Ganoderma lucidum, a medicinal mushroom renowned for its production of a diverse array of compounds, accounts for the pharmacological effects including anti-inflammatory, antioxidant, immunomodulatory, and anticancer characteristics. Thus, it is recognized as a valuable species of interest in the pharmaceutical and nutraceutical industries due to its important medicinal properties. Recent advances in omics technologies such as genomes, transcriptomics, proteomics, and metabolomics have considerably increased our understanding of the bioactives in G. lucidum. This review explores the application of molecular breeding techniques to enhance both the yield and quality of G. lucidum across the food, pharmaceutical, and industrial sectors. The article discusses the current state of research on the use of contemporary omics technologies which studies and highlights future research directions that may increase the production of bioactive compounds for their therapeutic potential. Additionally, predictive methods with computational studies have recently emerged as effective tools for investigating bioactive constituents in G. lucidum, providing an organized and cost-effective strategy for understanding their bioactivity, interactions, and possible therapeutic uses. Omics and machine learning techniques can be applied to identify the candidates for pharmaceutical applications and to enhance the production of bioactive compounds in G. lucidum. The quantification and production of the bioactive compounds can be streamlined by the integrating computational study of bioactive compounds with non-destructive predictive machine learning models of the same. Synergistically, these techniques have the potential to be a promising approach for the future prediction of the bioactive constituents, without compromising the integrity of the fungal organism.
关键词:
人工智能;生物活性化合物;计算技术;灵芝;草药;机器学习技术;组学
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This research work has been funded by DST-SERB, Govt. of India under the CRG project vide sanction order number CRG/2021/001815 and this article has been produced with the financial support of the European Union under the REFRESH–Research Excellence for region Sustainability and High-tech Industries (No. CZ.10.03.01/00/22_003/0000048) via the operational Programme Just Transition. The work in this article was partially carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (No. 80NM0018D0004).
