Importance of In silico Tools in Anticancer Drug Discovery from Nature

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Currently, cancer has become one of the most dreadful diseases threatening human health. Natural plant sources play a vital role in the development of several anti-cancer drugs such as vincristine, vinblastine, vinorelbine, docetaxel, paclitaxel, camptothecin, etoposide, teniposide, etc. Various chemotherapies fail due to adverse reactions, target specificity, and drug resistance of some types of drugs. Researchers are attentive to developing drugs that overcome the problems stated above by using natural compounds that may affect multiple targets with reduced adverse effects and that are effective against several cancer types. The development of a new drug is a highly complex, expensive, and time-consuming endeavour. In the traditional drug discovery process, ending with a new medicine ready for the market can take up to 15 years and cost more than one billion dollars. Fortunately, this situation has changed with the arrival of novel approaches recently. Many new technologies and methodologies have been developed to increase the efficiency of the drug discovery process, and computational methodologies utilise the existing data to generate knowledge that affords valuable understanding for addressing current complications and guiding the further research and development of new naturally derived drugs. Consequently, the application of in silico techniques and optimization algorithms in drug discovery ventures can provide versatile solutions to understand the molecular-level interactions of chemical constituents and identify the hits. Lead optimization techniques such as ligand-based or structure-based drug design are widely used in many discovery efforts. In this chapter, we first introduce the concepts of CADD, in silico tools, etc. we then describe how this virtual screening has been successfully applied. Furthermore, we review the concept of natural product anticancer therapies and present some of the most representative examples of molecules identified through this method.<br>