AI APPROACH

Rank hypotheses by mechanism and selectivity-the laboratory confirms, rejects, or redirects each one.

Agemica uses artificial intelligence to order biological hypotheses by mechanism, selectivity, and feasibility, then treats the laboratory as the authority that confirms, rejects, or redirects each prediction.

Why combinations

Single agents fail. Combinations win.

Most cancers are not single-pathway diseases. The AI is designed for the combinatorial search space - not for one-target-at-a-time medicinal chemistry.

The model decomposes a tumor's omics signature into pathway dependencies, then searches over combinations of FDA-approved interventions that hit them simultaneously.
Multi-pathway combinations designed to target central cancer-driving mechanisms across tumor types.

Data foundation

Trained on a substrate the literature already trusts.

1,000+
Cancer cell lines analyzed for response signatures
17
Rediscovered standards of care during in-silico validation across 17 cancers.
6
combinations showed broad efficacy across 10+ cancers ex-vivo
The AI is only as honest as the assay it is held to.
- Validation principle
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