Generative design platforms create new sequences toward a functional goal. Orbion doesn't design de novo — it characterizes, ranks and de-risks candidates for expression, stability and developability. The two are complementary: design proposes, Orbion helps you decide what to make.
You're mapping where generative protein design fits, and whether it's the same thing as protein characterization.
The Difference
What Actually Separates Them
It's easy to bucket every protein-AI company together, but design and characterization are different jobs. A generative design platform starts from a functional goal and invents new sequences to reach it. That is a creation problem.
Orbion starts from candidate sequences you already have — designed, natural, or mutant — and tells you which will express, stay folded and behave, with the liabilities flagged. That is a selection problem.
The two sit at different points in the same pipeline. Design proposes; characterization decides what's worth making. Used together, a design loop produces candidates and Orbion ranks them for expressibility and developability before any reach the bench.
At A Glance
Two Different Jobs
De Novo Design
- Starts From A Functional Goal
- Generates New Sequences
- Optimizes Toward Binding Or Activity
- A Design–build–test Loop
Characterization & Triage
Orbion- Starts From Candidate Sequences
- Predicts Expression, Stability, Developability
- Ranks What To Make First
- Bench-Ready Constructs & Protocols
Side By Side
How They Compare
| De Novo Design | Orbion | |
|---|---|---|
| Primary Job | Create New Proteins | Characterize & De-Risk Candidates |
| Input | A Functional Goal | Candidate Sequences |
| Output | Novel Sequences | Ranked Constructs, Liabilities, Protocols |
| Expression & Yield Read | Not The Focus | Predicted From Sequence |
| Where It Sits | Upstream Generation | Downstream Selection |
| Works Together? | Produces Candidates | Ranks Them Before The Bench |
When You Want De Novo Design
If your goal is to invent a binder or enzyme that doesn't exist yet, that is a generative design platform's job, not Orbion's. The natural pairing is to bring the candidates a design loop produces to Orbion, to rank them for expressibility and developability before you commit them to the bench.
Where Orbion Fits
The Judgment Layer, Prebuilt
- Orbion is the selection layer, not a design engine. It takes any set of candidates — designed, natural or mutant — and predicts which will express, fold and behave.
- That makes it complementary to de novo design: the generative loop proposes, Orbion de-risks, and only the winners reach the wet lab.
The model is the cheap part. The product is the judgment around it — model routing, calibrated confidence, low-confidence flags, and a workflow that stops a bad prediction from becoming a bad decision.
The Evidence
Backed By Bench Results, Not Just Claims
Prospective and blind where noted. The signal is in the ranking — absolute numbers still need your wet lab, which is why every engagement starts with a blind benchmark on your own targets.
Go Deeper
Related On Orbion
FAQ
Common Questions
No. Orbion characterizes and ranks candidates you bring — designed, natural or mutant. De novo generation is a different job, handled by design platforms.