Computer-Aided Drug Design of Plant-Based Compounds

Introduction

It is known that plants have been used in the treatment of various diseases since ancient times. The therapeutic effects of various compounds found in plants are an invaluable source for many drugs and drug studies that are still under development. Non-nutritive phytocompounds found in plants show various properties such as antimicrobial, antidiabetic, anticancer and are used in the treatment and protection processes of diseases (Tabassum Khan & Gurav, 2018). It is known that secondary metabolites are important in the defense mechanism in plants and these metabolites can be used as drugs in humans. Plant-based polyphenol nutraceuticals are transformed in the intestine and provide beneficial effects as protective against bacteria, viruses, and protozoan parasites (Marín et al., 2015).

The US National Cancer Institute (NCI) provides services for preclinical research of compounds found in extracts of natural origin investigated for use in cancer studies (Mukherjee et al., 2001). For example, various drugs such as Paclitaxel, Docetaxel, Teniposide, and Topotecan are plant-derived anticancer drugs and have been approved by the FDA. It has been reported that plant-based compounds are used in drug and vaccine studies in COVID-19 studies that have entered our lives in recent years (Mahmood et al., 2020). Plant systems provide positive results in the production and distribution of vaccines due to the low cost of plant-based vaccines in vaccine studies and the fact that vaccines can be taken orally (Kumar et al., 2021; Sartaj et al., 2017). The alarming rise of antibiotic resistance in recent years has also led researchers to search for alternative plant-based therapeutics. Plant-based substances such as polyphenols, alkaloids, and tannins (polyphenols, alkaloids, and tannins) can be used as antimicrobials or as antibiotic resistance modifiers. It is aimed to overcome this problem with different phytochemicals (AlSheikh et al., 2020). Since experimental drug and vaccine studies take a long time and are costly, the biggest assistant of such studies is computer-aided drug design (CADD) studies. CADD is an important approach used in drug discovery and development. It is known to have the potential to almost halve the high cost of drug discovery and development (Xiang et al., 2012). CADD attempts to predict drug-receptor interactions using computational methods (Ebhohimen et al., 2020). This approach includes structure-based drug design and ligand-based drug design approaches (Surabhi & Singh, 2018). In structure-based drug design (SBDD), the structure of the target protein is known, whereas, in ligand-based drug design, the structure of the target protein is unknown (Imam & Gilani, 2017). In the SBDD approach, the ligand's interaction with the receptor and its affinity value is calculated after docking analysis. The best binding pose and affinity value of the ligand to the receptor are tried to be determined. In new drug discovery studies, ligand molecules with the best interaction and affinity values can be determined and drug development studies can be intensified on the appropriate ones. Molecular docking and molecular dynamics simulation analyzes are performed in molecular modeling studies in which such approaches are used. In the ligand structure-based drug design (LBDD) approach, the structural information of the ligand bound to the receptor is known. A pharmacophore model can be revealed so that these ligands with known structures can bind to the targeted site (Surabhi & Singh, 2018). Ligand-based drug design studies include a pharmacophore-based approach and quantitative structure-activity relationships (QSAR). In this approach, it is assumed that structurally similar compounds can have the same biological effect and interact with the target protein in the same type (Macalino, et al., 2015).