One pipeline, from target to validated antibody.
Formula-Y fuses in-silico design with in-vitro validation into a single high-performance antibody discovery pipeline. Here is what happens at each stage, and why it changes the odds.
Design first. Test what deserves testing.
Everything up to the shortlist happens computationally. Only the strongest candidates reach the bench, and every result sharpens the next round of designs.
Two decisions that make or break a program. AI on both.
Antibody discovery hinges on choosing the right target and designing an antibody that binds it. Formula-Y brings AI to both, before the lab work starts.
Choose the right molecule to bind
Choosing a target still leans heavily on manual literature review, expert intuition and expensive screening. It is slow, hard to scale, and a major reason candidates fail later: a great antibody against the wrong target is still a dead end.
- AI analyses biological data to prioritise the most promising, disease-relevant targets
- A ranked, evidence-backed shortlist before any lab work begins
- Valuable when no obvious target exists yet, or when many compete
Design candidates before the bench
Our models design antibody candidates against the chosen target computationally, exploring a design space far beyond nature's repertoire, and rank them so that only the strongest go forward.
- Candidates proposed and ranked before any wet-lab work
- Higher hit rates, far fewer experiments on candidates that never had a chance
- New routes toward difficult, historically hard-to-drug targets
Prove it at the bench, with partners
Top-ranked candidates are handed off for in-vitro testing with laboratory partners. Results feed back to sharpen the models, closing the loop between computation and the bench.
- Testing run by partner labs
- Each round of results improves the next round of designs
- We are actively looking for validation partners
Discovery by design, not by screening.
Antibodies are one of the most important classes of modern medicine, used against cancers, autoimmune disorders and infectious diseases. But discovering them is complex, costly and slow, and still largely driven by laboratory trial-and-error.
Formula-Y turns that around. Candidates are designed and ranked computationally first, so laboratory effort goes only to the candidates most likely to succeed. That means fewer dead ends, faster programs, and routes to targets that have resisted conventional approaches.
Less trial and error. Lower cost. Fewer animals.
By replacing rounds of blind laboratory screening with data-guided design, the approach reduces trial-and-error, lowers cost, and cuts the reliance on animal use in the discovery phase. It also anchors high-value, digital R&D in Groningen and the Northern Netherlands.
Antibodies are among the most effective responses to infectious disease, autoimmune conditions and other emerging health threats. The ability to identify targets and design candidates quickly is a real capability for pandemic preparedness: accelerating the response in a warm outbreak phase, and building scalable groundwork in the cold phases between crises.
What a program with Formula-Y looks like.
Bring a target, or let us help you choose one. We return ranked candidates; your lab, or one of our partners, takes them to the bench. Every result makes the next round better.