Life Sciences & Biotech

Protein design.
Reproducible, then tested.

The models are increasingly open. What separates a useful design campaign from a pile of plausible structures is the pipeline around them: filters, ranking, tracking and an honest hand-off to the bench.

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What we build

An AI-driven protein design pipeline chains structure prediction, generative design and scoring models into one tracked workflow, so every candidate sequence or molecule carries its full lineage: which model, which parameters, which filters it passed and why it was ranked where it is. We build and run that orchestration on your compute. We do not replace your computational biologists or chemists; we give them a pipeline they can rerun, audit and extend.

The components are typically open models your team already knows, for example AlphaFold-class or ESM-class structure predictors, diffusion-based backbone generators such as RFdiffusion, inverse-folding models such as ProteinMPNN, and docking or physics-based scoring. For small molecules the same pattern holds with generative chemistry, property prediction and synthesisability filters. Licence terms differ by model and some restrict commercial use, so model choice starts with the licence.

What decides whether it works

Filters you trust

Generation is cheap; selection is the science. Developability, aggregation, immunogenicity proxies and synthesisability filters are tuned with your scientists, and each is versioned.

Data from your bench

Every assay result is written back against the candidate that produced it, so the next round ranks on your own evidence, not only on the model’s confidence.

Not: a substitute for the assay

A predicted structure or score is a hypothesis. Nothing in the pipeline claims binding, function or safety; the wet lab decides, and the pipeline records what it decided.

A design round

DESIGN LOOP

01

Design brief

target, constraints, scientist-set
02

Generate

backbones, sequences or molecules
03

Predict and score

structure, affinity proxies, properties
04

Filter and rank

versioned rules, full lineage kept
05

Bench

scientists choose what to make and test
06 · Branch / escalation

Results back

assay data informs the next round

Solid: one design roundDashed: lab results feeding the next

Every candidate carries its lineage, so a surprising result can be traced back to the exact model, version and filter that produced it.

Questions

Questions about Protein & Molecular Design Pipelines.

What is AI-driven protein design?

It is the use of machine-learning models to propose protein structures and sequences with a desired shape or function, rather than only modifying known proteins. Typically a generative model proposes backbones, an inverse-folding model proposes sequences that should fold to them, and a structure predictor checks the result. The output is a ranked set of candidates for experimental testing, not a validated protein.

Can AI design antibodies?

Models can now propose antibody and binder candidates, and the field is moving quickly, but proposals still need expression, binding and developability testing, and many fail. A pipeline is useful when it raises the hit rate of what you test and keeps a record of why each candidate was chosen. We build that pipeline; claims about any candidate’s binding come only from your assays.

Do we need our own GPUs?

Not necessarily. Many design campaigns run well on rented cloud GPUs in a region you choose, and the pipeline can move later. Where sequences are pre-filing and confidential, running on infrastructure you control is usually the safer default. We size compute from a pilot round rather than guessing up front.