A new paradigm for target discovery.
We use AI and 20 years of proteomic data to put drug-discovery teams on the right target — before they spend years on the wrong one.
What "accessible" means, in biology.
A ranked target is only useful if a therapeutic can physically reach it. The endothelial surface — and the caveolae that pump cargo across it — is the accessibility layer AVATAR scores against.
Solving the real bottleneck
The hardest part of a therapeutic isn't making it potent; it's getting enough of it across the blood-vessel wall and into diseased tissue. Most discovery tools score whether a protein is present. AVATAR scores whether you can actually reach it. That delivery-accessibility problem is the one we solve.
Platform, not a pipeline
Cavatar is built around a data and AI engine, not a single drug. AVATAR carries antibodies, conjugates, and nanoparticles to a chosen tissue — program after program, customer after customer.
Data compounds
Every mass-spec run and every in-vivo validation sharpens the models and expands the atlas. Unlike a static drug pipeline, the platform gets more valuable — and more defensible — with scale.
From spectrum to ranked, deliverable target.
- Input
Mass-spec data
- Structure
AVAPROT engine
- Rank
AI scoring models
- Output
Ranked, delivery-aware targets