Potential Nutraceuticals Based on In Silico Studies

The conventional drug discovery approach is time-consuming, costly, and carries significant risks. Recently, in silico methods have gained considerable attention due to their advantages in cost efficiency, reduced time requirements, and minimized labor. Many new drugs having much higher potential have been developed successfully using a computational approach. In silico approaches have transformed nutraceutical development and optimization, offering expedient and economical methods for identifying bioactive compounds. Derived from natural sources, nutraceuticals show substantial promise in health promotion and disease prevention, particularly for cardiovascular disorders and cancer. This chapter explores the application of in silico methodologies, including molecular docking, Quantitative Structure-Activity Relationship (QSAR) modeling, and molecular dynamics simulations in nutraceutical discovery. It emphasizes the effectiveness of computational methods in predicting bioavailability, therapeutic potential, and safety profiles of nutraceuticals prior to experimental validation. Case studies featuring compounds such as curcumin and resveratrol demonstrate successful implementations of these technologies. This chapter also addresses a list of databases for nutraceutical screening and research. The discussion also addresses inherent limitations and challenges, notably the oversimplification of complex biological systems and computational resource constraints. Looking ahead, the chapter outlines future research trajectories, highlighting the increasing significance of personalized nutraceuticals. These advancements, bolstered by progress in artificial intelligence and quantum computing, carry profound implications for public health strategies and outcomes. Thus, computational tools have become crucial in nutraceutical research for designing and optimizing bioactive compounds with enhanced therapeutic potential.