Ganoderma species are promising sources of bioactive natural products with substantial pharmacological potential and have long been valued in traditional Chinese medicine (TCM) for their diverse therapeutic properties. However, their complex multi-component, multi-target nature presents significant challenges in elucidating pharmacodynamic mechanisms and optimizing clinical applications. Recent advances in network pharmacology (NP) and artificial intelligence (AI) offer innovative strategies to address these challenges. NP integrates compound-target-pathway-disease networks, while AI enhances predictive modeling, target prioritization, and the analysis of large-scale pharmacological data. Together, these approaches facilitate mechanistic interpretation, rational formulation, and personalized use of Ganoderma-derived medicines. This review highlights recent progress in applying NP and AI to identify key bioactive constituents and therapeutic pathways of Ganoderma spp., while also addressing limitations related to data quality, standardization, and clinical translation. By emphasizing the synergy between traditional TCM theory and modern computational technologies, this integrative approach advances natural medicine research and holds promise for strengthening the scientific foundation and global acceptance of Ganoderma-based therapeutics.
Sharma et al. (Fri,) studied this question.