AI-enabled fungal biomanufacturing: from genome mining to the artificial intelligence virtual cell

Fungi are important chassis organisms for sustainable biomanufacturing owing to their strong secretion capacity, metabolic diversity, and adaptability to industrial processes. Driven by the rapid development of Artificial Intelligence (AI), computational approaches that enable machines to learn patterns from data and make predictions, together with omics technologies and synthetic biology, data- and model-driven strategies are increasingly reshaping fungal cell factory design and optimization. This review summarizes recent advances in AI-enabled fungal biomanufacturing, covering AI-assisted genome mining, enzyme and pathway analysis, genome editing, metabolic network modeling, and intelligent fermentation control. The review further highlights the emerging concept of the artificial intelligence virtual cell (AIVC), which integrates multi-omics data, mechanistic modeling, and machine learning-based approaches into a unified multiscale framework for simulating dynamic cellular behaviors. The technical foundations and potential applications of AIVC in fungal metabolic engineering, drug discovery, agriculture, and environmental biotechnology are reviewed. Finally, key challenges-data quality and standardization, cross-scale model integration and interpretability, and the gap between in silico prediction and experimental validation-are highlighted. This review provides a systematic perspective on the role of AI and virtual cell technologies in advancing next generation fungal biotechnology.