PerturbOS: An End-to-End Agentic Framework for Prediction-Guided Single-Cell Perturbation Discovery

Identifying interventions that reshape cellular behavior can reveal disease mechanisms and guide therapeutic research, but testing large genetic and chemical spaces with single-cell assays is costly. Existing perturbation datasets and predictive models offer a way to focus these experiments. However, their availability alone does not establish whether they cover the interventions and cellular context specified by a new discovery question. Using resources without assessing that fit can mislead intervention selection. To turn existing resources into discovery-specific guidance, we introduce PerturbOS, an end-to-end agentic framework that assesses resource suitability before prioritizing interventions. Given a natural-language question, PerturbOS selects and executes a suitable analysis workflow, then uses its quantitative output alongside biological knowledge to refine candidate interventions. In retrospective drug prioritization for increasing TOP2A expression in A549 cells, 49\% of PerturbOS's shortlisted drugs were among those with the largest measured increases, compared with 9\% for an agentic baseline without predictive feedback. PerturbOS also outperformed LLM baselines in selecting appropriate workflows across 37 biological scenarios. By improving both workflow selection and intervention prioritization, PerturbOS offers a foundation for more systematic, question-driven biological discovery guided by existing perturbation resources.