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Recent progress in artificial intelligence (AI) has given rise to AI virtual cells (AIVCs), which are digital twins of predictable or dynamic biological cells. This model can simulate, predict and replicate the behavior of real cells in digital software. Extracellular vesicles (EVs) are nanoscale phospholipid bilayer structures released by cells and are important for intercellular communication. To fully leverage digital models in EVs research, we propose the interdisciplinary concept of AI virtual EVs (AIVEVs). This review systematically outlines the construction of AIVEVs through both knowledge-driven (white-box) and data-driven (black-box) modeling paradigms, integrating multi-omics data to simulate EVs biogenesis, cargo sorting, and intercellular communication. Moreover, we highlight how AIVCs drive models to predict the composition of AIVEVs, analyze cell communication behavior, construct diagnostic atlases of pathological virtual cells, and enhance the ability to trace vesicle origins. Furthermore, we also present a closed-loop workflow from in silico prediction to experimental validation and project the developmental trajectory of AIVEVs toward clinical translation. We firmly believe that AIVEVs can accelerate the development of EVs-based disease diagnosis and treatment, thereby opening a new era of intercellular communication research.
Liu et al. (Mon,) studied this question.