We're making drug development decisions patient specific
The right drug. The right patient. The right dose. Predicted before the trial. Explained by biology.
Solve the patient, solve the trial
Drug development still treats patients as averages. Simaya was founded on a different premise: if a model represents the cause-and-effect biology - the causal mechanism - it can estimate outcomes for the individual and show its reasoning. That's what makes a prediction usable in a high-stakes R&D decision - which patients to enroll, and at what dose.
Mechanism over correlation
We model cause and effect, not surface patterns.
Per patient, not per population
Every prediction is for an individual patient profile.
Explainable by design
If we can't say why, we don't ship it.
From premise to platform
The gap
Correlational models often can't explain themselves; mechanistic, causal models are designed to. That gap is the opportunity.
The engine
We built a causal modeling engine that represents the cause-and-effect biology and trains on patient data, under controlled validation.
Today
We help R&D teams select the right patients for clinical trials and support dose decisions.
Our vision
Over time, we aim to extend patient-level prediction beyond R&D toward care settings - a long-term aspiration that will follow the appropriate clinical validation and regulatory pathway.
Partner with us.
Whether you're running trials or investing in what's next, let's talk.