Work

Artificial intelligence virtual extracellular vesicles (AIVEVs)

Year: 2026

Type: article

Abstract: 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. • Introduction of AIVEVs as a Novel Digital Twin Framework: This review pioneers the concept of AIVEVs, integrating AIVCs with EVs biology to create a predictive digital model for simulating EVs biogenesis, composition, and intercellular communication. • Comprehensive Simulation of EV-Mediated Communication: AIVEVs enable the dynamic modeling of donor–EVs–recipient interactions, allowing for the simulation of disease-specific communication networks, prediction of therapeutic EVs candidates, and identification of diagnostic biomarkers in silico . • Bridging Computational Predictions with Experimental Validation: The proposed framework combines multi-omics data integration, AI-driven prediction, and experimental feedback, offering a closed-loop research paradigm that enhances the accuracy, scalability, and clinical translatability of EVs-based studies.

Source: Bioactive Materials

DOI: 10.1016/j.bioactmat.2025.12.050

Authors: Han Liu, Shiyu Li, Jian Wang, Jiacan Su

Institutions: Shanghai University, Jinan University, National University of Singapore, Shanghai Jiao Tong University, XinHua Hospital

Language: English

Topic: Extracellular vesicles in disease

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,200