PharmaOS connects computational discovery to clinical reality — grounding AI in physics, synchronizing lab and clinic in real time, and turning CRO evidence into actionable protocols.
Digital Twin Powered Clinical Trial Simulation and Safe Protocol Design.
Over $4 billion invested. Hundreds of AI-native biotechs launched. Yet process failure — not scientific failure — remains the leading cause of drug attrition.
WHERE DRUGS DIE — The Process Failure Map
State-of-the-art models memorize patterns, not physics. CRO data sits in silos, disconnected from protocol design. Patient enrollment fails because trial criteria are written without real-world biomarker grounding. PharmaOS closes these gaps — at every handoff where drugs die today.
A unified operating system that connects discovery, validation, manufacturing, and clinical execution in a single, bidirectional data environment.
Multi-relational knowledge graphs map biological targets, disease pathways, and compound interactions at scale.
Every AI hypothesis is stress-tested against binding geometry, metabolic stability, and physicochemical constraints before CRO handoff.
Absorption, distribution, metabolism, excretion, and toxicity profiling applied at hypothesis stage — not post-synthesis.
Real-time virtual replicas of CRO environments predict deviations and flag risks before experiments begin.
Validated assay results automatically generate structured clinical protocol drafts with dosing, checkpoints, and enrollment criteria.
Safety signals and efficacy gaps route back into the discovery engine as hard constraints — redirecting synthesis in real time.
AI-driven cohort matching against genomic and phenotypic eligibility markers, grounded in real-world data.
Clinical guidelines, dosing schedules, and checkpoint logic encoded as structured, evidence-linked protocol objects.
Evidence-triggered amendments reduce lag from months to days as trial data accumulates in real time.
Watch how PharmaOS connects discovery, validation, and clinical protocols in a single unified workflow.
State-of-the-art protein and molecular models memorize patterns. They fail at novelty because they don't understand physical constraints.
PharmaOS wraps every AI hypothesis with a Physics-First Validation layer — checking synthesizability, binding geometry, metabolic stability, and CRO manufacturability before a single experiment is greenlit.
Every candidate compound is scored against reaction feasibility models before CRO handoff. Eliminates compounds that look good computationally but cannot be manufactured.
Pre-CRO GateAbsorption, distribution, metabolism, excretion, and toxicity profiling applied at hypothesis stage — not post-synthesis when costs have already compounded.
Hypothesis StageCRO digital twins simulate manufacturing parameters and predict process deviations before physical experiments begin — reducing wasted cycles by orders of magnitude.
CRO IntegrationDrug development has always been a one-way street. PharmaOS makes it a live feedback loop — evidence flows in both directions, in real time.
The result: Every drug candidate is continuously informed by clinical reality — from first hypothesis to final protocol. Failures upstream cost days. Failures downstream cost billions.
Live CRO Integration ActiveDisease-domain-specific protocol stacks with structured patient identification, enrollment workflows, and biomarker-gated decision trees — all connected to the evidence layer.
AI-driven cohort matching against genomic and phenotypic eligibility markers. Surfaces qualified patients before sites go live — reducing recruitment lag by surfacing the right population from real-world data.
Real-world data integration to surface qualified patients before sites go live. Protocol criteria are auto-validated against historical enrollment data — reducing the #1 cause of trial delay.
Evidence-triggered protocol amendments as trial data accumulates — reducing amendment lag from months to days. Safety signals and sub-group findings automatically update protocol objects.
We are onboarding research teams, CROs, and clinical operations leads who are ready to stop losing drugs to process failure.