Unraveling bioactive potential and production in Ganoderma lucidum through omics and machine learning modeling.
10.1016/j.chmed.2025.05.003
- Author:
Sonali KHANAL
1
;
Anand KUMAR
2
;
Pankaj KUMAR
3
;
Pratibha THAKUR
4
;
Atul M CHANDER
5
;
Rachna VERMA
6
;
Ashwani TAPWAL
4
;
Vinay CHAUHAN
3
;
Dinesh KUMAR
1
;
Deepak KUMAR
2
Author Information
1. School of Bioengineering and Food Technology, Shoolini University of Biotechnology and Management Sciences, Solan 173229, India.
2. Department of Pharmaceutical Chemistry, School of Pharmaceutical Sciences, Shoolini University, Solan 173229, India.
3. School of Advanced Chemical Sciences, Shoolini University, Solan 173229, India.
4. ICFRE-Himalayan Forest Research Institute, Shimla 171013, India.
5. Department of Biology, University of Mississippi, Oxford, MS 38655, USA.
6. School of Biological and Environmental Science, Shoolini University of Biotechnology and Management Sciences, Solan 173229, India.
- Publication Type:Review
- Keywords:
Ganoderma lucidum (Leyss. Ex Fr.) Karst.;
artificial intelligence;
bioactive compounds;
computational techniques;
herbal medicine;
machine learning technologies;
omics
- From:
Chinese Herbal Medicines
2025;17(3):414-427
- CountryChina
- Language:English
-
Abstract:
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.
