**7. Combined computer-aided drug designing (CADD) and virtual screening**

Computer-aided drug discovery (CADD) can be described as utilising computer technology and software to elevate drug discovery efforts. It is also known as *in silico* drug discovery and is the application of computational methods to guide and systemise the process of drug discovery and its various stages. The two most commonly reported computational approaches to drug discovery are structure-based methods (primarily molecular docking studies) and ligand-based approaches such as ligandbased pharmacophore methods and three-dimensional quantitative structure–activity relationship (3D QSAR) models. Many therapeutically relevant malarial proteins have been identified and characterised, from receptors and transporters involved in membrane transport and signalling to enzymes used in biosynthesis. Many structurebased antimalarial drug development studies have been predicated on this diversity of protein targets [68].

Structure-based drug discovery (SBDD) searches for novel compounds that work against a specific target using information from the target's structure. The crystal structure of the drug target, preferably co-crystallised with a known ligand, can be used to extract the 3D structural information. It aids in the discovery of new ligands by identifying them from virtual chemical libraries or directing the design of novel compounds. The most commonly used SBDD approach is molecular docking, whereby computer software simulates ligand-target binding and predicts binding conformations and molecular interactions between ligands and target macromolecules. In the absence of a 3D crystal structure or a realistic homology model of the therapeutic target is unavailable, a ligand-based drug design (LBDD) technique can be useful. In research when the active inhibitors/ligands are known, ligand-based approaches are a potential alternative. Ligand-based pharmacophores are constructed using molecular descriptors of known active ligands. These models identify the ligand characteristics that are required for ligand-target compatibility, such as H-bond acceptors/donors, hydrophobicity, ionizable groups, and so on, making it easier to run comparison searches of huge compound datasets [68].

Drug development attempts for antiplasmodial drugs commonly use structurebased virtual screening techniques. Dahlgren et al. used structure-based screening of the Maybridge and ZINC drug-like databases to find four small-molecule inhibitors of *Plasmodium falciparum* macrophage migratory inhibitory factor (*Pf*MIF; PDB: 2WKF) [69]. In another study by Carrasco et al., 4 active compounds were identified against the chloroquine-resistant W2 strain *of P. falciparum.* For their study, they used the structure-based virtual screening a drug-like database included in the MOE package [70]. Similarly, a potential antiplasmodial against the *Plasmodium falciparum* FKBD35 protein was identified by Harikishore *et al*. by screening the database, ChemDiv, applying the same approaches. A structure-based pharmacophore model was developed 1st which was based on the *P. falciparum* FKBD35-FK506 X-ray crystal structure. As a result of pharmacophore-based screening of the ChemDiv library, a condensed library of 13,000 compounds was presented. ADME (absorption, distribution, metabolism, and excretion) filters were used to refine the library, yielding 2600 compounds. In docking investigations of the focused library into the active site of *P. falciparum* FKBP35, the docking software GOLD was used [71].

Lima *et al*. utilised a combi-QSAR approach, combining 2D- and 3D-QSAR models, in a virtual screening study of the ChemBridge database for a selection of new antimalarial virtual hits. This was followed by in vitro experimental tests of the potential *Pf*dUTPase inhibitors against chloroquine-sensitive and multidrug-resistant strains of *P. falciparum*, in an effort to identify new potential and selective antimalarial hits [72].

CADD approaches represent the best opportunity so far to enhance productivity pipelines not only in malaria, but also in various other diseases [73]. CADD is a very appealing venture for improved and efficient drug discovery for not only tropical infections but also for non-tropical diseases, due to the easy availability of many diverse chemical libraries in pharmaceutical companies, virtual discovery organisations, and academic institutions, as well as the increased genomic information that facilitates computational predictions [74–76].

#### **8. Conclusions**

Malaria is one of the deadliest infectious diseases in the world with no certified vaccine against malaria pathogens. Artemisinin and quinine are two most efficacious drugs that prevail. Unfortunately, ACT (Artemisinin-based combination therapies) resistant strain has been discovered in Southeast Asia. *P. falciparum* has developed drug resistance through single nucleotide polymorphism (SNP) in its genome. Traditional methods have been utilised to identify SNPs, however, with the advancement of NGS technologies, new possibilities were being opened up in the malarial research development as well as in the primary and applied field. As the new genomic data revealed a large number of processes, which can transform it into new therapeutic strategies. New quantitative analysis methods are continually being developed and tested for the most accurate analytical approach in the case of the *Plasmodium* genome. Studies in genomics and Systems Biology have made a huge contribution towards a better understanding of the *P. falciparum* parasite. Most significantly, they have built an impressive pipeline of novel therapeutic targets and several potent vaccine candidates. Along with the malarial parasite, the genomic study has also contributed to the genetic factor of humans that have a significant vulnerability and response against both malaria and its potent drug or vaccines. Beyond new drug and vaccine candidates, genomics has also contributed towards the drug mechanism of action. Thus, integration of both the genomic and system can lead to a better eradication of malaria strategies.

#### **Acknowledgements**

SR would like to acknowledge Dibrugarh University for providing computational facilities and financial support. The authors would also like to acknowledge Mr. Abhichandan Das for helping with the preparation of the chapter.

#### **Conflict of interest**

The authors declare no conflict of interest.

*Systems Biology Approaches towards Immunity against* Plasmodium *DOI: http://dx.doi.org/10.5772/intechopen.104614*
