Virtual Cell
Biological intelligence,
rendered at atomic fidelity.
Three pillars of
biological intelligence
Proteomics
Full-atom protein structure prediction and interaction mapping across 200M+ annotated sequences.
Genomics
Sequence analysis, CRISPR target prediction, and epigenetic mapping at single-base resolution.
Simulation
Molecular dynamics, Monte Carlo sampling, and quantum chemistry computations in real time.
How the engine
renders reality
Each simulation passes through four precision-engineered stages, from raw molecular input to interactive real-time output.
Molecular Input
PDB structures, FASTA sequences, or CIF files parsed and validated before any computation begins.
PDB · FASTA · CIFForce Field Assembly
AMBER/CHARMM parameters applied. Charge distributions calculated at quantum precision.
AMBER99 · CHARMM36Parallel Integration
4.2 PF of FP64 compute across distributed tensor cores with adaptive time-stepping.
GPU · TPU · DistributedFidelity Render
Real-time WebGL visualization of the running trajectory. Export completed simulations to CIF, XYZ, or PDB.
WebGL · XYZ · ExportExplore the
cell atlas
Interact with 847 catalogued cell types
across 32 tissue families.
Simulation
in motion
Every computation rendered as it runs. Full trajectory access with sub-picosecond resolution and real-time energy tracking.
Begin your Simulation
Virtual Cell is currently in early access. Apply now to join research institutions, biopharma teams, and independent investigators already on the platform.
How something
like this gets built
None of the core engines need to be invented. The hard part is assembling them, scaling the infrastructure, and having the scientific expertise to know when the output is meaningful.
The 3D frontend — rendering the cell, shaders, scroll and mouse interaction. This is what you're looking at right now.
Already builtML models that predict a protein's 3D shape from its amino acid sequence alone. Open source and deployable on cloud GPUs.
Open sourcePhysics engines that simulate how atoms move and interact over time. Decades of academic research, freely available.
Open source200M+ protein sequences, 200K+ experimentally solved structures, and annotated genomes. All public, regularly updated.
Free & publicGPU clusters that do the heavy lifting. The primary cost driver — a meaningful cluster runs $2–5M per year at cloud rates.
Cloud availableThe hardest dependency. Domain experts who know whether the simulation results are physically meaningful — or just plausible-looking noise.
The real barrierReal-world equivalents: Schrödinger (founded 1990), D.E. Shaw Research (2002), Relay Therapeutics (2016). All took 5–10 years to reach product maturity.