Khanal Sonali, Kumar Anand, Kumar Pankaj, Thakur Pratibha, Chander Atul M, Verma Rachna, Tapwal Ashwani, Chauhan Vinay, Kumar Dinesh, Kumar Deepak
School of Bioengineering and Food Technology, Shoolini University of Biotechnology and Management Sciences, Solan 173229, India.
Department of Pharmaceutical Chemistry, School of Pharmaceutical Sciences, Shoolini University, Solan 173229, India.
Chin Herb Med. 2025 May 19;17(3):414-427. doi: 10.1016/j.chmed.2025.05.003. eCollection 2025 Jul.
, 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 . This review explores the application of molecular breeding techniques to enhance both the yield and quality of 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 , 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 . 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.
作为一种以产生多种化合物而闻名的药用蘑菇，具有抗炎、抗氧化、免疫调节和抗癌等药理作用。因此，由于其重要的药用特性，它被认为是制药和营养保健品行业中有价值的物种。基因组学、转录组学、蛋白质组学和代谢组学等组学技术的最新进展大大增加了我们对该蘑菇生物活性成分的了解。本综述探讨了分子育种技术在食品、制药和工业领域提高该蘑菇产量和质量方面的应用。文章讨论了利用当代组学技术的研究现状，这些技术进行研究并突出了未来的研究方向，这些方向可能会增加生物活性化合物的产量以发挥其治疗潜力。此外，最近出现的计算研究预测方法已成为研究该蘑菇生物活性成分的有效工具，为理解其生物活性、相互作用和可能的治疗用途提供了一种有条理且经济高效的策略。组学和机器学习技术可用于识别药物应用的候选物，并提高该蘑菇中生物活性化合物的产量。通过将生物活性化合物的计算研究与相同的非破坏性预测机器学习模型相结合，可以简化生物活性化合物的定量和生产。协同作用下，这些技术有可能成为未来预测生物活性成分的一种有前景的方法，同时不损害真菌生物体的完整性。
