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.

Consolidated bioprocessing (CBP) is a promising strategy for low-cost malic acid production from plant biomass. In this study, we report a comprehensive redox engineering strategy to redirect reducing equivalents towards the reductive tricarboxylic acid (rTCA) pathway for enhanced malic acid production directly from cellulose in the thermophilic fungus Myceliophthora thermophila JG207. Firstly, deletion of key NADH-consuming pathways, including glycerol-3-phosphate dehydrogenase (gpdh) and mitochondrial external NADH dehydrogenases (nde1 and nde2), resulted in a significant increase in the NADH/NAD⁺ ratio and a corresponding 17% increase in malic acid titre. Subsequent removal of alternative oxidase (AOX) further heightened the NADH pool, although it induced oxidative stress and slowed growth. To alleviate redox imbalance, we overexpressed the native transhydrogenase SthA, which effectively converted NADPH to NADH and improved malate production by an additional 6%. Fine-tuning NAD⁺ kinase (UTR1) expression further optimised cofactor homoeostasis, bringing about the highest malate titre of 89.2 g/L, a 38.9% improvement over the starting strain JG207. Finally, overexpression of lytic polysaccharide monooxygenase gene (lpmo) and cellobiose dehydrogenase gene (cdh) enhanced cellulose degradation, shortening the fermentation cycle by two days on Avicel and crushed corncob. This work not only advances the fundamental understanding of redox metabolism in filamentous fungi but also provides a proof-of-concept for sustainable organic acid production from inexpensive lignocellulosic feedstocks.