How Galen reasons about cancer.
A causal world model, built from the mechanisms underneath the data.
Three rungs of reasoning.
Association — what most AI in biology does today. Drug X is associated with mutation Y. Useful when the data is dense, but it breaks on rare mutations, novel combinations, and newly approved therapies where historical data doesn’t exist yet.
Intervention — Galen maintains a structural causal model of cancer biology. It reasons not about what correlates but about what happens if you intervene: inhibiting a target, perturbing a pathway, combining two agents. Do-calculus over biology.
Counterfactual — the level that matters most to R&D. Reasoning about cases never seen before: If this tumor carried a different resistance mutation, what strategy would still work?
A worked mechanism.
One end-to-end trace Galen can produce, shown as steps rather than prose. Each step is grounded in evidence and traceable back to its sources.
EGFR L858R
Activating mutation in non-small-cell lung cancer.
MAPK signaling
Constitutive pathway activation drives proliferation.
Osimertinib
3rd-generation EGFR inhibitor; covalent binding at C797.
C797S resistance
Acquired mutation alters the covalent binding site.
Counterfactual
A different mutation would change the strategy — for instance, MET amplification points to a MET inhibitor combined with an EGFR-TKI.
The substrate.
Galen continuously integrates experimental evidence from the world’s peer-reviewed biomedical databases — genomic studies, clinical trials, drug mechanisms, binding assays — into a structural model of cancer biology. Every claim traces to evidence, with provenance preserved end to end.
What it’s used for.
Target discovery & prioritization. Which targets are causally upstream of a phenotype, in which contexts, and how robust is the evidence? Galen reasons across mechanisms, not just expression correlations.
Target-binding & selectivity. What does the causal landscape look like around a candidate compound — on-target effects, off-target liabilities, resistance trajectories, and the mechanisms that drive each?
Synergistic combinations & resistance. Combinations that look promising on paper often share mechanism. Galen reasons about pathway redundancy, escape routes, and counterfactual response — the questions that decide whether a combination is worth pursuing.
Access.
Galen is in private access with biopharma R&D partners. Reach out to discuss a pilot or API integration.