Features
The full stack for model-based biological design, from JEPA world model training to Active Inference policy learning and candidate ranking.
World Model Planning
Dreams multi-step intervention strategies in latent space before committing to expensive real-world experiments.
JEPA Architecture
Joint-Embedding Predictive Architecture encodes observations into latent states and predicts transitions without a decoder during planning.
Active Inference
Grounded in the Free Energy Principle. Balances exploitation (fitness) and exploration (epistemic uncertainty) automatically via expected free energy minimisation.
Multi-Scale Biology
Operates across atomic (MolDreamer), protein (ProteinDreamer), and cellular (CellDreamer) scales with a unified codebase.
Multiple Backends
Choose from Latent Diffusion JEPA (default) or Energy-Based JEPA with SIGReg regularisation. Both share the same encoder-predictor interface.
Modular Design
Swap any component (encoder, JEPA predictor, reward head, policy) independently. Built with PyTorch and clean abstractions.
Multi-Objective Optimisation
Optimise for stability, binding affinity, catalytic activity, and expression simultaneously with Pareto-based ranking.
Active Learning Loop
Dream, propose, evaluate, update. Iteratively refines the world model with each round of experimental feedback.
Hugging Face Hub
Push and pull pre-trained models via Hugging Face Hub with safetensors serialisation and auto-generated model cards.
Uncertainty Quantification
Ensemble, evidential, and MC-dropout methods provide calibrated uncertainty estimates to guide exploration.
FastAPI Backend
Production-ready REST API with Redis job queue, GPU worker pool, and real-time progress streaming.
Reproducible Science
YAML configs, W&B tracking, deterministic seeding, and versioned model checkpoints for fully reproducible experiments.