AI-driven virtual cell models in preclinical research: technical pathways, validation mechanisms, and clinical translation potential

AI-driven virtual cell models show the potential to transform the paradigm of life sciences research by integrating multimodal omics data (e.g., single-cell transcriptomics and proteomics) with advanced algorithms such as deep generative models and graph neural networks to enable high-precision predictions of drug responses, gene perturbations, and disease progression. These models enable high-precision predictions of drug responses, gene perturbations, and disease progression. This review outlines the technical pathways and validation mechanisms of virtual cells, emphasizing a closed-loop workflow from computational evaluation to experimental verification using CRISPR assays and organoid platforms. The applications of virtual cells in personalized drug screening and disease modeling are highlighted, showcasing their potential to reduce animal testing and optimize therapy. However, challenges in regulatory acceptance, data privacy, and model interpretability remain. Global policy and standardization trends are driving clinical translation, and future advancements will involve cross-disciplinary integration and greater standardization to enhance the impact of virtual cells in precision medicine and drug discovery.