Computational Methods Used in Phytocompound-Based Drug Discovery

Phytocompounds are gaining popularity, due to lesser toxicity, greater bioavailability and high chemodiversity. Phytocompounds are evolving as new leads for the development of a novel drug. Phytocompounds exhibit a wide range of biological properties, and are being used as antioxidants, immunomodulatory, antimicrobial, cardiovascular, and anticancer drugs. However, their identification is still relatively limited. The major hurdle in the discovery of an efficient phytocompound is a complete dependence on time-consuming in vitro and in vivo screening systems. Alternatively, the computational drug discovery approach of using an online database and bioinformatics tools would be a cost-effective and time-saving option. Recent advancements in computational and structural biology have attributed the three-dimensional (3D) structure to many natural drug-like compounds and disease-related target macromolecules, and have been stored into renowned databases like Protein Data Bank (RCSB-PDB), DrugBank, and ZINC. The drug discovery filed is swiftly advancing with bioinformatics applications, and the availability of digitalized molecular data is paving new avenues in Computer-aided drug discovery (CADD) area. Virtual library screening (VLS), a widely recognized technique due to it being cost-effective, time-saving, and less laborious, evaluates drug candidates using computational tools, such as AutoDock Vina, GOLD, and Glide. This chapter provides comprehensive information about the available biological databases and bioinformatics tools that are useful for the analysis of plant-derived bioactive compounds and their molecular interactions in diseases. Applications of advanced bioinformatics tools and methods for designing, optimization, and high-throughput screening of phytocompounds are detailed. In addition, it emphasizes the advantages, limitations, challenges, and future perspectives of the computational approaches in analyzing phytocompound interactions.

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