Computational Approaches in Natural Product Research: Advances, Challenges, and Future Directions

Document Type : Research Article

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Department of Chemistry, Faculty of Science, University of Qom, Qom, Iran

10.22091/jaem.2024.11133.1016

Abstract

Natural products offer immense potential for drug discovery, but their structural complexity and diverse bioactivities pose significant challenges. This review highlights the pivotal role of computational methods in addressing these challenges. We explore techniques for structural characterization, including DFT, molecular dynamics, and computational spectroscopy, which provide detailed insights into molecular properties and enable accurate structure elucidation. For activity prediction, molecular docking and QSAR modeling are discussed, emphasizing their utility in virtual screening and lead optimization. The integration of computational and experimental approaches is crucial for efficient drug discovery, with high-throughput virtual screening emerging as a powerful strategy. Despite advancements, challenges such as predicting complex structures and accurately estimating activity remain. Future directions include incorporating multi-omics data, exploring vast chemical spaces, and developing atomic-scale computational methods like QTAIM for a deeper understanding of molecular properties. By combining computational and experimental expertise, we can unlock the full potential of natural products for therapeutic and other applications.

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