How It Works
Rather than sampling every candidate in a real environment, BioDreamer learns a latent simulator and plans multi-step interventions inside it.
Observe
Encode biological state into latent space
The domain-specific encoder (ESM-2 for proteins, SE(3)-GNN for molecules, scVI-VAE for cells) maps raw biological data such as sequences, coordinates, and gene expression into a compact latent representation z_t.
Dream
Simulate outcomes in imagination
The JEPA predictor estimates the next latent state directly. Given the current state z_t and a proposed action (mutation, force change, gene knockout), it produces ż_{t+1} entirely in latent space without ever reconstructing observations.
Evaluate
Score fitness and uncertainty
The reward head predicts target properties (ΔΔG, binding affinity, cell state distance) from the dreamed state. The uncertainty module estimates model confidence, and high uncertainty signals promising regions to explore.
Plan
Select optimal interventions via Active Inference
The policy rolls out multi-step trajectories inside the world model and selects actions that minimise expected free energy, balancing high fitness (exploitation) with reducing model uncertainty (exploration).
Act & Update
Validate and refine the model
Top-ranked candidates are evaluated by a real oracle (MD simulation, ESMFold, or wet-lab assay). The results update the world model, improving its predictions for the next round and closing the active learning loop.