A Survey of AI Virtual Cells: Representation Learning, Perturbation Prediction, and Intervention Design

Predicting cellular responses to genetic and chemical interventions supports disease research and therapeutic discovery. Experimental screening is costly, limiting coverage of interventions and cellular contexts. This survey reviews artificial intelligence virtual cells (AIVC) through the progression from state representation learning to perturbation prediction and intervention design. We describe how state representations support response modeling and how learned responses guide intervention selection across previously untested interventions and diverse cellular contexts. We summarize data resources, representative model families, evaluation strategies, and multimodal, temporal, and spatial extensions, then discuss generalization and experimental feedback. A curated resource collection is available at https://github.com/ttruan2426-dot/Awesome-Single-Cell-Perturbation-Prediction.