Biohub is building world models of biology to understand biological systems, predict responses to interventions, and design experiments. This requires large-scale, AI-ready data and models that capture biological structure and function. Biohub’s high-throughput data generation efforts, including the Virtual Biology Initiative, address this need. This workshop explores building, scaling, and applying models, and how open models, data, and tools enable scientific discovery.

We begin with the ESM suite of protein models. ESMC is a transformer encoder trained via masked language modeling on evolutionary-scale protein sequences. Sparse autoencoders decompose its representations into interpretable features aligned with binding sites, structural motifs, and other biological concepts. ESMFold2 builds on ESMC for atomic-level structure prediction and protein design. Applying these models across 6.8 billion sequences produced the ESM Atlas, a searchable resource of learned features and 1.1 billion predicted structures that reveals relationships sequence-based search may miss.

Extending this approach to cells requires capturing how cellular state and experimental context shape perturbation responses. Imaging and perturbation experiments yield rich measurements, but turning heterogeneous outputs into reliable training data requires substantial engineering. We examine scaling data ingestion and image processing, standardizing biological metadata, and efficiently serving large datasets for training and scientific exploration.
Through technical walkthroughs and hands-on demonstrations, participants will learn how biological models are designed, trained, and evaluated, and how complex biological data becomes AI-ready datasets. Biohub’s commitment to openly sharing models, datasets, and tools enables researchers to build on these resources and apply the workshop’s approaches to their research.