Drug prediction
that knows why
Simaya models the biology behind every prediction to estimate who a drug is most likely to help before the trial begins
For every patient three outcome predictions
Not a population average. It estimates the likely outcome for each individual patient profile.
Estimate the probability that a given patient responds, before enrollment decisions are made.
Flag elevated toxicity risk at the patient level to inform safer trial selection.
Support dose selection by estimating the range that balances predicted efficacy and toxicity for each patient's biology.
Most models predict from correlation.
Simaya reasons from biology.
Statistical models find patterns in data. They can tell you what tends to happen - but not why, and rarely for a patient who doesn't resemble the training set. Simaya encodes the underlying biology, so every prediction carries a mechanistic reason a reviewer can inspect.
A fitted surface through the data. Predictive until a patient sits outside its support.
A model of how the biology drives the outcome - and why. Because it encodes mechanism, it is designed to extrapolate to patients the data alone never saw.
Select the right patients for your trial
Poor patient selection is a leading, addressable driver of trial failure. Simaya estimates who is most likely to respond and who carries elevated toxicity risk, so teams can sharpen enrollment criteria before the first dose.
- ✓Enrich the cohort with predicted responders
- ✓Identify patients at elevated toxicity risk during cohort design
- ✓Support dose selection with mechanistic, inspectable evidence
- ✓Designed to de-risk the program ahead of costly Phase II/III spend
See what Simaya predicts for your program
Tell us about your trial and we'll walk you through how patient-level, mechanism-aware prediction can reshape your enrollment strategy.