We generate therapeutic assets by leveraging our proprietary Causal AI and patient-derived single-cell interventional data.

Pharma's discovery methods are indirect, because they had to be. Mouse models, genetic correlation, and pathway inference became standard because interventional data straight from human cells didn't exist at scale until recently. None of them answer the question the clinic ultimately asks: what happens when you hit this gene, in this person. That gap is why most drug programs fail, at a cost of hundreds of millions each. We generate that data directly in patient cells, and use causal AI to build a working model of human biology from it.

Sparse evidence means everyone converges on the same handful of targets. Because so few targets ever look de-risked enough to pursue, competition for them is fierce. Our causal models trace the regulatory network upstream of those crowded targets, to the master regulators actually driving them: novel targets nobody else can see. And because we model the full causal path, not just the target itself, we can see what hitting that gene does downstream, including where it's likely to cause toxicity, before a single compound is made.

Most AI predicts. Drug discovery requires intervention. Standard models learn what tends to move together, not what happens if you change one thing. Ask them an interventional question anyway, and they answer confidently and wrong. But "what happens if you hit this gene, with this drug, in this patient" is exactly the question drug discovery needs answered. Our causal AI is built to answer it directly, not approximate it from correlation.