Abstract:
The discovery and identification of novel metabolites is crucial for advances in pharmaceutical and health applications. However, this is a time-consuming process and necessitates the integration of in silico approaches for quick, enhanced efficiency and accuracy. This chapter focuses on the in silico prediction of secondary metabolites, particularly those derived from endophytic fungi, leveraging advanced computational techniques. Endophytic fungi, known for their symbiotic relationship with host plants, are a well-known source of bioactive secondary metabolites with diverse biological activities. By utilizing large datasets and sophisticated algorithms, the structure, function, and potential biological activity of these fungal secondary metabolites can be predicted with high precision. The integration of genomic, transcriptomic, and metabolomic data aids to build predictive models that identify novel secondary metabolites from various endophytic fungi. Significant improvements in prediction accuracy, enables the identification of compounds with potential therapeutic and pharmaceutical applications. In silico methods revolutionize the discovery and development of secondary metabolites, offering a faster, cost-effective, and efficient alternative to traditional experimental approaches. Additionally, it highlights the untapped potential of endophytic fungi as a rich source of novel bioactive compounds.
