Four Foundation Models, One Platform
Four foundation models that work together as a virtual cell — predicting biological behavior across scales, from molecular interactions to production.
Foundation Models
Each model is trained on diverse biological data and designed to work seamlessly together across the engineering lifecycle.
Research
Your AI Research Assistant
Aggregate biological knowledge from scientific literature, databases, and experimental data. Get intelligent, contextual recommendations for pathway design and experimental planning.
Dynamics
Predict Cellular Behavior
Physics-grounded foundation model that learns cellular behavior patterns while respecting thermodynamic and biological constraints. Predict experimental outcomes before running costly experiments.
Central Dogma
From DNA to Protein
Multi-scale modeling of genetic information flow from DNA to RNA to protein. Design sequences that reliably produce desired proteins with predicted expression levels.
Perturbation
Design & Optimize
Navigate the vast genetic design space efficiently. Suggest optimal genetic edits to achieve target phenotypes through multi-objective optimization.
How It Works
Our virtual cell models accelerate the Design-Build-Test-Learn cycle with in silico predictions at every stage.
Design
Research model aggregates knowledge; Perturbation model suggests optimal genetic edits to achieve your target phenotype.
Build
Central Dogma model designs optimal DNA sequences, regulatory elements, and predicts expression levels for reliable construction.
Test
Dynamics model predicts cellular behavior and experimental outcomes, guiding experiment prioritization and reducing waste.
Learn
Experimental results feed back into all four models, continuously improving predictions and narrowing the design space.
A Better Way to Engineer Biology
See how an AI-driven approach compares to traditional synthetic biology workflows.
| Metric | Traditional Approach | With Avitai |
|---|---|---|
| Development Timeline | 3 - 10 years | Months |
| Cost per Molecule | $10 - 100M | Fraction of the cost |
| Experiment Success Rate | 5 - 10% | Significantly higher |
| Design Iterations | Hundreds of manual cycles | AI-guided, focused cycles |
| Scale-up Predictability | Low — lab results often fail at scale | Physics-grounded predictions |
Frequently Asked Questions
A virtual cell is a multi-scale AI model that predicts biological behavior in silico. Our four foundation models are trained on diverse biological data — sequences, structures, metabolic networks, expression profiles — and work together to simulate cellular processes, predict outcomes, design sequences, and optimize engineering strategies across organisms and applications.
The platform integrates four foundation models that together form a virtual cell mirroring the Design-Build-Test-Learn (DBTL) cycle. You describe your engineering goal, and the virtual cell predicts behavior, suggests genetic designs, and recommends experiments — all in silico before you step into the lab. After experiments, results feed back to improve future predictions.
Our models are pre-trained on public biological datasets, so you can start with just a description of your target organism and desired phenotype. As you provide your own experimental data — expression profiles, growth curves, metabolic measurements — the models adapt to your specific context and improve their predictions for your system.
Absolutely. Your data is encrypted at rest and in transit. We never share proprietary data between customers, and our models maintain strict data isolation. We offer deployment options that keep your data within your own infrastructure for maximum security.
Ready to See It in Action?
Request a demo to see how our platform can accelerate your synthetic biology workflow.