Operationalize large perturbation models in your environment
Turn heterogeneous perturbation data into AI-ready products, then build, benchmark, fine-tune and deploy prediction models that generalize across experiments.
You run huge, expensive perturbation screens, and still can’t reliably predict an unseen perturbation in a cell type you care about. Ardigen turns your heterogeneous screens into an AI-ready substrate, then builds and benchmarks models that predict perturbation, readout and context (P/R/C) jointly, so effects generalize across experiments, inside your environment, with your IP staying yours.
Per-dataset models don't transfer, and predictions fail out-of-distribution
The pipeline breaks at every hand-off. You generate perturbation data: Perturb-seq, CRISPR, compound and Cell Painting screens, but the data barely overlaps across different readouts, contexts and protocols. Per-dataset models don’t transfer, so insights don’t integrate across experiments. Predictions fail out-of-distribution the moment a new cell type or perturbation appears.
The result: no in-silico prioritization, so cost and cycle time stay high and most of the biology stays unexplored.
Where the work gets stuck
Time
Every new target or context needs a fresh screen, months per cycle.
Money
Perturbation screens are among the most expensive assays, and FM compute adds GPU burn on top.
Risk and trust
Published benchmarks find single-cell FMs often no better than simple baselines on perturbation prediction.
Missed opportunity
The highest-value contexts, rare, primary, diseased cells, can’t be screened directly, exactly where models are weakest.
We adapt and operationalize the right model for your data and question, we don’t ship one fixed algorithm or a virtual cell in a box. We harmonize data across screens into an AI-ready data product, then build, fine-tune and benchmark models that predict perturbation, readout and context jointly, so effects generalize across experiments.
Multimodal by design: we link chemistry (R-MAT), phenotype (Cell Painting FM), transcriptome, proteome and spatial, then deploy scientist-facing in-silico screening, MoA, gene-interaction networks and repurposing inside your environment.
Four steps to in-silico leverage
Make the data AI-ready
Harmonize heterogeneous screens into a single perturbation substrate.
Build & tune the right model
Select and fine-tune among STATE, LPM-style P/R/C, GEARS, CPA and scGPT for your data — not one fixed architecture.
Benchmark for trust
Decision-relevant evaluation that proves the model beats simple baselines and generalizes to unseen perturbations and contexts.
Deploy in your environment
In-VPC or on-prem in-silico screening, MoA and prioritization your scientists can act on.
One roof for molecules, phenotype, omics and spatial
Benefit pilars
Molecular structures
R-MAT molecular representation for compound screens and structure-aware prediction.
Phenotype
Cell Painting foundational model for single-cell morphological representation.
Transcriptome
Perturb-seq and single-cell FMs (STATE, GEARS, CPA, scGPT) tuned to your biology.
Proteome
Give teams a shared, documented product with aligned metadata, quality criteria, provenance and access patterns.
Spatial
Spatial foundation models for context-aware data preparation and modeling.
In-silico leverage on the data and compute you already pay for
Benchmarking-first, multimodal, and built to run in your environment
Ardigen combines life-sciences data engineering, multimodal modelling and decision-relevant benchmarking to turn perturbation screens into models your scientists can act on. We build on existing foundation models and data: operationalizing, tuning and validating them for your biology, inside your environment.
Benchmarking-first
We prove a model beats simple baselines and generalizes out-of-distribution before you act on it, the trust gap the whole field has
Multimodal perturbation depth
Molecular structures + phenotype + transcriptome + proteome + spatial under one roof; SC-MAE + R-MAT together gave 3× more high-performing assays.
Operationalization, not research code
We productionize research-grade FMs into stable, deployable systems.
Right model, tuned
We select and tune among STATE, LPM-style P/R/C, GEARS, CPA and scGPT for your data — not one fixed algorithm.
Your data and IP stay yours
In-VPC or on-prem, no training on your data; biology-close delivery, not generic engineering.
Generalizes where it matters
Predict unseen perturbations and new cell contexts, including rare, primary and diseased cells you can’t screen directly.
Frequently Asked Questions
What is a large perturbation model?
A model that predicts how cells respond to perturbations, across perturbation, readout and context, so insights transfer between experiments instead of being trapped in one dataset.
Do you generate the screening data?
No. We make your existing screens AI-ready and build the models on top; wet-lab generation stays with you.
Which models do you use?
We select and tune the right architecture per data and question: STATE, LPM-style P/R/C, GEARS, CPA, scGPT, rather than forcing one fixed model.
Will our data stay within our environment?
Yes — in-VPC or on-prem. We don’t train on your data, and your IP stays yours.
Can our team extend and maintain the models after delivery?
Yes, we productionize with stable APIs and unified data loaders, so your team can keep building
Start with one scientific question, modality or data bottleneck
See the impact
Case studies
Contact
You already have the screens. Get the in-silico leverage.
Bring us your perturbation data and the predictions you need to trust. We’ll show you what a benchmarked, deployed model changes.
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