A view of the future.
…they will completely outthink their makers.
…we’re now at the beginning of inorganic, or mechanical, evolution, which will be thousands of times swifter.
Our next chapter.
Medicine has a
capacity problem.
Another promising target. Another programme waiting for a team. Too much biology remains beyond the reach of the way we discover drugs.
We’re building drug-discovery capability into a scalable AI system: one intelligence designed to coordinate many therapeutic campaigns in parallel and carry relevant findings from each experiment across the portfolio.
This is neo pharma.One intelligence. Many campaigns.
Proteus One
One system built towards general intelligence for drug discovery. Designed to scale across targets, modalities and parallel campaigns.
Solid tumours
Two arms. One intent.
Bring tumour recognition and T-cell recruitment into one bispecific antibody.
- Tumour recognition
- T-cell recruitment
- A shared IgG scaffold
Discovery capability
that scales.
More programmes in parallel, with experimental learning shared across the portfolio.
Focuses
Our starting priorities for therapeutic discovery.
Undruggable targets
Rare diseases
N-1 therapeutics
What we mean by neo pharma.
Drug-discovery capability, built into a scalable AI system. A ground-up pharmaceutical model for multiplexed campaigns, shared learning and horizontal scale.
Read our definition of neo pharmaWhat is neo-pharma?
Neo-pharma is a ground-up pharmaceutical model in which drug-discovery capability is built into a scalable AI system. It connects target discovery, therapeutic discovery and clinical forecasting to run large, multiplexed campaigns across an owned portfolio. Relevant findings inform the next decision across campaigns, allowing discovery capacity to expand without building a separate organisation around every programme.
How does neo pharma relate to AI drug discovery?
Neo pharma describes how the pharmaceutical company is built. Its scalable AI system is the core discovery capability, connecting target selection, therapeutic design, experiments and clinical reasoning across concurrent campaigns. At Proteus, the ambition is general intelligence for drug discovery, developed through experience across an owned portfolio.
What are multiplexed therapeutic campaigns?
Multiplexing means running many campaigns at different stages through a shared intelligence. While one experiment is running, another campaign can be designing candidates and another interpreting results. Evidence returns across the portfolio continuously, and relevant findings can improve the next decision in more than one programme.
What does horizontal scale mean for a drug company?
It means expanding the number of therapeutic programmes the company can pursue without increasing programme teams in the same proportion. AI coordinates research across targets, modalities and experimental partners. Biology still takes time; the opportunity is to overlap more work and make each result useful across the portfolio. This is the operating model we are building.
What is Proteus One?
Proteus One is the intelligence architecture we are developing for this model. Asclepius focuses on target discovery, Proteus on therapeutic discovery and Pythia on clinical forecasting. Each system has a distinct role and can pursue questions independently, while shared objectives, evidence and experimental feedback connect their decisions. The connected architecture is in development.
Where do experiments fit?
Experiments test the biological and therapeutic hypotheses. In the model we are building, their results inform subsequent designs, target choices and clinical questions. A prediction remains a hypothesis until it has the evidence needed to support it.
What is Proteus working towards?
Our starting priorities are undruggable targets, rare diseases and N-1 therapeutics. These focuses guide the discovery capability and campaigns we are building.