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Predicts how cells will respond to a drug or gene knockout, trained on over 100 million perturbed cells.
State is the Arc Institute's perturbation model, a transformer that predicts how a cell's gene expression shifts in response to a drug or genetic knockout, conditioned on both the perturbation and the cell's context. The 2025 bioRxiv preprint pairs a state-transition model trained on more than 100M perturbed cells, including the Tahoe-100M dataset, with a cell-embedding model trained on 167M human cells, and it serves as the reference entry for Arc's Virtual Cell Challenge. It is aimed at researchers who want to prioritise which perturbations to test experimentally, including in cancer cell lines. The training data come from cell lines, so transfer to tissues and patients in vivo is unproven, and benchmark work in the field has shown perturbation prediction is hard. For a newcomer: State tries to predict what a drug does to a cell before anyone runs the experiment.
Transformer predicting expression shifts conditioned on perturbation and cell context.
Query for this technology: (TITLE:"State" OR ABSTRACT:"State" OR TITLE:"Arc Institute perturbation model" OR ABSTRACT:"Arc Institute perturbation model") AND (cancer OR tumor OR tumour OR oncology OR carcinoma OR lymphoma OR leukemia OR leukaemia OR myeloma OR sarcoma OR melanoma OR glioma). Results are unfiltered search hits about State (Arc Institute perturbation model), not a curated reading list.
Shares Virtual cell roadmap: from bulk omics to a predictive model of a cancer cell, Drug discovery roadmap: screening in mice → maps of dependency → designing in silico, AI in oncology roadmap: pattern readers → foundation models → agents in the workflow, Single-cell & spatial profiling and the tags foundation-model, virtual-cell.
Shares Virtual cell roadmap: from bulk omics to a predictive model of a cancer cell, Drug discovery roadmap: screening in mice → maps of dependency → designing in silico, AI in oncology roadmap: pattern readers → foundation models → agents in the workflow, Single-cell & spatial profiling and the tags foundation-model, virtual-cell.
Shares Arc Institute, Virtual cell roadmap: from bulk omics to a predictive model of a cancer cell, Drug discovery roadmap: screening in mice → maps of dependency → designing in silico, AI in oncology roadmap: pattern readers → foundation models → agents in the workflow and the tag virtual-cell.
Shares Virtual cell roadmap: from bulk omics to a predictive model of a cancer cell, Single-cell & spatial profiling and the tags foundation-model, virtual-cell.
Shares Tahoe-100M, Virtual cell roadmap: from bulk omics to a predictive model of a cancer cell, Drug discovery roadmap: screening in mice → maps of dependency → designing in silico and the tag virtual-cell.
Shares Virtual cell roadmap: from bulk omics to a predictive model of a cancer cell and the tags foundation-model, virtual-cell.
Shares Virtual cell roadmap: from bulk omics to a predictive model of a cancer cell and the tags foundation-model, virtual-cell.
Shares Virtual cell roadmap: from bulk omics to a predictive model of a cancer cell and the tags foundation-model, virtual-cell.
Open-source projects that implement or serve this technology, from OnCo's own catalogue: licence and last activity as the repository reported them on the day of the fetch. Listing is not endorsement; check the licence before reuse and the validation before clinical use.
Arc's virtual cell model that predicts how cells respond to perturbations, trained with the Virtual Cell Atlas.