Tahoe-100M is an atlas of 100 million single-cell transcriptomes, capturing how 50 cancer cell lines respond to ∼1,100 drug-dose treatments. By pairing single-cell and molecular phenotypes at scale, the resource links drug mechanisms to cellular responses and provides an openly available substrate for training predictive models of cell behavior.

Modeling perturbation effects across large single-cell populations requires flexibility to capture heterogeneity. By training over sets of cells in a shared embedding space, State outperforms baselines at generalizing effects to new contexts. Cell-Eval, the framework used for this comparison, provides a comprehensive benchmark for future models.

Nature Biotechnology, Published online: 20 March 2026; doi:10.1038/s41587-026-03064-w

This Review discusses the promises and pitfalls of biological AI algorithms and presents a vision for generalist biological artificial intelligence, in which models can perform diverse tasks across biological domains.