Fixing the biology bottleneck
Amyloid drug discovery has been held back by challenging targets. We re-engineer these amyloids for high-throughput binder discovery.
The challenge
Two bottlenecks we set out to fix.
AI-driven workflow
The only input is the drug target. From there we find, predict, produce and test binders in short cycles that pair generative AI with an accelerated lab.
Generative AI
Accelerated lab
Input
Drug target
A disease-relevant protein or amyloid assembly.
01
Find
Map the binding surfaces worth targeting.
02
Predict
Design binder candidates and score them in silico.
03
Produce
Make the top-ranked binders in our lab.
04
Test
Measure binding, specificity and function.
Engineering target and binder
Precise protein assemblies for precision therapeutics.
We engineer precise protein assemblies to discover precision therapeutics at scale. We focus on binding surfaces that are difficult to access with standard in vitro libraries or in vivo approaches.
01
Proprietary libraries
Amyloid-focused binder libraries tuned to diverse fibril architectures, for rapid hit discovery across neurodegenerative and systemic targets.
02
Amyloid-tailored validation
We test hits against disease-relevant fibril states and select for polymorph specificity and functional activity, not pan-amyloid binding.
03
Fast iteration cycles
Short design, build and test loops generate more developable hits and filter out liabilities early.
