Run fewer experiments. Find the winners first.
ML models built around your problem, validated against your bench, and wired into your lab. Your data never leaves your environment.
Predicted Shortlist
From your panel
Wet-lab plan
Run the shortlist
vs. full panel
Focus instrument time
Your data stays put
The Difference
Models built on your problem, validated against your bench.
Discovery ML is only useful if it works on your targets, your formats, your data — and integrates cleanly into the lab software your team already uses. Here’s how we approach it.
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Co-developed on your data
Modeled around your targets, formats, and assay
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Back-tested against your bench
Predictions scored on your measured wet-lab results before production
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On-prem / in-VPC by default
Proprietary sequences never leave your environment
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Wires into your existing workflow
Predictions flow into your Wizards and LIMS, not a side tool
What You Get
Outcomes, not abstractions.
A shortlist before the bench
Wet-lab time runs on the candidates most likely to win, not the whole panel. Free up your bench for the work that pays off.
Built around your problem
Models co-developed for your target, your assay, your data. Not a generic platform you spend months bending to fit your workflow.
Wired into your existing workflow
Predictions flow directly into your existing GeNovu Wizards and LIMS. No second system, no manual hand-offs.
Your data stays put
On-prem or in your VPC by default. Proprietary sequences and program data never leave your environment.
Validated against your bench
Pilots are scored against your measured results before any production use. Confidence is earned, not assumed.
Fewer dead ends, faster cycles
Focus expensive instrument time where confidence is high. Cut the panel before the wet lab, not after.
Why Enterprise-Grade
ML that meets pharma where it actually operates.
Built to clear the bar your security, scientific, and procurement reviewers will hold it to.
Data sovereignty by default
On-prem or in your VPC. Proprietary sequences and program data never traverse third-party APIs or public servers. Built for pharma security posture.
Connects to your existing workflow
Predictions flow into the GeNovu Wizards and LIMS your team already uses. No second system for them to learn, no manual hand-offs to maintain.
Validated against your own wet-lab data
Co-development with a back-test gate. Pilots are scored against your measured results before you rely on them — not vendor benchmarks on someone else’s data.
Reproducible engineering
Pinned scientific stack, negative controls, honest evaluation. The same scientific rigor we ship in regulated lab software, applied to discovery ML.
Engagement Model
How a Custom ML engagement runs.
We co-develop with you. Pilots are validated against your measured data before you rely on them.
Discovery
We sit with your team to scope the problem, the data, and what "win" means for your wet lab.
Pilot
A bounded engagement on your data and your target. Clear deliverables and timeline.
Back-test
Validated against your measured results before any production use — so confidence is earned, not assumed.
Productionize
Integrate into your existing GeNovu workflow and harden for ongoing use.
Who It's For
Teams choosing candidates for expensive experiments.
If your wet-lab kinetics or developability work is the bottleneck — and your panel is bigger than your throughput — predictive triage is where ML pays off first.
- Antibody and protein discovery teams
- Bioanalytical and PK reagent development
- Immunogenicity (ADA) assay development
- In-house discovery teams choosing candidates for the bench
- Big Pharma, biotech, and large CROs
Got a panel that's too big for your bench?
Tell us about your target, your candidates, and what "win" looks like for your wet lab. We'll scope a pilot that's validated against your data before you rely on it.