More AIxBio from Arc Institute

Evo 2 is one piece of a broader effort at Arc to build the full stack of interconnected AI and biology. On the AI side, these resources range from the Evo series of models to learn the language of DNA, to virtual cell models that predict how cells respond to perturbations, to agentic mining of public data, to tools for training models and applying their predictions and designs.

Learn more about:

Arc's Virtual Cell Initiative — Our Institute-wide effort taking a full-stack approach to generate training data and build virtual cell models.

State — Arc's first virtual cell model, trained on large perturbational datasets to predict how genetic, chemical, and environmental changes shift gene expression across cell types.

Stack — A single-cell foundation model that uses in-context learning to predict cellular responses to perturbations never directly measured.

scBaseCount — AI agents that find, clean, and uniformly process single-cell data for model training, part of Arc's Virtual Cell Atlas.

The Virtual Cell Challenge — An annual competition launched in 2025 to evaluate and improve virtual cell models across the field.

MULTI-evolve — An AI-guided protein engineering framework from the Hsu and Konermann labs that accelerates iterative design-test-build cycles.

CodonFM — A family of open-source AI models developed with NVIDIA that reveal the grammar underlying codon choice.

ProPer-seq — A cost-efficient method for linking perturbations to transcriptional phenotypes.

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