Keywords

8.1 Introduction

Drug designing deals with the discovery and development of therapeutic molecules for a drug target. The drug is a small molecule that has potential to modulate the function of drug targets, such as a protein and sometimes nucleic acid tool, i.e., regulatory RNAs (Dersch et al. 2017). Drug design involves the design of molecules that are complementary in shape to the chosen drug target and modulate in the desired manner (Zauhar et al. 2003). Nowadays various drug designing approaches are in practice, broadly they can be classified into two types: (1) traditional methods: traditional methods involve trial and error method of testing for chemicals on cultured or animals cell, and observe the outcome of treatments, and (2) rational drug design: this approach is based on the hypothesis that modulation of a specific biological target which will be considered as drug targets, may have therapeutic value. In this approach, a potential therapeutic target is identified and purified. The purified protein is used to develop a screening assay. In rational drug design, 3D structure of the drug target should be available. The small bioactive searched by screening libraries of a drug or bioactive compound. This can also be performed by the screening assay, which also known as chemical or wet screening assay.

Nowadays computational methods are also in practice to screen compounds virtually and are well known as virtual screening (McInnes 2007). After library screening, the molecules are subjected to biological screening to test toxicity and those who show positive screening enter into the clinical trials where they try on human volunteers/patients to check pharmacokinetics (ADMET) of the drug. In the case of the successful completion of the clinical trials, a molecule passes to the approval agency and then finally hits the market (Fig. 8.1). This whole drug designing process is very time consuming and expensive, and at any stage of the process, a lead molecule can fail. Failure of leads at a later stage is responsible for the loss of millions of dollars for pharmaceutical companies (Hughes et al. 2011).

Fig. 8.1
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The flow of drug designing process (Katara 2017)

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To reduce the chance of later-failure and speed up the molecular screening process, computational approaches are in practice for the last one and a half decade. Nowadays, designing drug using computational approaches is well known as computer-aided drug designing (CADD). CADD involves various approaches such as QSAR, virtual screening, docking, etc. (Katsila et al. 2016). Computational approaches have speed up the process of drug discovery and have provided novel drug targets and lead structures (Katara 2013). The computational method can identify drug targets and leads against them, affinity and efficacy between them before clinical trials and saving enormous time and cost (Shekhar 2008; Katara 2017).

8.2 Drug Targets

The term drug target describes the native biomolecule in the human body whose function can be modulated by a drug molecule, which may have a therapeutic effect against the disease or some adverse effect. Mostly these drug targets are biological targets in nature. Various protein drug targets are currently utilized by available drugs, most of them belong to one of four major drug target protein classes (Table 8.1), in some cases, nucleic acids are also utilized by drugs as a target.

Table 8.1 Details of frequently used drug target protein classes
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8.3 Drug Target Identification

After identifying the biological nature and origin of a disease, identification of potential drug targets is the first step in the discovery of a drug. Drug target identification follows the hypothesis that the most promising targets are tightly linked to the disease of interest, and have an established function in the underlying pathology, which can be observed with high frequency in the disease-associated population. By definition, it is not necessary for potential drug targets to be involved in the disease-causing process, or responsible for a disease, but they must be disease-modifying. Currently, various strategies are in practice for drug target identification, which is either based on experimental approaches or computational approaches.

Experimental approaches are mainly based on comparative genomics (expression profiling) and supplemented with the phenotype and genetic association analysis. Mostly, all experimental approaches provide reliable results, and theoretically, they should be the first choice methods for target identifications. Even though experimental approaches are more precise, they are suffering from some practical limitations, i.e., relatively high costs and intensive scientific labor required for experimental profiling of the full target space (>20,000 proteins, nucleic acid) of chemical compounds and they often end with few drug targets in hand. Due to all these limitations, mostly scientists and pharmaceutical companies utilize the computational methods for first-line research and then use the experimental approaches for further validation and other purposes.

8.4 Computational Approaches for Drug Target Identification

The development of bioinformatics has come up with various bioinformatics resources, including the database, algorithm, and software, which push the CADD in every aspect of the drug designing process (Table 8.2). One of the most important contributions is computational drug target identification, as discussed earlier that identification of the drug target is a very crucial and most decisive step of the drug designing process. In this regard, for the last one and half decades, various scientific studies carried out with the aim of drug target identification with the help of bioinformatics resources and proposed various approaches for drug target identifications. These approaches easily handle and deal with a huge amount of genomics, transcriptomics, and proteomics data, and also process it efficiently, and at the end provide potential drug targets in a short period at a low cost.

Table 8.2 Bioinformatics resources for drug target identification and CADD
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Currently, several computational approaches are available which utilized different molecular information, i.e., gene and genome sequence, molecular interaction information and protein 3D structure. Most of these approaches are interlinked. Still, based on their concept, they have broadly classified into two types: (1) homology-based approaches and (2) network-based approaches. The major features which are checked for drug target prediction are listed in Table 8.3 (Kim et al. 2017).

Table 8.3 Important features utilized in drug target identifications
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8.5 Homology-Based Approaches

Homology-based approaches utilize sequence similarities among genes and proteins, further based on predicted homology, it takes the decision just like decision tree analysis. Mostly these methods consider the various level of homology test, which follows top-down direction. Each level of homology test scale down the data, starting from complete genes or proteome, and step by step either eliminate those which fitted in “inappropriate” or select only those which fitted in “appropriate.” Homology-based approaches always ended with countable potential drug targets (Fig. 8.2), and because of their scale down nature, these approaches are also known as subtractive (genomic or proteomic) approaches.

Fig. 8.2
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Schematic diagram of the standard flowchart for drug target identification using homology-based approach

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The term “inappropriate” and “appropriate” are conditional, and they are tested on various biological conditions that play a decisive role in target selection. The following are the major conditional tests that help to decide the further consideration of molecules for drug target identification.

8.5.1 Human Homologs

It is assumed that humans have various genes, and few of them are playing an indispensable biological role, considered as housekeeping genes. The use of human housekeeping genes or homologs of human housekeeping genes as a drug target can create lethal conditions and result in the death of human patients. To avoid such accidental use of the housekeeping gene as well as some important pathway-related gene as a drug target genes of the microbial pathogen are generally compared against the human, and those genes which show significant similarities with human housekeeping or crucial genes will be considered as “inappropriate” and mostly eliminate from rest of the process.

8.5.2 Human-Microbiome Homologs

The human body, especially, the gut has a lot of microbes that are already listed by the human microbiome project. Most of these microbes are involved in the biological process, which is beneficial for humans and thus considered beneficial microbes. Use of homologs from these beneficial microbes as a drug target can harm these bacteria, which can affect the related biological process in the human host, i.e., digestion, respiration process, etc., because of the above said reason, human-microbiome homologs are considered as “inappropriate” and eliminated from the further process.

8.5.3 Essentiality

Identification of drug targets against the microbial pathogen assumes that the essentiality of the target protein for pathogen-microbes is one of the advantageous and “appropriate” features. Without the function of essential proteins, microbial-pathogen will not able to survive. Various essential genes and proteins are identified by experimental approaches and enlisted in various databases. The database of essential genes (DEG) is one of the most active databases providing a collection of essential genes and protein sequences. Based on the above concept, those pathogenic genes/proteins which show homology with essential genes/proteins are considered as “appropriate” and include for the further process.

8.5.4 Virulence Factor Homologs

Those proteins whose role in virulence and pathogenicity is reported through the experiment are considered as virulence factors. Various such proteins are available, especially for microbes, and their molecular information is stored in various databases, i.e., virulence factor database (VFDB) and database of fungal virulence factors (DFVF). Genes/proteins of the pathogens that show homology with these virulence factors can be considered as “appropriate” and utilized as a potential drug target.

8.5.5 Drug Target Homologs

Information about known and explored drug/therapeutic targets is available, i.e., therapeutic target database (TTD). Homology mining with TTD is in practice, and those candidate molecules which show significant homology with these known targets are considered as “appropriate” and included for further exploration.

8.5.6 Cellular Location

The cellular location of the target protein is one of the very important features and plays a crucial role in target selection. In a homology-based approach, sequence-based gene ontology (GO) and annotation are in practice to look at the sub-cellular location along with the cellular component, biological process, and molecular function. Generally, those targets whose access is easy are preferable over others.

8.5.7 Role in the Biological Pathway

Biological pathways are responsible for the synthesis or metabolism of various bio-products. Few of these pathways are very important and unique, and they are solely responsible for their processes and products. The blockage of these pathways creates a scarcity of their products and finally reduces the chance of survival of the pathogen. Various pathway databases are available to conduct such checks. Current literature shows that the KEGG pathway is one of the richest and preferable pathway databases utilized for this purpose. Those pathways which are unique for pathogen are considered as appropriate pathways, and gene/proteins involved in them were considered for the further process. In contrarily those pathways which are also shared by human/host and their gene/proteins are “inappropriate” and excluded from further consideration.

It has been observed that homology-based approaches are very fast and almost cover the entire target space, and it only needs sequence information as input. Available reviews suggest that uses of homology-based approaches are very common for microbial disease and generally restricted with them only. Their use for other types of infection or disease is not in common practice.

8.5.8 Case Study: Subtractive Approach for Drug Target Identification

The subtractive approach is one of the very famous approaches that have been utilized for target identification against various pathogens. In 2011 Katara et al. presented a subtractive approach exploiting the knowledge of global gene expression along with sequence comparisons to predict the potential drug targets in Vibrio cholerae, cholera causing bacterial pathogen, efficiently. Their analysis was based on the available knowledge of 155 experimentally proved virulence genes (seed information) (Fig. 8.3). For target identification, they utilized co-expression based gene mining and multilevel subtractive approach. At the end, they reported 36 gene products as a drug target, to check the reliability of the predicted targets they also performed gene ontology through Blast2GO. They observed these targets for their involvement in a crucial biological process and their cellular location. They found all these 36 gene products as reliable targets and conclude them as potential drug targets.

Fig. 8.3
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Subtractive approach for drug target identification

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8.6 Network-Based Approaches

It examines the effects of drugs in the context of molecular networks (i.e., protein–protein interactions, gene networks, transcriptional regulatory networks, metabolic networks, and biochemical reaction networks). In molecular network models, molecules refer as nodes, and each edge corresponds to an interaction between two molecules, based on the direction and importance of interaction between nodes, sometimes edges also mention the direction and weight (Fig. 8.4). Drug target identification through the network is based on the fact that networks have many important nodes that are vulnerable and can be targeted in many ways. Most of the time, these nodes are very crucial, and sometimes essential for the whole network structure, inhibition of such nodes can reduce their efficiency and damage of these nodes can shut down the complete network. Network inhibition process follows one of the following two models: (1) partial inhibitions: Partial knockout of the interactions of the target nodes, and (2) complete inhibition: all interactions around a given target node are eliminated.

Fig. 8.4
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Various components of a standard network

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In the drug designing process, these target nodes can be considered as potential drug targets. Various molecular networks (Table 8.4), including protein-interaction networks, regulatory, metabolic, and signaling networks individually or in integrated form can be subjected to a similar analysis (Imoto et al. 2007; Sridhar et al. 2008; Kotlyar et al. 2012; Shin et al. 2017).

Table 8.4 Types of the biological network for drug target identification
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8.6.1 Centrality Based Drug Target

Network centrality can be used as a potential tool for network-based target identification. Network centrality can prioritize proteins based on the network centrality measures (i.e., degree, closeness betweenness). It can be used to characterize the importance of proteins in the biological system.

8.6.1.1 Hubs as Target

Real-world networks almost show a scale-free degree distribution, which means that in these networks, some nodes have a tremendous number of connections to other nodes (high degree), whereas most nodes have just a few. Here, nodes with a great number of connections than average called hubs. It assumes that the functionality of such scale-free networks heavily depends on these hubs, and if these hubs are selectively targeted, the information transfer through networks gets hindered and results in the collapse of the network (Pinto et al. 2014).

8.6.1.2 Betweenness Centrality Based Target

Hubs are the centers of local network topology, thus only provide the local picture of the network. Betweenness centrality is another approach that can be used to explain network centre, unlike, hub it provides central elements of the network in the global topology, thus, provide a global picture of network connections. Conceptually, betweenness is the number of times a node is in the shortest paths between two other nodes (Fig. 8.4), thus higher the betweenness means more importance of the node in quick network communication. Such higher betweenness centrality nodes can be utilized as a potential target against drugs (Melak and Gakkhar 2015).

8.6.1.3 Mesoscopic Centrality Based Target

Considering the advantage of both local and global centers of network topology for drug target identifications, the third class of centrality called mesoscopic centrality has also been reported. Mesoscopic centrality is neither fully based on local information (such as hubs) nor global information (such as betweenness centrality) on network structure. It mainly considers long-range connections between high degree nodes, which make a profound effect on small-world networks.

8.6.1.4 Weight-Based Drug Target

Recently, the weighted-directed network is also reported for drug target identification studies (Wang et al. 2013). The weighted-directed network is closer to the real, cellular scenario, where PPIs are characterized by their affinity and dominance (link weight) as well as direction (e.g., in form of signaling), as mentioned in Fig. 8.5. It has been assumed that the deletion of the links with the highest weighted centralities is often more disturbing to network behavior than the removal of the most central links in the similar un-weighted network topology.

Fig. 8.5
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Molecular network with a different type of connectivity between nodes (a) undirected (b) directed (c) weighted, and (d) weighted directed

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Utilization of the complex structural information of real-world networks to measure the centrality is not an easy task, and it requires more sophisticated methods to overcome these challenges. Bioinformatics provides various tools to support network construction, visualization, and network-based analysis, i.e., weight, centrality, interaction directions (Table 8.5).

Table 8.5 Tools supporting molecular network analysis for drug target identification
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8.6.2 Limitations

Drug target identification through the biological network is an empirical approach, which relies on available information on molecular networks. However, numbers of molecular interaction databases are available, and most of them suffer from uncertainties, false-positive entries, and the average probability of particular interaction along with nomenclature as well as interpretation problems. However, to overcome these issues, recently, PPI databases are linked with protein structure data, which provides more reliable and validated interactions. At the same time, scientists also propose some alternative, i.e., use of the curated database and low-resolution network to surmount the above-mentioned problems (De-Alarcón et al. 2002).

8.7 Properties of an Ideal Drug Target

Identification of potential drug targets is not the last step. Nowadays, through various computational approaches, a huge number of probable targets are reported against different diseases and are available in databases and literature (Katara et al. 2011). It is not a good idea to recommend them directly for testing, its recommendation that first, we check them for an ideal property (Table 8.6), and then for druggability. Only those targets which fulfill most of them are considered as an ideal drug target and recommended them for further validation and testing (Gashaw et al. 2011).

Table 8.6 Important properties to assess the ideal drug targets
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8.8 Druggability of Drug Target

In drug designing process, the potential of any target is defined by its druggability (affinity of the target to bind with drug-like molecules), thus the target must be druggable (Fauman et al. 2011). Biomolecules (i.e., protein, nucleic acid) with an activity that can be modulated by a drug are considered as a druggable target. These targets must have binding sites with typical structural and physicochemical properties that favor binding interaction with high affinity and specificity.

8.8.1 Importance of Druggability

Despite technological advancement in the drug designing process, most drug discovery projects fail because of the druggability problem. To avoid the failure of a drug discovery project, which is mostly very expensive, it is very important to understand the difficulties associated with a potential target. Druggability has become part of the target identification and validation process, more significantly in the case where targets do not belong to traditional classes (Finan et al. 2017).

8.9 Computational Methods for Druggability Assessment

To date, various targets are reported and documented through various methods, and few of them are already in practice (drugs are available against them), such targets are druggable. If no drug available for a target, then predict druggability is required. Various computational methods are available to evaluate the druggability of target protein, mainly rely on either sequence-based or 3D-structure based properties of proteins (Fauman et al. 2011).

8.9.1 Sequence-Based Methods

A protein is druggable if its other family members are known to be targeted by drugs. For such analysis, sequence alignment can be used to predict sequence similarity (homology) between probable target (query) proteins and database of known druggable targets (Finan et al. 2017). The sequence-based concept provides a significant approximation of druggability, but it suffers from the following limitations: (1) its predictions are limited to known drug target families, it does not attempt for those potential targets, which belong to the novel “un-drugged” protein family; and (2). It assumes that all members of the protein family are equally druggable, which is not true.

8.9.2 Structure-Based Methods

Structure-based methods rely on the availability of 3D structure information, thus only can apply to those proteins whose structures are available. Along with experimentally determined 3D structures, it also considers high-quality structure models through homology modeling. Several structure-based methods are available for the assessment of target druggability, irrespective of their different algorithms; all of them consist of the following three common components.

8.9.2.1 Identifying Cavities and Binding Pockets

Many computational methods and tools have been developed for binding pocket identification, which scans 3D surface and interior of the target protein for potential cavities (possess suitable properties for binding a ligand) that can act as binding pockets. These tools mainly tend to look for cavities with suitable size, shape, and composition to accommodate drug-like molecules.

Working of binding pockets detection methods depends on either energy-based or geometry-based detection algorithms (Nisius et al. 2012; Zheng et al. 2013). Energy-based detection predicts pockets by computing the interaction energy between atoms of protein and a probe molecule (Ghersi and Sanchez 2011). Geometry-based detection predicts the solvent accessible area that is embedded in the protein surface. Comparative studies suggest that both types of detection algorithms have good performance and advantages (Schmidtke et al. 2010). It has been observed that geometry-based detections are more suitable for large-scale pocket detection. Their inherent advantages, i.e., high speed and robustness against structural variations or missing atoms and residues in the input structures, provide the edge over an energy-based detection algorithm (Schmidtke et al. 2010). With the increasing availability of binding cavity information, recently, one new class of methods called information-based detection methods are developed. These methods utilize available cavity information from its neighbor and similar proteins whose binding cavities are known.

8.9.2.2 Druggability of Binding Pocket

This second step aims to calculate the physicochemical and geometric properties of the pocket to check whether these properties are complementary with the properties of drug-like molecules. Lipinski’s rule of five (RO5) connects the physicochemical properties of a drug with its pharmacokinetic properties (Lipinski 2000). It is a well-known fact that the physicochemical properties of the druggable pocket should be the mirror image of the physicochemical properties of the drug-like molecule itself. This analogy gave the concept of a druggable pocket. Therefore, the complementary properties of the pockets reflect the Lipinski’s rule of five of “drug-likeness” (H-bond donors >5, H-bond acceptors = 10, molecular weight > 500, and the Log P (CLog P) is >5).

The major features which define and affect the druggability of pockets are pocket descriptors. Characteristic features of a binding site play a very crucial role in druggability calculation, and the selection of those descriptors, which are crucial for binding drug-like molecules, needs to be described as accurate as possible. Observations suggest that none of the individual pocket descriptors is sufficient for druggability explanation, and a group of descriptors is required to describe and calculate pocket druggability. Both physiochemical and geometrical features play a crucial role as descriptors. Physiochemical descriptors and frequently used physiochemical pocket descriptors include size, shape, electrostatics, hydrogen bonding, hydrophobicity, polarity, amino acid composition, rigidity, and secondary structure (Halgren 2009; Krasowski et al. 2011). Geometrical descriptors: Along with physicochemical properties, geometrical properties, i.e., the shape and size of the binding pocket, play a crucial role in suitable interactions with a small molecule (Zheng et al. 2013). The following are the major geometrical features involved in pocket druggability measurement.

8.9.2.2.1 Position of the Atoms

It has been observed that the position of the atoms in pockets affects the contribution of an atom in interaction. Atoms located at the contact surface considerably give a major contribution in contact energy (hydrophobic interaction) than those who lie outside of the surface, i.e., within the bulk of the protein cavity.

8.9.2.2.2 Cavity Size

Large spherical cavities are more exposed to the solvent, thus not suitable for binding, especially with small drug molecules. Narrow (micro) cavity pockets are less exposed to the solvent and offer more van der Waals contact, thus they are more druggable. These micro-cavities are also defined as hot spots, which are characteristic of highly druggable targets.

8.9.2.3 Target Specificity Assessment

Drug target must be specific; structure similarity of drug target molecules with other unwanted molecules will create problems in the drug development process. Structural similarity of the binding sites could make the design of selective inhibitors difficult. During target selection, it is very important to assess the structural landscape of the primary binding sites of the target to confirm the druggability. Sequence and structural alignment based computational methods are available to perform specificity assessment.

8.9.2.3.1 Sequence Alignment Based Assessment

It is based on the sequence information of binding sites of the target protein. It assumes that when the degree of conversation between the two sequences is sufficiently high, then identical amino acids in the sequence will likely correspond to identical binding site structure.

8.9.2.3.2 Structure Alignment Based Assessment

These methods are based on either structural superposition or pharmacophore features. Structural superposition generally utilizes a 3D grid force field around the binding sites, which can be calculated using various types of energy terms, i.e., electrostatic, hydrophobic, and hydrogen bonding. In the grid approach, the field potentials can be calculated for each suspicious protein and are used for comparing their binding sites. The structural similarity between a pair of proteins can be studied by correlation functions of the various molecular interaction fields (MIFs) of the two grids or by utilizing the Fourier transformation of correlation functions or related approaches. Another approach consists of identifying pharmacophore features that generally summarized with the help of surface chemical features (SCF), including hydrophobic centers, H-bond donors and acceptors, positive and negative charges, and aromatic centers, etc. This SCF based on the consideration can be determined on the whole protein surface or a chosen cavity. Binding sites with the highest SCF matches show the highest similarity with the query binding site. Various computational tools are already available, which provide the facilities to evaluate binding site similarities and assess the specificity (Table 8.7). Almost all tools rely on the available entries at the protein structural database.

Table 8.7 Bioinformatics resources for druggability detection and evaluation
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8.9.3 Quantification of Druggability

Quantification of druggability could provide the best criteria for target selection, but till now, none of the standard explanation is available for this purpose. Each method has its measures for druggability, thus a druggability score of a specific target might vary. However, irrespective of an individual’s weaknesses and strengths, all major druggability measures can classify targets into druggable, non-druggable, medium druggable, and difficult-druggable.

8.9.4 Major Concern

8.9.4.1 Size of Training Sets

Most of the druggability assessment methods are based on the machine learning algorithm, thus highly dependent on available training sets (ChEMBL, BindingDB, PubChem, etc.) used to train them. The size and quality of the available datasets in databases directly affect the reliability and scope of the assessment methods.

8.9.4.2 Binding Site Flexibility

The identification of the binding cavity in a rigid target is based on the assumption that the cavity already exists. There are some proteins whose binding pockets do not exist in their native structure, and their active pockets behave like inducible allosteric sites, which only revealed after protein conformational changes. In such a case, it is very difficult to assess the binding pockets, and this situation is considered as a binding site “flexibility problem.” The presence of multiple X-ray conformers for a specific target can help us to handle binding site flexibility. Multiple conformers allow us to assess the relative variability of certain residues within the binding site pockets. Based on such relative variability information, it is possible to assess the plasticity of the binding site.

8.10 Target-Based Drug Discovery

As discussed, drug targets are the most crucial element of the drug designing process, and selection of the targets decides the fate of the drug designing process that it will succeed or get fail at a later stage. For several decades, pharmaceutical companies are successfully using well established one drug-one target approach for drug designing purposes. By realizing the scenario, the central dogma of the drug designing process has now shifted from one drug-one target to one drug-multi-target concept and considers multiple targets for a single drug.

8.10.1 Multi-Target Drug Designing

Computational approaches specifically those, which are based on system biology concepts are very crucial in the identification of multi-targets, thus play a major role in the success of the multi-target-based drug designing (Vasaikar et al. 2016). Multi-target-based drug designing approach is, to some extent, similar to single target-based drug designing, but it initiated with the set of targets multi-targets (Fig. 8.6). The following are the main steps of multi-target drug designing.

Fig. 8.6
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Target-based drug designing (a) single target-based drug designing, (b) multi-target-based drug designing and (c) major steps involve in multi-target-based drug designing

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8.10.1.1 Identification of a Set of Targets “Multi-Targets”

This is the most crucial step which decides the fate of the whole following process. System biology-based molecular networks are in practice to identify multi-targets.

8.10.1.2 Generation of Multi-Target Pharmacophore

Computational methods are available to design multi-target (structure) based pharmacophore, which utilizes combinatorial algorithms (Kumar et al. 2018; Ramsay et al. 2018). The most common steps in multi-target pharmacophore generation include (1) interaction profiling (MIFs) of all targets, (2) identification of common MIFs/features, and (3) multi-target specific and selective ensembles development.

8.10.1.3 Virtual Screening

Pharmacophore generation is followed by virtual screening of chemical libraries to find suitable compounds against multi-target pharmacophore.

8.10.1.4 Generation or Selection of Multi-Target Compound

Multi-target compounds are generated through the integration of pharmacophore of above-selected molecules (already known drugs or drug candidates).

8.10.1.5 Evaluation and Optimization of Multi-Target Specific Compound

Evaluation and optimization process mainly includes multi-target specific interaction assay (to avoid off-targeting), QSAR, and degree of modulation. Though multi-target drugs seem promising and designing of these compounds is not a straightforward task. It needs to deal with various crucial issues, i.e., right target-sets selection, balanced activity towards them, and excluding activity at off-target(s), while at the same time retaining drug-like properties (Hopkins 2008; Bottegoni et al. 2012). Available experimental methods are not enough to handle these issues, thus the feasibility of multi-target drugs profoundly depends on computational approaches and resources. Various databases are also there, i.e., DrugBank, STITCH, BindingDB ZINC, PubChem, KEGG DRUG, which provide required information about molecular pathways, 3D structure, chemical reactions, side effects, and known drug targets, thus help in the success of poly-pharmacologic drugs.

8.11 Summary

Now day’s computational biology becomes an indispensable tool for almost every aspect of biology and related fields, and drug designing is not an exception. CADD is now a mature field, and its success influenced by its first and pivotal step that is the identification of drug targets. This chapter provides an overview of various computational approaches available for drug target identification. It also discusses various bioinformatics resources, i.e., database, methods, and software, which can be handy for drug target identification purposes.