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Resolving Target Biology and Compound MOA at Scale: Enabling AI to Chat with Cells on the BioGPU Runtime
Disease is driven by complex biological networks that remain only partially understood until experimentally resolved as a whole.
Targets and compounds advance without clear biological understanding, leading to failure.
AI and large-scale experimental data have scaled hypotheses and insights. The bottleneck now shifts to biological resolution.
Static data, including omics, cannot capture cellular biology in action, where pathways interact and drive disease.
Without experimental access to biology inside cells at AI scale and speed, target biology and compound MOA remain only partially understood.
A new paradigm brings cellular biology directly into AI: BioGPU, the biology-resolution runtime for AI. Introducing visual computing — a high-content, pathway-level imaging experimental modality in cells at scale, capturing biology in action.
This generates disease biology models of pathway and network activity in real cells.
Through the BioGPU agent, AI models chat with cells, operating on these models at runtime in a continuous loop of hypothesis, experiments, and refinement to:
- Resolve target biology — what targets do and how they are regulated, enabling causal targets to advance and weak targets to be killed
- Resolve compound MOA, how compounds modulate the system, including pathway and off-target effects
You will see this in rheumatoid arthritis and ALS case studies, where BioGPU connects to frontier AI systems, ChatGPT and Gemini AI co-scientists, operating on the BioGPU models at runtime to test and refine hypotheses, deprioritize inactive pathways, surface novel mechanisms and high-confidence targets.
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