lack.
Across well-characterised targets, Galactic AI retrieved up to 185× more biological relationships than GPT-5, which omitted core facts documented tens of thousands of times.
Galactic AI returns the same facts every time, compared to only 28% consistency between repeated queries to GPT-5.
Every relationship & annotation links back to its source publication, so biologists can trace and verify each claim instead of trusting an opaque model.
Access to complete raw data sets this solution apart from other existing tools.
Almirall
Galactic AI uncovers cause-and-effect relationships between drugs, genes, and diseases, building the mechanistic rationale that makes targets twice as likely to succeed[1].
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