// Step 01 — Ask | Expand | Organize
Transforms focused seed questions into structured, mechanistically organized hypotheses through scalable inference and clustering. Unbiased. Scalable. Structured.
Agentic AI for biological discovery — connecting scientific grounding with scalable inference.
Systematically exploring and exhausting the hypothesis space around a biological target — revealing what’s validated, what’s emerging, and what remains unexplored.
Transforms focused seed questions into structured, mechanistically organized hypotheses through scalable inference and clustering. Unbiased. Scalable. Structured.
Extracts key biological entities from each hypothesis. Builds a mathematical co-occurrence map and compares it with an LLM-inferred network, surfacing asymmetric, cross-domain insights. Precise. Comparative. Insight-Driven.
Links inferred networks to omics, literature, and clinical data — distinguishing what’s validated, what’s emerging, and what’s unexplored.
Transforms scattered hypotheses into ranked mechanistic landscapes.
From ~30 structured hypotheses, Hypotheon extracted nearly 700 biological entities, mapping relationships that reveal new pathways, biomarkers, and therapeutic opportunities.
Translates discovery into action:
• Proposes grounded experiments from inferred mechanisms
• Prioritizes models and readouts by feasibility and cost
• Builds CRO-ready blueprints for rapid validation
We’re building out this section — including a GitHub repo, entity network demos, and additional visual workflows.
Check back soon for updates.