ParsimonicBiology's models have never seen an experiment fail.
They're trained like multimodal language models, and scale doesn't fix what that misses. We build biologically informed models that learn from the experiments that failed.
01 — What we build
A library, not a model
Small models, each measured against the specific biological property it handles.
An agent that knows which one you need
It reads a campaign's results, diagnoses what the failures share, and reaches for the specialist that covers it.
Diagnoses that get checked
Every hypothesis the agent makes is a prediction about the next round. The assay settles it, not the explanation.
The result is fewer rounds between a campaign starting and a design that works.
02 — Where it applies
- Protein therapeutics
- Binder and enzyme campaigns, where developability failures surface late and expensively.
- CAR and receptor design
- Modular constructs where function comes from how domains combine.
- Regulatory elements
- Promoters, enhancers and UTRs.
- Capsids and delivery
- Library-scale design where sequence likelihood predicts fitness poorly.
- Personalized genetic medicine
- N-of-1 constructs, where there are only a few shots and no room for a screen.
03 — Why this matters
Most breakthroughs are gated by a number of experiments. The drug that ends a disease, the material that makes fusion work — each sits behind a count of attempts nobody has found a way to reduce. We work on reducing it.
04 — Contact
We work inside existing design programs. Send us a campaign and we'll show you what the last round missed.
info@parsimonic.com