Our approach
Better drugs need the cell's biology inside the design.
Drug design today optimizes a molecule against its target. A disease is a system: thousands of molecular activities connected in a cellular network. A drug changes that whole network, so the network has to be part of the design. Anima's approach is to treat the disease as that system and to resolve it with experiments.
The problem
Design sees the target. The disease lives in the network.
AI can now design molecules that bind a target well. Binding is one event. What follows spreads through the cellular network: intended effects, compensation and unwanted effects. Design rarely sees that response, so it surfaces late in a program, as missing efficacy or a safety problem.
How we solve it
Three ideas behind BioGPU.
- 01
The disease as a system
We study the cellular network a target sits in, in the cells where the disease happens, and how that network responds when the target is hit. The unit of analysis is the network, not a single readout.
Patient-derived and disease-relevant cells · 150+ cellular systems
- 02
Resolve, don't guess
Hypotheses come from AI. BioGPU tests them with cellular network experiments and keeps only what the evidence supports. Each round of experiments resolves more of the network and points to the next one.
Reason · Run · Recognize · Resolve
- 03
A modality rebuilt for AI
We reinvented Visual Biology for AI. An agent designs the experiments, molecular-specific visualizers make each activity visible, a foundation model recognizes the images, and the results arrive as Visual Biology Tokens that AI can reason on.
1,000+ cellular activities · 2B+ images · one foundation model
Explore the BioGPU tech stack →
What it changes
The network becomes part of every design decision.
A resolved target network informs target, mechanism and safety decisions on its own.
Drug-design AI reads each compound's cellular network effect and uses it to improve the following design.