{"entity":{"id":"scgpt","kind":"technology","name":"scGPT","aka":[],"tldr":"A GPT-style model for single-cell data that predicts cell types, perturbation responses, and gene networks.","summary":"scGPT is a generative transformer for single-cell data that tokenises genes and bins expression values, borrowing the GPT recipe from language modelling. The Nature Methods 2024 paper pretrained it on 33M cells and showed fine-tuning for cell-type annotation, batch integration, perturbation response prediction and gene regulatory network inference. It is used by computational biologists as a general-purpose starting point that can be adapted to a new dataset with limited labels, including tumour microenvironment atlases. The main caveat is that its perturbation prediction is only modestly above simple baselines, a finding echoed across single-cell foundation models, so its strengths are in annotation and integration rather than in forecasting drug effects. For a newcomer: scGPT is a GPT-style model for cells that is good at naming cell types and merging datasets.","status":"emerging","asOf":"2026-09-08","links":[{"label":"Nature Methods 2024","url":"https://doi.org/10.1038/s41592-024-02201-0"}],"tags":["foundation-model","virtual-cell"],"related":["virtual-cell"],"cancers":[],"sections":["ai-computation","drug-discovery"],"technologies":["single-cell-spatial"],"targets":[],"drugs":[],"companies":[],"institutions":[],"pathways":[],"terms":[],"trials":[],"people":[],"bottlenecks":[],"keyPapers":["paper-cui-nat-methods"],"journals":[],"dependsOn":[],"notes":[],"principle":"scGPT is a generative transformer over gene tokens and expression bins.","strengths":["General-purpose fine-tuning"],"limitations":["Perturbation prediction only modestly above baselines"],"since":2024},"route":"/technologies/scgpt/","neighbours":{"roadmap":[{"id":"ai-oncology-roadmap","kind":"roadmap","name":"AI in oncology roadmap: pattern readers → foundation models → agents in the workflow","route":"/roadmaps/ai-oncology-roadmap/"},{"id":"drug-discovery-roadmap","kind":"roadmap","name":"Drug discovery roadmap: screening in mice → maps of dependency → designing in silico","route":"/roadmaps/drug-discovery-roadmap/"},{"id":"virtual-cell","kind":"roadmap","name":"Virtual cell roadmap: from bulk omics to a predictive model of a cancer cell","route":"/roadmaps/virtual-cell/"}],"section":[{"id":"ai-computation","kind":"section","name":"AI & Computation","route":"/fronts/ai-computation/"},{"id":"drug-discovery","kind":"section","name":"Drug Discovery Platforms","route":"/fronts/drug-discovery/"}],"technology":[{"id":"single-cell-spatial","kind":"technology","name":"Single-cell & spatial profiling","route":"/technologies/single-cell-spatial/"}],"paper":[{"id":"paper-cui-nat-methods","kind":"paper","name":"scGPT: toward building a foundation model for single-cell multi-omics using generative AI","route":"/key-papers/paper-cui-nat-methods/"}]}}