AI Platform for Biotech
Turn scientific data into a shortlist worth testing.

Prepare the assay data, narrow the candidate space, and bring activity, liability and uncertainty into the decision about what to test next.

Talk to UsExplore Your Challenges
BUILT AROUND YOUR WORK

Make the next experiment a better-informed choice.

A small team cannot test every candidate. The value is in deciding what deserves scarce assay capacity—and understanding what the model cannot tell you.

Biotech & DiscoveryIllustrative workflow · explore a challenge
WHERE THE WORK GETS STUCK

The candidate library is larger than your screening capacity, and a ranking alone does not explain what should advance.

Target & candidate library
Activity and screening evidence
WHAT YOUR TEAM GETSFor human review

A shortlist for experimental review

  • Candidates prioritised
  • Supporting reasoning attached
  • Next experiment remains a human decision
DECISION OWNERDiscovery scientist
HOW WE HELP

Use target-led screening and candidate scoring to assemble a shortlist with reasoning and evidence for scientific review.

Explore the relevant workflowsTherapeuticDiagnostics
Published research benchmark−21.6%

Model error vs standard preparation on the published MDM2 benchmark; R² 0.87.

Explore the foundation
WHAT WE BRING TO THE TABLE

AI for biotech research, from shortlist to liability check.

We scope the combination around your process and data. Each workflow below has its current development status shown.

01Supported workflow
Research & Development

Therapeutic

A shortlist you can defend.

Curated data — including drug repurposing sets — narrowed in silico to a ranked shortlist, reasoning attached.

Explore Therapeutic
02Supported workflow
Research & Development

Diagnostics

Simulate 1%. Predict the rest.

Simulate a representative sample, train on it, and score the full library before the bench.

Explore Diagnostics
03Supported workflow
Research & Development

ADMET

Catch liabilities early.

Stereochemistry kept as signal, and every model checked against real chemistry — not dataset artefacts.

Explore ADMET
More workflows for Biotech & Discovery3 more
THE OUTCOME TO DESIGN FOR
  • Model-ready data with a documented data card
  • Ranked candidates with reasoning attached
  • Prediction limits stated before lab prioritisation
A PRACTICAL FIRST CONVERSATION

Start with the decision behind your next round of experiments.

You do not need a finished specification. Start with the work you want to improve.

01 / YOUR CONTEXT

What helps us understand the problem

  • Your target and candidate-selection question
  • Available assay or screening data
  • The limits of your experimental capacity
02 / OUR DISCUSSION

What we’ll work through together

  • Whether the data can support the intended model
  • What a useful shortlist needs to explain
  • How success should be checked experimentally
Bring the people closest to the work
Head of R&DHead of Data Science or AIResearch leadership

What should your team test next?

Bring your target, available data and selection challenge. We’ll discuss what can be prioritised computationally and what still needs the lab.

Bring your challenge.
Let’s find the right next step.

Talk Through Your Challenge
Looking at a different type of organisation?
Pharmaceutical ManufacturersGeneric ManufacturersCDMOsResearch Teams

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