# Virtual cell roadmap: from bulk omics to a predictive model of a cancer cell

Source: https://onco.cc/roadmaps/virtual-cell/  
OnCo record `virtual-cell` (Roadmap). Data CC BY-NC 4.0, attribute "Data from OnCo (onco.cc)"; commercial use needs a licence.

## TL;DR

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.

## Summary

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.

## Fields

- Kind: Roadmap
- Last checked: 2026-09-08

## Sources

- Defining a Cancer Dependency Map: which genes each cancer cell line cannot live without (Cell 2017): https://doi.org/10.1016/j.cell.2017.06.010

## Connected records

- fronts: [AI & Computation](https://onco.cc/fronts/ai-computation/), [Drug Discovery Platforms](https://onco.cc/fronts/drug-discovery/)
- technologies: [Boltz-1 / Boltz-2 (MIT, open)](https://onco.cc/technologies/boltz/), [Cell2Sentence / C2S-Scale (Yale, Google)](https://onco.cc/technologies/c2s-scale/), [CRISPR functional genomics](https://onco.cc/technologies/crispr-screens/), [Functional (ex vivo) drug testing](https://onco.cc/technologies/functional-drug-testing/), [GEARS and perturbation prediction benchmarks](https://onco.cc/technologies/gears/), [Geneformer](https://onco.cc/technologies/geneformer/), [MRD / molecular residual disease testing](https://onco.cc/technologies/mrd-testing/), [Nicheformer (spatial single-cell)](https://onco.cc/technologies/nicheformer/), [Patient-derived organoids](https://onco.cc/technologies/organoids/), [scFoundation (BioMap)](https://onco.cc/technologies/scfoundation/), [scGPT](https://onco.cc/technologies/scgpt/), [Single-cell & spatial profiling](https://onco.cc/technologies/single-cell-spatial/), [State (Arc Institute perturbation model)](https://onco.cc/technologies/state-arc/), [TranscriptFormer and rBio (CZI virtual cell models)](https://onco.cc/technologies/transcriptformer/), [Universal Cell Embedding (UCE)](https://onco.cc/technologies/universal-cell-embedding/)
- companies: [Chan Zuckerberg Initiative (Biohub)](https://onco.cc/companies/chan-zuckerberg-initiative/), [Google DeepMind (and Google Research)](https://onco.cc/companies/google-deepmind/), [Vevo Therapeutics](https://onco.cc/companies/vevo-therapeutics/)
- institutions: [Arc Institute](https://onco.cc/institutions/arc-institute/), [Stanford Health Care / Stanford Cancer Institute](https://onco.cc/institutions/stanford/), [Yale School of Medicine / Yale Cancer Center](https://onco.cc/institutions/yale-school-of-medicine/)
- collections: [Arc Virtual Cell Atlas](https://onco.cc/collections/arc-virtual-cell-atlas/), [CZ CELLxGENE / Human Cell Atlas](https://onco.cc/collections/cellxgene-hca/), [DepMap (Cancer Dependency Map)](https://onco.cc/collections/depmap/), [Tahoe-100M](https://onco.cc/collections/tahoe-100m/), [TCGA / NCI Genomic Data Commons](https://onco.cc/collections/tcga-gdc/)
- ideas: [Patient-level multimodal foundation models for treatment selection](https://onco.cc/ideas/idea-multimodal-foundation-model/)
- roadmaps: [AI in oncology roadmap: pattern readers → foundation models → agents in the workflow](https://onco.cc/roadmaps/ai-oncology-roadmap/), [Drug discovery roadmap: screening in mice → maps of dependency → designing in silico](https://onco.cc/roadmaps/drug-discovery-roadmap/)

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JSON: https://onco.cc/api/v1/entities/virtual-cell.json