Virtual screening of phytochemicals for drug discovery

Virtual screening (VS) plays a very significant role in drug discovery for natural compounds. Experiments involving vast libraries using VS have already shown promising results for the putative discovery of bioactive compounds. It may be used to search databases for compounds that match either an existing pharmacophore model or a macromolecular target’s three-dimensional (3D) structure. Since research in this sector is cumbersome and costly, a more logical and cost-effective approach is required to solve these difficulties. This involves the use of high-throughput screening (HTS) methods in natural product-based drug discovery. This chapter provides some fundamental concepts, pre-requisites, and limits of VS in conjunction with previously conducted studies. It also focuses on artificial neural networks for their application in predicting biological activity along with software or AI tools used for VS and case studies related to hyphenated techniques. The integration of VS principles with well-established methodologies from conventional pharmacognosy in order to optimize their usefulness in drug discovery is discussed in this chapter to encourage researchers to take a step forward with this promising in silico tool. Computer-aided drug design (CADD) and other AI tools amalgamation for VS have a potential future for the discovery of natural moieties with therapeutic importance.