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Table 3.2 (Continued)
Small molecule Structure Pubchem CID
Rimegepant
F
N
N
N
O
O
H
H
N
F
N
N
H
O
N
51049968
Maraviroc
F
F
O
N
N
N
H
N
N
H
H
3002977
Tezacofactor
O
O
O
H
F
N
N
O
H
H
O
H
O
F
F
46199646
Tofacitinib
N
C
O
N
N
N
N
N
H
9926791
(Continued)
3 Novel Drug Targets for Small Molecule-based Drug Discovery64
Although small molecules are extensively used in the pharmaceutical industry, they will soon be
dominated by biologics. The only advantage of small molecules over biologics is it cost-effectiveness
and ease of manufacture. Biologics as therapeutic candidates fulfill the unmet clinical needs and are
considered an important drug of the future especially targeting the undruggable molecules [55]. In
summary, small molecules are effective drug candidates for modulating drug targets. However, there
is scope for further identification of druggable novel targets and there is also a need for modifying
small molecules to interact effectively with the target and bring about desired physiological changes.
Table 3.2 (Continued)
Small molecule Structure Pubchem CID
Brepocitinib
N
N
N
N
H
H
N
N
N
H
O
F
F
118878093
Azalomycin F4a
N
H H
H
H
H
O O
O
O
H
H
H
H
H
H
H
H
H
H
H
O
O
O
O
O
O
O
O
H
H
H
H
O
O
O
H
H
H
O
O
H
N
N
76963406
Risdiplam
F
O
N
N
H
N
N
H
H
N
F
118513932
References 65
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69
4.1 Introduction
In the present situation, computer-aided drug design is evolving worldwide, and researchers are
also keenly interested in it. Before the advancement in the CADD, the conventional drug design
was in the authentic methods that were utilized; however, using this concept, target identification,
inhibitor identification, and many more were time-consuming and less accurate. Several studies
were performed in this field to develop and design the crucial steps useful in the drug discovery
process [1]. Recently, to overcome the challenges of drug discovery, the CAAD came as a new hope
that scaled the drug design process with high accuracy [2]. It comprises target retrieval, structure
prediction, active site identification, ligand library preparation, and virtual screening for the novel
inhibitor identification toward the target structure. These compounds were optimized after the
crucial steps of validation. Interestingly, the virtual screening process reduces the costs of the novel
discovery. Based on the computational screening, the research easily finds the promising one and
utilizes it for further experimental evaluation, such as in vivo and in vitro. The computer-assisted
drug designs lie on several important aspects and among these tools and databases are two main
pillars that are handling and revolutionizing this CADD method in modern drug discovery [3–5].
The accurate methodology lies in different concepts, such as the selection of targets and whether
their structure is available, as it is essential for inhibitor identification. The computer-assisted drug
design mainly lies in the concept of structure-based drug discovery (SBDD) and ligand-based drug
discovery (LBDD). Several methodologies were employed utilizing the concept of SBDD and LBDD
in the present situation [6, 7]. In this chapter, the list of tools and databases that are available for
the SBDD and LBDD were briefly described. Moreover, the SBDD- and LDBB-based case studies
were also mentioned, which can help the readers understand the concept of computer-assisted
study related to the drug discovery process.
4.2 Structure-Based Drug Discovery Concept
As the number of emerging and remerging cases of pathogens increased daily [8], the requirement
for inhibitors through computer assistance also increased, along with identifying novel compounds
that had promising interaction with the pathogen target. SBDD is the most impactful approach for
4
Computer-assisted Methods and Tools for Structure-
and Ligand-based Drug Design
Saurav Kumar Mishra, Sneha Roy, Tabsum Chhetri, and John J. Georrge
Department of Bioinformatics, University of North Bengal, Darjeeling, West Bengal, India
70
the identification of therapeutics for pathogens. In this aspect, computational capacity is one of the
pillars that can help achieve accuracy in less time [6]. The initial steps are target identification and
ligand library retrieval from a different set of databases to the molecular docking examination and
the ADME (absorption, distribution, metabolism and excretion) properties of the identified com-
pound to reduce the number of compounds for the molecular dynamic simulation [9]. However,
based on crucial validation steps, the final one was experimentally validated before administration.
The basic concept behind the SBDD is illustrated in Figure 4.1. In the case of the SBDD, the target
structure is most essential, and if no information is available, the structure is modeled followed by
homology modeling, ab initio modeling, threading methods, and many more [3, 6]. Only after the
accurate structure of the target is available or retrieved, the concept of SBDD can be further processed.
4.2.1 Structure Generation of the Target
The starting phase of SBDD procedure consists of these steps. The 3D structure is created in this
stage; nevertheless, it may or may not be approachable in the PDB. Without discovery or solution,
the structure can be predicted through a sequence search of a similar nature. Three distinct
modeling methods, homology modeling, fold recognition, and ab initio modeling, are introduced.
Homology modeling, which implies 3D models and templates with a high degree of sequence
similarity, is the most prevalent technique in the interim. Many tools and resources are available [6].
These tools are including MODELER, SWISS-MODEL, PHYRE2, INTFOLD, RAPTORX, HHPRED,
ALPHA FOLD, SNPWEB, MODBASE, MOULDER, and MODLOOP. All these tools are for
homology modeling with different algorithms and specific uses. But if a similar sequence or
structure is unavailable in the comparative modeling database, fold recognition and ab initio or de
novo modeling are used. This method is based on the energy functions, and it is given a number of
possible confirmations, which depend on the thermodynamic stability, low energy states, and
native-like models. Some of the web servers, including ROBETTA, QUARK, and I-TASSER, are
available for initio-based prediction. The list of tools that are associated with it is shown in Table 4.1.
4.2.1.1 The Detailed Description of Each Tool
Modeller: Primarily on the basis of its alignment (templates), the program MODELLER can be
utilized to accomplish this by predicting the 3D structure of a particular protein sequence. In
Target
structure
Compound
library
Molecular docking
ADME analysis
MD simulation
Experimental evaluation
Retrieved from
different
database
Figure 4.1 Illustration of basic concepts
that are involved in the SBDD.
4.2 Structure-Based Drug Discovery Concept 71
addition to facilitating the recalculation of models in the event that the alignment is modified,
MODELLER also offers tools for integrating pre-existing information of the target, including
cross-linking constraints and secondary structures, and enables the calculation of models based
on multiple templates. Ab initio modeling of insertions is an additional capability provided by
MODELLER, which is often essential when annotating functions. Important to comparative
modeling, particularly in between 30% and 50% sequence identity, the program MODELLER is
also practical for loop modeling. Whereas the core regions remain relatively conserved and pre-
cisely aligned, the loops among the homologs vary in this range. MODELLER offers the requisite
instruments to accomplish loop modeling, which can be considered a miniature protein-folding
problem. In addition, the efficiency and practicality of protein structure models can be assessed
using MODELLER. It provides insights into the accuracy and applicability of the models by ena-
bling the docking of ligands into comparative models and the identification of putative binding
regions [10]. Swiss Model: Starting with primary sequences of partners, the SWISS-MODEL
server constructs three-dimensional models of protein complexes. Through homology modeling,
the server subsequently deduces the structure. Extrapolating experimental data from protein
structures that are evolutionarily related and function as templates for the target sequences con-
stitutes this procedure. Providing input data, selecting a template, constructing the model, and
estimating the grade of the model are all components of the default modeling workflow.
ProMod3 was additionally developed to facilitate flexible and rapid prototyping for forthcoming
modeling developments in SWISS-MODEL. In addition, a novel approach based on an original
description and evolutionary distance has been implemented in ProMod3. This enhancement
enables more accurate modeling of the stoichiometry and the overall structure of protein com-
plexes when simulating the quaternary structures of homo- and hetero-oligomers of proteins.
The accuracy and dependability of the 3D models produced by the SWISS-MODEL server have
Table 4.1 List of associated tools for the target structure generation.
Sl. no. Name URLs References
1 Modeller https://salilab.org/modeller/ [10]
2 Swiss model https://swissmodel.expasy.org/ [11]
3 Phyre2 http://www.sbg.bio.ic.ac.uk/~phyre2/html/page.cgi?id=index [12]
4 IntFOLD https://www.reading.ac.uk/bioinf/IntFOLD/ [13]
5 RaptorX http://raptorx6.uchicago.edu/ [14]
6 HHpred http://protevo.eb.tuebingen.mpg.de/hhpred
https://toolkit.tuebingen.mpg.de/tools/hhpred
[15]
7 Robetta https://robetta.bakerlab.org/ [16]
8 AlphaFold https://alphafold.ebi.ac.uk/ [17]
9 SNPWEB http://salilab.org/SNPWeb [18]
10 QUARK https://zhanggroup.org/ [19]
11 I-TASSER https://zhanggroup.org/I-TASSER/ [20]
12 MODBASE http://salilab.org/modbase [21]
13 MOULDER http://salilab.org/modweb [21]
14 MODLOOP http://salilab.org/modloop [21]
15 MODWEB http://salilab.org/modweb [21]
72
been enhanced due to the progress made with the ProMod3 modeling engine [11]. Phyre2: About
its methodologies and constraints, the Phyre2 protein structure prediction application is similar
to other servers designed for protein structure prediction. With an intuitive interface that enables
access to state-of-the-art bioinformatics techniques, Phyre2 is primarily distinguished by its
approach to methods. Phyre2 constructs three-dimensional models that protein sequence and
forecast ligand binding sites using sophisticated remote homology detection techniques. Model
quality assessment, alignment confidence, conflicts and rotamers analysis, pocket detection, and
mutational analysis are a few of the supplementary tools it provides for the comprehensive man-
agement and analysis of protein structure modeling projects. When considering constraints,
Phyre2 is comparable to other servers in its category. The detection of homology between a
sequence supplied by the user and a sequence whose structure is known constitutes one limita-
tion. The persistent challenge of the protein-folding problem is reflected in the fact that modeling
will be either unattainable or highly unreliable in the absence of homology detection. Phyre2 is a
highly regarded protein structure prediction tool that distinguishes itself through its intuitive
interface and extensive collection of protein structure analysis and prediction tools. Although
alternative robust structure prediction servers are also accessible, Phyre2 is the most widely uti-
lized and dependable for various modeling tasks. The prediction center’s website does not cur-
rently offer access to CASP11 data regarding the average quality of models. Non-bioinformatician
usability is the principal distinction between these servers and Phyre2 rather than accuracy.
Predicting the structural consequences of point mutations constitutes the second constraint,
which reapplies to all commonly employed methodologies. Prediction capabilities are present in
Phyre2 [12]. ntFOLD: The web resource known as the IntFOLD server facilitates the prediction of
protein structure and function. The server’s recent utilization in CASP experiments, user-friendly
interfaces, and benchmarked performance are emphasized. The server provides predictions
regarding natively unstructured regions, protein tertiary structures, structural domain bounda-
ries, and protein–ligand interactions. Graphical outputs of predicted models are now available;
they have been updated to enhance performance. Protein structural domain boundaries, natively
unstructured or disordered regions in proteins, protein–ligand interactions, and estimates of
model accuracy (EMA) are all included in the unified resource provided by the IntFOLD server,
enabling the automated prediction of protein tertiary structures. Access to an integrated suite
comprising six component methods is facilitated through the server. The server’s recent utiliza-
tion in CASP experiments, user-friendly interfaces, and benchmarked performance are empha-
sized. The server provides predictions regarding natively unstructured regions, protein tertiary
structures, structural domain boundaries, and protein–ligand interactions. Graphical outputs of
predicted models are now available; they have been updated to enhance performance. It has con-
sistently exhibited outstanding performance. According to independent official evaluation met-
rics, the IntFOLD server has positioned itself among the top-performing publicly accessible
servers in protein structure prediction [13]. RaptorX: A bioinformatics application known as the
RaptorX server, which predicts the structure of proteins based exclusively on their sequence or
sequence profile. In the case of proteins lacking near homologs in the Protein Data Bank (PDB)
or possessing sparse sequence profiles, it exhibits superior performance compared to alternative
servers. The server simultaneously predicts the solvent accessibility, disordered regions, and pro-
teins’ secondary structure, utilizing a deep learning model known as DeepCNF. The quality of the
training data for secondary structure and disorder prediction is a determinant of the accuracy of
the predictions. In addition to representing the intricate relationship between structure and
sequence through a deep hierarchical structure, this model also depicts the interdependence
among neighboring property labels. Processing time on the RaptorX server is contingent on