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12.4 Applications of homology modeling in CADD
The primary application of homology modeling lies in the predic tion of 3D protein
structures based on the knowledge of the structures of homologous proteins. Homol-
ogy modeling is a versatile tool in CADD that aids in the rational design of drugs, un-
derstanding molecular interactions, and optimizing the drug development processes.
It is particularly valuable when experimental protein structures are scarce or difficult
to obtain. The following are some major applications of homology modeling in CADD.
– Structure prediction: Homology modeling is used to predict the 3D structures of
target proteins, especially when experimental structures are unavailable. These
predicted structures serve as a basis for understanding protein–ligand interactions.
– Rational drug design: Once the 3D structure of the target protein is predicted, it
can be used to design small molecules (ligands) that interact with the protein’s
active site. This is crucial for the rational design of drugs and drug candidates.
– Virtual screening: Predicted protein structures can be employed in virtual
screening experiments to identify potential drug candidates from large com-
pound libraries. This helps in narrowing down the search for lead compounds.
– Binding mode analysis: Homology models enable the visualization and analysis
of the binding modes between ligands and target proteins. This information is
vital for optimizing the binding affinity and specificity of drug candidates.
– Mutagenesis studies: Homology modeling can be used to predict the effects of
mutations in the target protein. This is valuable for understanding how genetic
variations may impact drug responses and personalized medicine.
– Protein–protein interaction studies: Homology modeling can also be applied to
predict the structures of protein complexes and protein–protein interactions.
This is important in understanding various signaling pathways and designing
drugs that target protein–protein interactions.
– Drug resistance mechanisms: Homology modeling helps in studying drug resis-
tance mechanisms. By modeling the mutated target protein structures, research-
ers can design new drugs that can overcome resistance.
– Enzyme mechanism elucidation: It is used to elucidate the catalytic mechanisms
of enzymes, which is i mpor tant for designing enzyme inhibitors and studying
metabolic pathways.
– Biosimilars development: In the development of biosimilars, homology model-
ing is used to ensure that the 3D structure of the biosimilar closely matches that
of the original biological drug, ensuring similar efficacy and safety profiles.
– Protein function prediction: Homology modeling can provide insights into the
function of uncharacterized proteins by comparing them to homologous proteins
with known functions.
12.4 Applications of homology modeling in CADD 289
12.5 Advantages of homology modeling
Homology modeling is a versatile and valuable tool in computer-aided drug design,
allowing researchers to make informed decisions and streamlining the drug develop-
ment process. Homology modeling offers some key advantages over the conventional
drug designing process, which are depicted below.
– Rapid structure prediction: Homology modeling allows for the quick prediction
of protein structures, saving time in drug development.
– Cost-effective: It is a cost-effective alternative to experimental X-ray crystallogra-
phy or NMR spectroscopy for structure determination.
– Facilitates rational drug design: Knowing the 3D structure of a target protein
enables rational drug design, optimizing the development process.
– Structural insights: It provides insight into the active site and binding pocket of
a target protein, which is crucial for designing effective drugs.
– Polypharmacology: It aids in the identification of potential off-target effects,
helping to design safer drugs with fewer side effects.
– Virtual screening: Homology models can be used in virtual screening to identify
potential drug candidates for a specific target.
– Prediction of protein mutations: It can predict the effects of mutations in a pro-
tein, aiding in the understanding of drug resistance and personalized medicine.
– Advantageous for membrane proteins: It is particularly useful for predicting the
structures of membrane proteins, which are challenging to study experimentally.
– High accuracy: With high sequence similarity between the target and template,
homology models can be remarkably accurate in predicting the overall structure.
– Enhanced drug optimization: It can guide structure-based drug optimization by
identifying potential binding sites and interactions for lead compounds.
12.6 Limitations of homology modeling
Homology modeling, while a valuable tool in computer-aided drug design, has several
limitations that researchers should be aware of. It is important to consider these limi-
tations when using homology modeling in computer-aided drug design and validat e
the resulting models through experimental data and other computational methods,
when possible. The following are some of the major limitations of homology modeling
in CADD:
– Sequence similarity requirement: Homology modeling relies on the availability
of a sufficiently similar template protein structure. If a suitable template is not
available, modeling becomes challenging or unreliable.
– Inaccurat e modeling for highly divergent sequences: Homology modeling is
less effective when modeling proteins with low sequence similarity to known
structures, leading to potentially inaccurate models.
290 Chapter 12 Homology modeling
– Incorrect loop modeling: Loops, especially long and flexible ones, can be chal-
lenging to model accurately, often resulting in inaccuracies in the final structure.
– Domain and multidomain proteins: Homology modeling struggles with proteins
that consist of multiple domains, as the arrangement and orientation of these do-
mains may not be predicted accurately.
– Posttranslational modifications and nonstandard residues: Homology model-
ing does not account for posttranslational modifications or nonstandard amino
acids, limiting its applicability in certain cases.
– Incomplete or incorrect template structures: If the selected template structure
contains errors, gaps, or missing regions, these inaccuracies can be propagated to
the homology model.
– Limited applicability for membrane proteins: Membrane proteins have distinct
structural features, such as hydrophobic transmembrane domains, which are chal-
lenging to model accurately using standard homology modeling techniques.
– Dynamic or disordered regions: Proteins with highly dynamic or disordered re-
gions may not be accurately represented in the homology model, leading to po-
tential inaccuracies.
– Ligand interactions: Homology modeling does not predict ligand interactions or
binding sites accurately, making it less suitable for drug design without additional
information.
– Challenging cases: There are some proteins for which homology modeling may
not be suitable due to unique structural features, and alternative modeling tech-
niques or experimental methods are required.
References
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292 Chapter 12 Homology modeling
Chapter 13
Pharmacophore mapping in computer-aided
drug designing (CADD)
In the realm of drug design, pharmacophore mapping is the blueprint of hope for a healthier future.
– Dr. Jane Pharmaceutica
13.1 Introduction
Pharmacophore mapping is a pivotal technique within the realm of CADD. It serves as
a virtual compass guiding researchers through the complex landscape of molecular
interactions, with the ultimate goal of discovering novel therapeutic agents [1]. At its
core, pharmacophore mapping seeks to identify and define the essential stru ctural
and chemical features that a molecule must possess to interact with a specific biologi-
cal target, such as a receptor or enzyme. The process begins by analyzing the 3D struc-
tures of known ligands that bind to the target of interest. These ligands’ common
features, including key functional groups and spatial arrangements, are extracted and
abstracted into a pharmacophore model. This model represents the essential “phar-
macophoric” elements required for effective binding [2, 3].
Pharmacophore mapping is invaluable for various aspects of drug discovery. It
aids in virtual screening, allowing researchers to sift through vast chemical libraries
to identify compounds that fit the pharmacophore, thereby expediting the lead discov-
ery process [4]. Furthermore, it is a critical tool for optimizing drug candidates by
guiding structural modifications to enhance their affinity and specificity for the target
[5]. Pharmacophore mapping is a cornerstone of CADD, enabling the rational design
of drugs with increased precision and efficiency. It accelerates the identif ication of
promising leads and aids in the development of innovative therapies, contributing sig-
nificantly to the field of pharmaceutical research and in the quest for improved
healthcare solutions [6, 7].
The concept of pharmacophore emerged in the last decade of the nineteenth cen-
tury. It took almost one century for it to emerge as a computational tool in the CADD,
when in the early 1990s, many commercial programs were introduced for the devel-
opment of 3D pharmacophore models [8, 9].
To gain insight and to develop a complete knowledge of the pharmacophore con-
cept, it is of utmost importance to dive into the historical perspective of this concept.
https://doi.org/10.1515/9783111434858-013
13.2 Historical perspective of the pharmacophore concept
The credit for coining the term “pharmacophore” is given to Paul Ehlrich (father of che-
motherapy) [10]. Ironically, he never used the term “pharmacophore” in any of his pub-
lications regarding his research related to this concept. It was Marshall who used the
term pharmacophore [11] in his publication On the physiological action of the alkaloids
of jaborandi leaves, based on Paul Ehrlich’s side-chain theory published in 1897 [10].
TheroleofPaulEhlrichinthedevelopmentofthepharmacophoreconceptis
very interesting and his attempts laid the foundation of modern day concepts of phar-
macophore modeling in CADD. In his research, he introduced the terms “haptophore”
and “toxophore.” As per the interpretation from Ehlrich’s w ork, he defined hapto-
phores as the chemical groups that are responsible for binding the drug to the side
chains (in the present scenario receptors). As he was working on the development of
the chemotherapeutic agents, he defined toxophores as the chemical groups that
show their toxic effect for alleviating the growth of microorganisms.
Some of the researchers argue that he did not use the term “pharmacophore,” so he
could not be given the credit for coining the term. Though he did not use this term in his
publication, he was well aware of the same, as officially he used this term “pharmaco-
rum” that later became “pharmacophore” in his Nobel Prize acceptance speech of 1908.
This can be related well to the “particle nature of the wave” that was defined by Albert
Einstein in 1906. He termed the particles of light as “quanta” in 1906 and got the Nobel
Prizeforthesamein1921butamoreacceptabletermforthesameis“photon,” which
was coined by Gilbert Lewin in 1926. This is an unarguable fact that credit for the concept
of photon is attributed to Albert Einstein, although he never used this term. On the same
analogy, credit for the concept of the term “pharmacophore” is attributed to Paul Ehlrich.
AtthetimeofPaulEhlrich,weakchemicalinteractionssuchashydrogenbonding
and Van der Waals interactions were not discovered, and hence the concept of hapto-
phore and toxophore was limited to defining the role of the strong chemical interac-
tions, such as covalent bonds, on the biological activity. With the passage of time and
with more relevant research in the field of chemistry, the concept of pharmacophore
gained popularity. It has been envisaged that for a molecule, it is not mandatory to
have separate haptophore and toxophore; instead, a single group can perform both
tasks, leading to the common term “pharmacophore.” Thus, the modern definition of
pharmacophore that came into the picture was given by Shueler in 1960 as the pattern
of forces in the space that are required for the desired pharmacological response. As
per Schueler, pharmacophore was defined as “a molecular framework that carrie s
(phoros) the essential features responsible for a drug’s (pharmacon) biological activity.”
Beckett and his coworkers proposed the pharmacophore models for the musca-
rinic agents for the first time [12]. They gave the desired approximate distances be-
tween the different features that are essential for a molecule to act as an analog of
muscarine (Fig. 13.1). Their work was based on approximations. The first commuted
pharmacophore model was developed by Kier and his coworkers in 1967 (Fig. 13.2) for
294 Chapter 13 Pharmacophore mapping in computer-aided drug designing (CADD)
the muscarinic agents [13]. Ironically, both of the above models were developed for
the same receptors and were similar in nature but were developed in different labs.
With the emergence of highly sophisticated computational tools with the passage of
time, it was from the early 1990s that 3D pharmacophore modeling became a possibil-
ity that laid down the foundation of highly helpful pharmacophore-based drug design-
ing approaches such as virtual screening, de novo drug designing, etc. Considering the
Fig. 13.1: The first pharmacophore developed by Becket and coworkers for a muscarinic receptor agonist.
They give the desired approximate distance between the required substituent for a molecule to act as a
potent muscarinic receptor agonist.
Fig. 13.2: Pharmacophore, developed by Keir and his coworkers, for the muscarinic agonist.
13.2 Historical perspective of the pharmacophore concept 295
importance of pharmacophore, the IUPAC defined this concept in the year 1998 [14] as
“A pharmacophore is the ensemble of steric and electronic features that is necessary
to ensure the optimal supramolecular interactions with a specific biological target
structure and to trigger (or to block) its biological response.”
13.3 Understanding the concept: pharmacophore
Pharmacophore is not just limited to identifying the chemical or functional groups es-
sential for biological activity, the notion which evolved from the lab of Paul Ehlrich.
Instead, it is the much broader term that includes every feature that is essential for im-
parting pharmacological response to receptors. Considering the above idea, the most
complete and simplest definition for the pharmacophore can be given as “A pharmaco-
phore is the pattern of features of a molecule that is responsible for a biological effect.”
Here, this pattern of features includes everything, from the earlier concept of the
“arrangement of chemical groups” to the modern perspective of hydrogen bonding,
hydrophobicity, etc. The following are the most important features that are taken into
consideration while defining a pharmacophore for any particular target (Fig. 13.3):
– Hydrogen bond acceptors (HBAs)
– Hydrogen bond donors (HBDs)
Fig. 13.3: The most common attributes of a pharmacophore to be developed.
296 Chapter 13 Pharmacophore mapping in computer-aided drug designing (CADD)
– Positive ionizable groups
– Negative ionizable groups
– Hydrophobic regions
– Metal binding regions
– Aromatic groups
These pharmacophoric features have to be arranged in a 3D space to obtain a clear
idea of how a new molecule should be designed, based on the pharmacophore. This is
termed “pharmacophore modeling or mapping.”
13.4 Types of pharmacophore modeling (mapping)
Pharmacophore modeling, similar to the analogy to CADD, is classified as ligand-
based and structure-based pharmacophore modeling(Fig.13.4).Bothoftheseap-
proaches differ from each other in generating the model, however, the application of
both remains the same [2].
13.4.1 Ligand-based pharmacophore modeling
Ligand-based pharmacophore modeling is a powerful method in drug discovery and
CADD that focuses on characterizing the common structural and chemical features of
Fig. 13.4: Classification of the pharmacophore modeling, based on the approach used for the
development of the pharmacophore.
13.4 Types of pharmacophore modeling (mapping) 297
active ligands or molecules known to interact with a specific biological target, such as
a receptor or enzyme [15]. This approach is particularly valuable when detailed struc-
tural information about the target is limited or unavailable. The process begins by
compiling a set of structurally diverse ligands that are known to bind to the target of
interest. These ligands are then aligned based on their common features, and a phar-
macophore model is constructed [16–18]. The pharmacophore model encapsulates
critical spatial arrangements and functional groups essential for binding and biologi-
cal activity, essentially representing the molecular “key” to the target’s “lock.”
Ligand-based pharmacophore modeling is a versatile tool in drug discovery for:
– Lead identification: It helps identify potential lead compounds by screening
large chemical databases to find molecules that match the pharmacophore
model.
– Lead optimization: Pharmacophore models guide medicinal chemists in modify-
ing lead compounds to enhance their affinity, selectivity, and other desirable
properties.
– Predicting biological activity: These models can provide insights into the phar-
macological activity of new compounds or compounds with features similar to
known actives.
– Exploring structure – activity relationships (SARs): Understanding the
ligand–target interactions aids in the exploration of SARs, which is crucial for ra-
tional drug design.
Ligand-based pharmacophore modeling is a valuable tool for drug discovery, enabling
the development of new therapeutic agents and facilitating the optimization of exist-
ing drug candidates. It complements other methods in CADD and plays a central role
in the quest for innovative pharmaceutical solutions. In this methodology, a pharma-
cophore is deduced from the known active ligands against a particular disease or dis-
order. This is the oldest approach for pharmacophore modeling and has come a long
way since its emergence in the early 1990s. The basic steps involved in the ligand-
based pharmacophore mapping include the following (Fig. 13.5).
13.4.1.1 Curation of the dataset
The very first step of ligand-based pharmacophore modeling is the selection of the
dataset for the generation of the pharmacophore. In this step, known active com-
pounds that are diverse chemically but share a common target biological activity are
selected. The dataset is then further divided into the training and test datasets. The
training dataset is used for the generation of the pharmacophore model and the test
dataset is used for the validation of the generated pharmacophore model. The dataset
can be divided manually, randomly, or through machine learning approaches such as
Kennard stone or Euclidean-based algorithms.
298 Chapter 13 Pharmacophore mapping in computer-aided drug designing (CADD)
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