Build a closed discovery loop of AI-to-biology
Biology GPU
The experimental runtime for AI drug discovery
We’ve built the experimental reasoning language AI needs to understand biology
Pathways seen in action
in space and time
computable for AI
AI Models
Multimodal Disease Models
Experimental Reasoning Loop
Biology GPU
Visual Biology AI Model
Multiple cell types across diseases, conditions and perturbations
The Biology GPU
The BioGPU’s Visual Biology Agent transforms AI models into experimental self-driving discovery engines, creating an explainable reasoning loop grounded in real biology that discovers causal targets and better drugs
Visualization layer: Proprietary visualisation modality that directly images biological pathways and mechanisms in action inside cells at AI scale
Biology layer: Large-scale experimental infrastructure that enables AI models to visually compute inside cells and reason experimentally about disease mechanisms
Compute layer: A Visual Biology Model trained on more than 2 billion proprietary pathway images, designed to recognize active biological pathways in healthy and diseased cells. Its runtime can be deployed in a customer’s private cloud
Access layer: The BioGPU Agent connects AI models (LLMs) to the BioGPU infrastructure
Visual Biology Model
Trained on 2B+ process visualizations, it recognizes active pathways in cells
Enabling experimental reasoning in real biological pathways
Visual mRNA Biology Model
Trained on mRNA biology regulatory visualizations mechanisms
Enabling the discovery of target modulators through mRNA biology
Visual Biology GPU Agent
LLM’s research partner for experimental reasoning inside cells
The functional biology modality AI needs, at the scale it thinks
A unified experimental imaging modality that makes functional biology at scale ready for AI
Giving models explainable insights to experimentally reason on disease-driving mechanisms
Visual Computing
inside cells
inside cells
Biological Processes and Pathways
Genomics
Transcriptomics
Proteomics
Pathology
Cellular morphology
What AI can do with the Biology GPU
Disease Signature & Target ID
Discover pathway signatures - compare cellular systems
Discover novel targets that modulate pathway signatures
Target Validation
Understand a target’s biological function
Compare and rank targets by pathway modulation
Hit Identification
Discover compounds with pathway selectivity
Compare clusters for their biological activity - de-risk programs early
Hit To Lead
Elucidate compound’s MOAs
Optimize activity on core pathways
Lead Optimization
Optimize chemistry in a visual biology loop - optimize pathway activity
Predict off-target effects
Preclinical Development
Visual computing toxicity panels
Compare biology across human and animal models
+ Additional use cases in mRNA biology with a specialized mRNA neural network
Partner with Anima to Build an
End-to-End AI-to-cell Stack
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Biology GPU
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Biology GPU Agent
See and compute on pathways inside real biology
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Targets and Drugs
Identify disease driving pathways, novel targets and drugs that modulate the phenotype