Protein
localization
BioGPU
The cellular biology runtime for AI agents
The cellular biology runtime for AI agents
Enabling AI to close the loop with functional biology to design better, safer drugs.
With BioGPU, AI agents see and experiment across cellular networks at scale to discover how molecules change biology and design better ones.
AI designs molecules
BioGPU runs cellular-network experiments at scale
Cellular Network Response
Functional Biology. Now in the Design Loop.
Giving AI Eyes on Cellular Networks.
BioGPU is a purpose-built AI-to-Cell technology stack that generates a new class of imaging data, enabling its Foundation Model to recognize molecular activities across cellular networks.
VISUAL BIOLOGY
FOUNDATION MODEL
FOUNDATION MODEL
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RNA granules
-
Phospho-signaling
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mRNA Translation
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Cytoskeletal dynamics
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Protein localization
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Protein interactions
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Mitochondrial state
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Nuclear translocation
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• • •2,500+ activities
2,500+
Molecular activities
1,000+
Experiments per run
Cells
Millions across conditions
Trained on 2 Billion Images. Built to Recognize Cellular Networks.
Cellular Activity Images
Visual representations of biological reality
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Nuclear
translocation -
Phospho-
signaling -
Cytoskeletal
dynamics -
RNA
granules -
Mitochondrial
state -
Protein
interactions -
mRNA
localization
VISUAL BIOLOGY
FOUNDATION MODEL
Across 140 cell types and 25 diseases
Visual Biology Tokens
Biological meaning
TDP43 accumulates in the cytoplasm.
mTOR signaling is hyperactivated.
Mitochondrial fragmentation increases.
RNA granule formation is enhanced.
Protein co-localization increases under perturbation X.
Nuclear translocation of TFs is enriched under condition Y.
The model recognizes molecular activity patterns across new cellular contexts, including patterns absent from its training examples.
Cellular Network Biology. From Inside Cells to AI’s Context Window.
Each Visual Biology Token links an observed molecular activity to its biological meaning and experimental evidence, making it accessible to AI.
FRONT: THE MEANING
Biological finding
VB-0001
Abnormal NUP62 distribution in both cytoplasm and nucleus reflects misregulated nuclear pore function and nucleocytoplasmic transport defects.
VB-0002
DDX6, SMNDC1, and ELAVL4 are identified as functionally relevant regulators of selection of the distal polyadenylation site in the SOD1 3'UTR.
front
VISUAL BIOLOGY TOKENS
THOUSANDS OF TOKENS
produced per experiment
Across pathways, cell types, conditions and diseases
back
BACK: THE EVIDENCE
Dynamic visual evidence
NUP62
H
D
DDX6,
SMNDC1,
ELAVL4
SMNDC1,
ELAVL4
si-Non-Targeting
si-DDX6
si-SMNDC1
si-ELAVL4
Hundreds of tokens from the same BioGPU experiment collectively represent cellular network biology, enabling AI agents to reason across its activities and interactions.
AI Agents Experiment Inside Cellular Networks
The BioGPU Agent designs network-level experiments, learns from the results, and uses the bioGPU Program’s experimental cellular intelligence to decide what to investigate next.
BioGPU Agent
Designs experiments and learns from the results.
BioGPU Pod
Executes Visual Biology experiments in cells.
Visual Biology Model
Understands cellular activities from images.
Visual Biology Tokens
Image + biological meaning + context.
1
Design next experiment
2
Run experiment in cells
3
Generate Visual Biology data
4
BioGPU Program Runtime
Experimental cellular intelligence that compounds over time.
Every experiment advances the next.
Every iteration deepens the understanding.
More Biology Per Experiment.
A richer view of cellular biology in a single experiment, capturing multiple mechanisms and their interactions together.
More Intelligence With Every Iteration.
Each experiment deepens the understanding of the cellular network, creating a learning loop that generates new hypotheses and guides the next experiment.
Experiment 1
Initial understanding
Initial understanding
Experiment 2
Deeper network understanding
Deeper network understanding
Experiment 3+
New hypotheses and directions
New hypotheses and directions
Experimental Learning Loop
Resolve the Biology. Guide the Chemistry.
AI agents use BioGPU to resolve the cellular network around a therapeutic target, then measure how their designed molecules change that network to guide the next design.
AI AGENT
Designs molecule
BioGPU
Experimentally resolves the cellular network
EXPERIMENTAL EVIDENCE
Network response to the molecule
Evidence guides the next design
See AI Agents Become Experimental Cell Biologists
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