Forecast the outcome. Trace every number.
OVIVO models the full causal arc of a therapy — from a patient's genome to a clinical outcome — as one connected chain of mechanistic reasoning. Every value carries its source. Every forecast can be checked against reality.
We don't ask you to trust a number. We show you where it came from — and, where the science allows, we forecast outcomes before they're known.
Most drugs fail not because the chemistry was wrong, but because the biology was never faithfully modeled before the money was spent.
A molecule is dosed, absorbed, metabolized; it engages a target; that engagement ripples through a pathway, shifts a disease, moves a biomarker — and sometimes helps a patient, or harms them. Each of those steps is knowable. Each has been measured, somewhere, by someone. What's been missing is a rigorous, traceable way to assemble that knowledge into a forecast you can check before a trial begins.
That is what we built.
One connected chain, from genome to outcome
The platform reasons across the whole arc the way the body does — as one continuous causal system, not a stack of disconnected calculators. Because the layers are coupled, a change anywhere propagates everywhere: a genetic variant that alters metabolism changes the exposure, which changes the engagement, which changes the modeled effect.
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Patient
Who they are
Genetics, physiology, current medications.
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Exposure
PK
What the body does to the molecule.
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Engagement
PD
What the molecule does to the target.
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Pathway
Propagation
The downstream molecular cascade.
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Disease
Does it bend
Whether the pathology actually moves.
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Biomarker
What's measured
The readouts a clinician would take.
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Safety
At what risk
Liabilities, weighed in the same model.
Not "does this molecule bind?" but "in this patient, at this dose, will this disease move — by how much, at what risk, and how sure can we be?"
Every number carries its source
We don't produce a number and ask you to trust it. We produce a number, show you where each input came from, and tell you how confident we are — and why.
Nothing is invented to fill a gap. If a parameter isn't real, it isn't included. It sounds obvious; in practice it's the discipline that separates a scientific instrument from a plausible-sounding guess.
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Source
A peer-reviewed binding assay, cited and linked.
Multi-study -
Transform
A mechanistic adjustment for this patient's metabolizer genotype.
Documented basis -
Confidence
Graded by the strength of the evidence beneath it — never assumed.
Surfaced, not hidden
Illustrative. On the public site we keep to capability, not program data.
The constraints that make the output trustworthy
These aren't marketing. They're the discipline that keeps the whole edifice sound over thousands of decisions — and the reason the platform compounds in value instead of decaying into noise.
Provenance is paramount
Every value traces to a specific, identifiable source. Nothing is invented to fill a gap.
Failure over fiction
When the data to answer a question doesn't exist, the system says so and stops. A clear refusal beats a fabricated answer.
Calibration is earned
Every forecast is graded by the evidence beneath it. A weak anchor never masquerades as a strong one.
Verifiable, or it's a hypothesis
Claims that can't be checked against an external source of truth are treated as hypotheses, not facts.
We don't ask the field to believe a story about mechanism. We show a scoreboard.
The platform makes blind, pre-readout calls on real clinical trials — committing to a direction, a magnitude, and a verdict in advance — and we keep a continuously audited record of how we do. It's bound to calendar time: it can't be reconstructed after the fact, and it can't be reproduced by anyone who wasn't making the calls before the outcomes were known.
The discipline is the credential.
Not a black box. Not curve-fitting.
OVIVO stands against the two dominant failure modes of computational drug development. We lead with what we do, and sharpen it by contrast — once, plainly.
Two ways the field gets it wrong
- Black-box AI that emits a number with no chain of custody and asks for your faith.
- Forecasting by analogy — guessing a trial's outcome from its statistical resemblance to past trials.
What we do instead
- Mechanistic and provenance-anchored: a chain of reasoning you can follow, end to end.
- We model the biology and forecast from mechanism — then check it against reality.
AI orchestrates, handles uncertainty, and writes the report. The science underneath is mechanistic, traceable, and falsifiable.
Start from where you are
The same platform, pointed at the question in front of you. Find your situation and follow it deeper.
Discovering a new drug
Start from what the disease needs. Identify targets from biological gaps, generate candidates, and put every one through the full chain under the same unforgiving scrutiny.
Explore discovery →A program is stuck
Wrong dose, failed endpoint, repurpose or kill? Simulate the next move across the full causal arc before you commit the spend.
Explore simulations →Choosing the right cohort
Forecast outcomes by patient population. Find the cohort where your drug actually works — before anyone enrolls.
Explore trials →Treating a complex case
Rare, treatment-resistant, or off-protocol. Run the platform at the level of one patient — their genetics, their disease, their current treatment. Research data, not clinical advice.
Explore precision medicine →A platform that compounds
OVIVO isn't a fixed product we ship and maintain. It's a living scientific ecosystem that grows with every program we touch — and grows in a way that's structurally hard to replicate.
It compounds, it doesn't plateau
Every program locks new, calibrated science into a shared foundation. A pathway mapped for one disease illuminates another; a calibration earned on one drug class sharpens the next.
Negative results are an asset
We keep an audited catalogue of what doesn't work, and exactly why — so no one pays to learn the same lesson twice. We're as proud of those as of the wins.
Modality-agnostic, delivery-first
Small molecules, degraders, peptides, oligonucleotides, antibodies — chosen to fit the biology. A molecule that can't reach its target isn't a therapy, no matter how well it binds.
A named limit is not a wall. It is a target.
Some biology isn't yet measured well enough for anyone to model faithfully. Some targets resist the structure that rational design needs. Where those limits exist, we name them — clearly, in the open, without euphemism. We'd rather you know exactly where our confidence ends than discover it the expensive way.
Nearly every boundary we've hit has become, in time, a research initiative and then a capability. We don't promise we can model everything. We promise we know what we can't model yet, why, and what it would take to change that.
Forward-looking · the frontier
The deepest layer of biology is, ultimately, physics. We're investing in how emerging quantum approaches to molecular modeling could let the platform reason about binding and selectivity at a fidelity classical approximation can't reach. We present this as what it is — a direction we're actively building toward, not a feature we claim today.