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The attempt to build a computer model of a cell good enough to predict what a drug or mutation will do before anyone runs the experiment.
A virtual cell would let researchers test thousands of drug ideas in silico and personalise treatment from a patient's own tumour profile. The field moved from static atlases to perturbation-trained models in five years; the honest status is that current models generalise poorly to unseen contexts and barely beat simple baselines on rigorous benchmarks, while data generation has begun to scale to the size the problem needs.
TCGA catalogues the genomes of 11,000 tumours; single-cell RNA-seq matures; the Human Cell Atlas begins. Models are statistical, per-dataset, and descriptive.
Perturb-seq and genome-wide CRISPR screens (DepMap) give causal training data; GEARS shows graph models can predict some unseen knockouts.
Geneformer, scGPT, UCE, scFoundation and others pretrain on tens of millions of cells. Benchmarks reveal that perturbation prediction often does not beat linear or mean baselines, forcing better evaluation.
Tahoe-100M (100M cells, 1,100 drugs, 50 cancer lines), Arc's Virtual Cell Atlas and Challenge, State trained on 100M+ perturbed cells, C2S-Scale's lab-validated hypothesis, CZI's cross-species models. The problem becomes one of held-out generalisation across cell contexts.
Models trained on perturbations in patient-derived organoids and spatial data (tumour niches, immune contexts) rather than cell lines alone; coupling with structure models for mechanism; prospective use to rank drug combinations for organoid confirmation.
A tumour's multi-omic profile seeds a patient-specific virtual cell population; treatment sequences are simulated before the first cycle; models are updated from ctDNA and imaging during care. Requires validation standards that do not yet exist.
Every era's records, trial outcomes and papers, and every watch item, as JSON.
Probability ranges are named estimates that the claim is borne out on roughly a five-year horizon. They are meant to be argued with: propose a revision with your name and reasoning via a pull request to src/data/confidence.ts.
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Shares Nicheformer (spatial single-cell), scFoundation (BioMap), CZ CELLxGENE / Human Cell Atlas.
Shares CZ CELLxGENE / Human Cell Atlas, DepMap (Cancer Dependency Map), CRISPR functional genomics, Functional (ex vivo) drug testing.
Shares Arc Institute, CZ CELLxGENE / Human Cell Atlas, Stanford Health Care / Stanford Cancer Institute, Single-cell & spatial profiling.
Shares CZ CELLxGENE / Human Cell Atlas, DepMap (Cancer Dependency Map), CRISPR functional genomics, Single-cell & spatial profiling.
Shares DepMap (Cancer Dependency Map), CRISPR functional genomics, Functional (ex vivo) drug testing, Patient-derived organoids.
Shares scFoundation (BioMap), CRISPR functional genomics, Functional (ex vivo) drug testing, Patient-derived organoids.
Shares CZ CELLxGENE / Human Cell Atlas, TCGA / NCI Genomic Data Commons, Single-cell & spatial profiling.
Shares Google DeepMind (and Google Research), Drug discovery roadmap: screening in mice → maps of dependency → designing in silico, AI in oncology roadmap: pattern readers → foundation models → agents in the workflow.