A new standard in AI discovery.
From disease signals to promising therapies.
Guided by AI.
Tested in human ex-vivo models.
Our AI model screens disease omics to
predict safe repurposed drug combinations
from cancer treatment datasets. Ranking
1.5M+ drug combinations for cancer to
surface the most promising candidates for
further validation in human ex-vivo
models.
Top-ranked candidates are validated in
human ex-vivo models derived from patient
tissue. These models preserve the tumor
microenvironment, enabling a realistic
measure of drug response before any
clinical advancement, filtering out weak
candidates early.
We track ex-vivo cancer response and
healthy-cell selectivity side by side.
Only combinations that kill cancer cells
while sparing healthy tissue advance to
the next stage. This dual readout ensures
both efficacy and safety are validated
before further investment in clinical
development.
The strongest candidates are moved toward
further studies with comprehensive data
packages from AI ranking and ex-vivo
validation. Our goal is to de-risk each
therapy candidate before it enters
costly late-stage development, maximizing
the probability of success for every
program that advances.
Neoantigen
Cancer Targets
Multi-Epitope
Cancer & Aging
Senolytic
Aging Targets
HLA-Aware
Immune Response
AI-Guided
Formulation
LNP/Lipid
mRNA Delivery
Patient-Derived
Cancer Response
Safety Readout
Normal Tissue
IC50 Curves
Dose Optimization
Multi-Omic
Pathway Analysis
Longitudinal
Drug Resistance