Core Viewpoint - The article introduces the concept of "AI Virtual Metabolism" (AIVM), establishing a new paradigm for metabolic network reconstruction driven by AI and multi-omics data, aiming to advance the understanding of biology and metabolic engineering [2][7][21]. Group 1: AI Virtual Metabolism Framework - AIVM combines retro-synthetic reasoning from chemistry with biological constraints to enhance the feasibility of metabolic pathway predictions [3][8]. - The framework utilizes large language models trained on multi-omics data to generate hierarchical representations of cellular functions, guided by the central dogma of molecular biology [8][21]. - AIVM incorporates various biological constraints, such as enzyme specificity and thermodynamic feasibility, to ensure realistic pathway predictions [8][9]. Group 2: Metabolic Pathway Reconstruction - The reconstruction of cellular metabolic pathways is crucial for understanding energy production, biosynthesis of macromolecules, and cellular signaling [5][12]. - Traditional biochemical methods face challenges due to limited experimental data and the complexity of metabolic networks, making complete reconstruction difficult [5][6]. - AI advancements offer a promising paradigm shift, enabling predictions of metabolic pathways without complete mechanistic understanding [6][21]. Group 3: Applications and Future Directions - The AIVM framework is envisioned to facilitate the engineering of microbial chassis for sustainable production of high-value compounds and therapeutic interventions [9][11]. - A hypothetical scenario illustrates the reconstruction of the artemisinic acid pathway in yeast, demonstrating the potential of AIVM to generate testable design hypotheses [11][12]. - Future applications may include optimizing microbial platforms and enhancing the supply of precursors for downstream processes [9][11]. Group 4: Challenges and Considerations - Key challenges include the need for large-scale, high-quality datasets to enhance biological realism and the complexity of extending predictions to eukaryotic organisms [22]. - The integration of computational modeling with experimental workflows is essential to address these challenges and establish biological credibility [22][21]. - The vision of an AI-enabled Virtual Cell is becoming a reality, providing powerful tools to accelerate optimization in metabolic engineering [22][21].