The AI-Driven Operating System for Synthetic Biology
Build virtual cells that predict biological behavior in silico. Design, test, and optimize — before you step into the lab.
Our team comes from
4
Foundation Models
7
Open Source Tools
MIT
Licensed
Engineering Cells: Expensive and Slow
The DBTL cycle hasn't changed in decades — hundreds of iterations, 5-10% hit rates, and lab results that don't predict production scale.
Development time today
From idea to production, a single molecule takes 3-10 years
Cost per molecule
R&D costs $10-100M per molecule with trial-and-error approaches
Success rate
Only 5-10% of experiments produce commercially viable results
AI That Predicts Before You Experiment
Our foundation models learn from biological data to predict outcomes, design optimal sequences, and navigate vast design spaces — reducing time and cost by orders of magnitude.
100x Faster
Virtual cells compress DBTL cycles from months to days with in silico prediction
10x Cost Reduction
Replace expensive trial-and-error with data-driven model predictions
Higher Success
Physics-grounded predictions ensure lab-to-production alignment
Science-Backed
Models respect thermodynamic constraints — interpretable AI you can trust
Four Models, One Virtual Cell
Together, our foundation models form a virtual cell — a multi-scale, predictive model of biological behavior that accelerates the entire DBTL cycle.
Research
Multi-Modal Knowledge Agent
A multi-modal, multi-species knowledge agent that aggregates papers, databases, and experimental data for intelligent recommendations.
Dynamics
Surrogate Dynamics Model
A surrogate model respecting dynamics laws and constraints — predicting cellular behavior across scales.
Central Dogma
System-Wide Molecular Interactions
A model for system-wide molecular interactions — multi-scale modeling from DNA to RNA to protein.
Perturbation
Perturbation Response Prediction
A model for perturbation response prediction — navigate the genetic design space to achieve target phenotypes.
Open Source Ecosystem
Seven JAX/Flax NNX tools powering the next generation of computational biology. Free, open, and built for the research community.
Artifex
Modular generative modeling
Datarax
Data pipeline framework
Opifex
Scientific ML platform
Calibrax
Benchmarking framework
DiffBio
Differentiable bioinformatics
Playground
Model infrastructure
Avitai Knowledge
Knowledge extraction
import artifex as ax
from datarax import Pipeline
from opifex import PINN
# Build a generative model with Artifex
model = ax.FlowMatching(dim=128)
# Create a differentiable data pipeline
pipeline = Pipeline(["normalize", "augment", "batch"])
# Train with physics constraints
trained = model.fit(pipeline(data), physics=PINN())Built for Your Workflow
Whether you're an academic researcher or running enterprise R&D, we have the tools you need.
For Researchers
- Free open-source JAX/Flax NNX tools
- Reproduce and extend our methods
- Academic licensing available
- Community support via GitHub
For Industry
- Full platform access with foundation models
- Custom model training on your data
- Dedicated support and SLA
- On-premise deployment available
Built by Engineers and Scientists
Founded by engineers from leading tech companies and research universities, combining deep expertise in machine learning, software engineering, and computational biology.
Stay Updated on Our Progress
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