**3.2 Virtual screening**

#### *3.2.1 Molecular docking*

In modern drug design and discovery, molecular docking is one of the foremost strategies to find the best molecules for a specific target. It assists to predict the interaction of protein and a ligand at the bond stage. Molecular docking is used to calculate the scoring function by sampling algorithms shown in **Figure 3** [54]. Furthermore, it is reasonable, time demanding, and effective more than any conventional technology. The computer-based drug designing method predicts the function of any individual protein of interest that conducts a very comprehensive approach [55]. It has various applications among them protein-ligand docking is one of them. It is primarily designed to predict the binding of small drug molecules from medicinal herbs or plants, invertebrates, and so on to target proteins [56]. The number of diseases is caused by the harmful receptors; hence, docking is designed to inhibit or induce the target protein. However, the discovery of drugs in the traditional way is very expensive because of the need to search a large volume of compounds to select against a particular protein for the proper binding interaction and targeting diseases [57]. Therefore, computerized docking can screen virtually thousands of compounds in a short time by experimental high-throughput screening and confirmed the ligand and receptor for stable binding. It also provides the knowledge-based scoring functions that interact with atom pairs between protein and ligand complexes along with three-dimensional structure. There are several purposes of molecular drug design [58]. Among them, three are main purposes such as predicting the active ligand, binding affinities, and identifying new ligand. Based on molecular

#### **Figure 3.**

*Molecular docking-based screening approaches of small molecules against a specific target.*

docking, it has numerous applications such as documenting the lowest free energy structure for the receptor-ligand complex, estimating the differential binding of a ligand of two separate macromolecular receptors, the geometry of the specific ligand-receptor complex, lead generation and optimization for future drug candidate, and so on [59].

#### *3.2.2 Molecular dynamic simulation*

Molecular dynamics (MD) simulation is a computer-based simulation technique that mainly analyzed the physical motion of atoms and molecules across a specific conformational space [60]. MD simulation is the unique way to drug discovery in the present era that led to work in a wet lab after computer simulation. The MD simulation ensures the detail of the dynamic properties of selected protein and plays a key role in the modeling and characterization of a protein [61]. The MD simulation also helps to determine the stability of a ligand to the active site of the targeted macromolecules. Moreover, MD simulation not only provides the information of the ligand optimization process at the qualitative level but also estimates accurately in ligand binding affinities. The tiny, microscopic event (protein-ligand interaction and molecular motion) occurs with a micro- or nano-second time scale that is not possible to determine without the technique [62]. Finally, dynamic simulation is a faster, reasonable, widely accessible, and perfect method for drug design *in silico* techniques. Moreover, it provides information of atomic position with respect to time. It usually explains the atomic and molecular properties of the protein, drug-target interaction, solvation of compound, and conformational changes that a receptor expresses in various conditions [63]. It works on the base of Newtonian mechanics (NM), which is related to the motion of large particles. The force fields are an important factor for MD simulation, which are mainly a set of the potential energy function for employing the relation between structure and potential energy. The particles are coordinated by the system of mathematical expression [64]. The computerized technique (force and energy) is used for building blocks that combined bonded and non-bonded interaction. Force acting on each atom was counted by using the force field of molecular machines that were developed through the four principal such as Born-Oppenheimer method, bond length, and bond angle, and potential energy of the surface molecule and atom type [65].

#### *3.2.3 Quantum mechanics*

Quantum mechanics has been leading a valuable method for drug discovery through characterizing the structure, dynamics, and energies of protein-ligand interaction [66]. In the medicinal industry and academic research, it is an inevitable part of drug design that fixes the problem by calculating chemical reactivity and helps to optimize structure [67]. The new drug candidate has been chosen by molecular mechanics (MM) but the quantum mechanics (QM)-based approach provides accurate accuracy and efficiency in the complexity of protein-ligand interactions [68]. Furthermore, it does not provide only the estimation of being affinities, determining ligand energies and bioactive conformations, refinement of molecular geometries but also added scoring docked ligand poses, describing molecular similarity, structureactivity, relationship analysis, and ADMET prediction in the activities of drug design [69]. QM is getting popular day by day in drug discovery because of the improving power or speed of the computer that led to advanced QM algorithm progress as well as a new application to address the shortcoming [70]. The popular method of fragment molecular orbital (FMO) offers a wide range of solutions where there are combined

*Immunoinformatics and Computer-Aided Drug Design as New Approaches against Emerging… DOI: http://dx.doi.org/10.5772/intechopen.101367*

accuracy, speed, and the ability to characterize important interaction such as strength in kcal/mol as well as hydrophobic, electrostatic, and so on. In summary, FMO analyzed the length such as polarization, desolation, and interaction with the illustrated for a water dimer and protein-ligand complex [71].

#### *3.2.4 Pharmacokinetic properties*

Pharmacokinetics is the unique part of the drug design where the time course study is done through the mathematical characterization for absorption of drug, distribution, metabolism as well as excretion (ADME) [72]. Therefore, before the clinical trial, it is the advanced procedure to select a drug for the development and decision-maker by AMDE test [73]. As a result, it is safe and effective for a specific patient by decreasing toxicity and increasing efficacy in drug therapy. However, the central compartment of the human body was interconnected by a series of compartments reversibly on the base of pharmacokinetic [74]. Therefore, drugs enter each compartment and are distributed homogenously to display consistent kinetics. Hence, it is separated by a mathematical model where AMDE can differ in each compartment and help to predict drug metabolisms or actions [75]. Moreover, the central compartment received drug and entry on the body called absorption. The drug is distributed to the peripheral compartment after absorbing into the body and beginning the metabolic processes. Finally, the excretion of the drug from the body is in three ways such as hydrophilic molecules by the renal system, hydrophobic molecules by the biliary system, and volatile substances by the pulmonary system [76].

#### **4. Immunoinformatic**

#### **4.1 Peptide design**

In the drug discovery, peptides are not new, but it makes a choice drug candidate in the challenging situation with the collaboration of immunoinformatics. Immunoinformatics can be developed as natural endogenous scaffolds with familiar biological activities for solving challenging medical problems by it distinguishing characteristics such as the ability to act firstly, more specific, the minimum range of toxicity, and so on [77]. Approximately 55 therapeutic peptides have been approved for clinical trial through the regulatory agency [78]. Currently, many researchers have been working to solve various diseases through antimicrobial peptide design. The target of antimicrobial peptides has accelerated through the deep generative models and molecular dynamics. By utilizing the immunoinformatic approach, the researcher has developed 20 novel peptides that were validated through deep learning and high-throughput molecular simulation [79]. The peptides are not only used in broad-spectrum potency but also lead to multi-drug resistant strain and low toxicity. Immunoinformatic approaches help to observe peptide activity, selectivity, toxicity, ease of synthesis stability, etc. During the present situation of COVID-19, immunoinformatics help to identify peptide candidates from diverse viral sequences as shown in **Figure 4**, which can be utilized for vaccine design [80]. It is identified and designed from the motifs and subsequently a peptide library. As a result, strong binding affinities of peptides had happened with the main protease of SARS-CoC-2 as well as maintained stability and physiological condition observed by molecular dynamic simulation [81].

#### **4.2 Vaccine design**

Vaccine design is a very powerful tool for saving human life in the world broadly. The advancement of technology has been trigger to develop new strategies by the target and the structures of antibodies, hitting conserved epitopes and controlling the usage of antigenic variation [82]. It designed immunogens for provoking foreign responses because of no single or best solution of immune drugs design till now [83]. However, artificial intelligence and system biology together make an opportunity for avoiding inefficiencies and failures that help to classical vaccine development pipeline [84]. *In silico* vaccine design process has been selecting well fragment of virus protein [85], thereby finally leading to a final vaccine as shown in **Figure 4** [86].

However, in the vaccine design process, DeepVacPerd is an efficient tool that predicts the best vaccine subunit candidates with 30 subunit candidates from the various protein sequences within a second by replacing the predicted and selected with deep neural network architecture [87]. Therefore, it has become a promising of higher efficiencies for the vaccine design and test process. Furthermore, systems biology developed various tools by analyzing large data set through the complex modeling interaction between the individual interaction [88]. The omics disciplines such as genomics, proteomics, metabolisms, and so on produced a simulation of the immune response, identified response-specific signature, and assessed their predictive value through the basilar idea with the integration of high-throughput data [89]. Therefore, several programs and consortiums have performed by developing novel analytic tools that may integrate the information from omics.

#### **4.3 Future perspective of vaccine**

Personalized vaccine candidates are developed against a specific "targeted" to maintain an optimized outcome. Failure of the vaccine candidates can increase

#### **Figure 4.**

*Immunoinformatics aided identification of peptide candidates and procedure for designing multi-epitope vaccine by utilizing the peptide.*

*Immunoinformatics and Computer-Aided Drug Design as New Approaches against Emerging… DOI: http://dx.doi.org/10.5772/intechopen.101367*

immunogenicity subsequently reactogenicity and adverse effects [90]. Therefore, the individual level, the gender level, the racial/ethnic level, and the subpopulation level should be considered during the personalized vaccine development process. Haplotype and polymorphism are mainly focused on the individual level selection as they can retard the formation of a protective immune response, where the gender level can determine the antibody tiger against a particular vaccine to males and females [91]. On the other hand, different human races or ethnic groups can show higher or lower immune responses to a specific vaccine candidate. Additionally, different interactions between host environmental, genetic, and some other factors may be influencing the vaccine immune responses; therefore, the subpopulation level should be also considered during vaccine design that can trigger an optimum immune response against a specific disease [92].
