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Research and discovery
Finding the signal in research that would take a person years to read
Research and discovery teams still sift through genomic data, literature, and knowledge graphs largely by hand, searching for the signal that points to a new target or a mutation worth pursuing. A researcher can spend more time reading than discovering.
AI reads at a scale no team can match. Predictive models flag gene mutations and drug discovery signals from genomic and clinical data. Generative AI surfaces the relevant finding from literature and knowledge graphs instead of a manual search. Agents keep signal detection running as new data and papers arrive. Your researchers spend their time on the finding, not the search, running in your own environment.
4 use cases
Biomedical literature and knowledge graph synthesisAI biomedical literature synthesis reads publications and internal reports, maps genes and targets, and answers questions with a citation for each statement.Gene mutation risk scoringGene mutation AI scores genomic and phenotype profiles for genetic risk and ranks the genes and pathways that drive it, for research and clinical teams.Mechanism of action prediction for drug screeningMachine learning that estimates how a compound acts on biology from gene expression and cell viability data, so scientists can prioritize hits sooner.Patent landscape and competitive intelligenceA patent landscape and competitive intelligence assistant searches patents, trial registries and publications and answers questions with citations.
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