Artificial Intelligence and Computational Modeling in Natural Product Drug Discovery

The continued investigation of natural products remains an integral part of drug discovery today and offers a tremendous resource of diverse and potentially bioactive molecules. The use of computational methodologies has transformed and improved the ability to discover, optimize, and validate leads with therapeutic potential from natural sources. Recent advances in in silico methods allow for the rapid screening of natural product databases, better characterization of pharmacokinetic properties, and assessment of molecular interactions with high accuracy. Quantitative structure–activity relationship (QSAR) modeling plays a central role in establishing the association of molecular descriptors with biological activity, which increases the predictability and efficiency of lead optimization. Structure- and ligand-based virtual screening methodologies also expedite the identification of promising natural scaffolds for a variety of disease processes. This chapter presents an overview of computationally guided methodologies in phytochemical research and how they contribute to a better drug discovery process, reduced costs, and better success in bioactive molecule development. By bridging traditional ethnopharmacology with data-driven modeling and predictive methods, a new paradigm towards sustainable and efficient drug discovery is presented.