From Correlation to Causation in Therapeutic development
Modern drug discovery has generated unprecedented volumes of data, yet most analytical systems remain correlation-driven. Associations between molecules, targets, biomarkers, and outcomes are identified statistically, but the underlying causal structure of disease biology often remains implicit.
is built on causal graphs, structured representations of mechanistic relationships across chemistry, biology, translational science, and clinical endpoints. These graphs encode directional dependencies: exposure drives target engagement; target modulation shifts biomarkers; biomarker dynamics influence clinical outcomes; regulatory constraints shape acceptable risk.