VCDesign: Finite-Budget Intervention Design for Virtual Cells

Virtual-cell models are typically judged by how well they predict the effects of perturbations or recover the interventions associated with a cellular transition. Yet experimental planning poses a different question: given a desired cellular state, which interventions should be tested when only a limited number of experiments can be performed? Here we introduce VCDesign, which, to our knowledge, is the first framework to formulate target-conditioned cellular intervention design as finite-budget ranking over a variable candidate set. Given source and target states, a candidate library and an experimental budget, VCDesign prioritizes the interventions to test. We also introduce a decision-aligned evaluation framework that measures the quality of the selected candidates using independently measured held-out outcomes. For candidates lacking historical perturbation responses, we develop VCDesign-CED, which uses existing perturbation measurements and biological knowledge to infer candidate effects and combine them with direct candidate scoring. Across four CRISPRi Perturb-seq contexts, VCDesign-CED improves the prioritization of response-unseen candidates, increasing pooled budgeted utility at 20 selections by 0.049 and recovering 1.11 additional top-5% candidates on average. When candidate responses are already available in other contexts, direct retrieval performs better, and routing candidates according to response availability further improves mixed-library selection. Controlled analyses further show that transferable perturbation evidence and candidate knowledge contribute more to design performance than simply adding training contexts or increasing effect-model complexity, while prediction accuracy does not reliably rank nearby models by their utility for intervention design. Together, these results establish a task and evaluation framework for using virtual-cell models to make experimental decisions under finite budgets.