The resources and modeling advances supporting AI virtual cells' value for mechanism-of-action analysis, efficacy, safety, resistance, and combination studies are reviewed, and evidence requirements for pharmacological use are defined.
Abstract
Drug discovery relies on cellular assays to determine whether a candidate produces a desired response, reveals its mechanism, or causes toxicity, but only a small fraction of compound-dose-time-context combinations can be measured experimentally. Recent perturbation atlases, single-cell and imaging technologies, and intervention-conditioned AI models now make prediction of unmeasured cellular responses a testable objective. AI virtual cells could therefore complement conventional discovery by prioritizing compounds, contexts, and follow-up experiments rather than replacing laboratory assays. Here, we review the resources and modeling advances supporting this capability, assess their value for mechanism-of-action analysis, efficacy, safety, resistance, and combination studies, and define evidence requirements for pharmacological use. Data coverage makes oncology, including immuno-oncology, plausible early proving grounds, with safety assessment as a crosscutting use case; broader deployment requires perturbation-specific, mechanistic, and decision-level validation.
A paradigm shift toward autonomous scientific agents capable of causal reasoning and end-to-end experimental guidance is highlighted, and persistent challenges are discussed, including data bias, limited interpretability, and in silico-to-wet lab translation.
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