Abstract As the fundamental unit of life, cells coordinate biological activities through the interaction between microscopic molecular mechanisms and macroscopic tissue organization. Traditional research studies, experiments, and biochemical analyses, give rise to important insights, although they are restricted in spatiotemporal resolution and processing power, thereby precluding the understanding of dynamic cross-scale biological events . Breakthroughs in artificial intelligence (AI) have given birth to the AI virtual cell (AIVC) as a new way to do research. By integrating multi-omics data and mixing methods from multidisciplinary models, AIVC establishes a digital twin system to simulate cell functions and behaviors. AIVC still faces a number of pressing challenges that need to be addressed in its current development stage. In this review, we are proposing a unified definition and technical framework for AIVC and analyze in detail the cross-scale coupling mechanisms of the “gene–protein–pathway–cell” hierarchy. Furthermore, we decompose the technical construction framework of AIVC from cross-scale representation engineering, functional submodule design, and multi-component dynamic regulation mechanisms. Additionally, we summarize the existing models and datasets in the field to provide reference resources for researchers. Finally, we deeply discuss the challenges faced by AIVC, such as data heterogeneity and model interpretability, and aim to accelerate the research progress in the AIVC field while driving the life sciences to shift from observational analysis to a paradigm that integrates predictability and innovation. Despite being in the early stage, AIVC is a trending topic that has garnered widespread interest. This review aims to integrate existing models, datasets, and technical ideas to provide a unified framework for field development.
Jiang et al. (2026) studied this question.