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167
8.1 Introduction
The landscape of drug discovery and development unfolds as a multifaceted and resource-intensive
journey spanning a duration exceeding a decade [1]. The intricacies of drug design and exploration
demand a holistic embrace of interdisciplinary strategies. Within this context, computer-aided
drug design (CADD) methodologies come to the fore, predominantly orchestrating the early to
intermediate phases of the drug discovery continuum. The expansion of computational prowess,
data reservoirs, software innovations, and algorithmic sophistication [2–4] has synergistically pro-
pelled the significant amplification of CADD’s role within the realms of drug discovery.
The CADD’s imprint resonates across diverse domains encompassing the pursuit of target iden-
tification, the validation of prospective targets, the pursuit of promising hits, the judicious curation
of lead compounds, and the meticulous refinement of their therapeutic potential [5]. This dis-
course converges its focus on the meticulous scrutiny of pharmacophore modeling situated within
the pantheon of CADD methodologies, thereby accentuating its prominence and implications.
8.1.1 The Role of Pharmacophore Modeling in Drug Design
A pharmacophore delineates the inherent molecular attributes of a small molecule that govern its
effective engagement within the cellular environment for biological or pharmacological functions.
These attributes encompass facets such as geometry, spatial orientation, physiological relevance,
and various other characteristics that collectively contribute to its functional role [6]. Serving as a
prime exemplar, a pharmacophore offers a tangible link between the three-dimensional (3D) struc-
ture and the activity of a molecule. Investigating the pharmacokinetic, pharmacodynamic, and
ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of a compound
constitute an indispensable stride toward establishing its candidacy as a potential drug-like entity.
The assessment of ADMET profiles holds pivotal sway, wielding significance for both synthetic
and endogenous molecules alike. Essential criteria encompassing favorable, adverse, and inhibi-
tory responses of ligands – small molecules – constitute pivotal landmarks for achieving targeted
ligand–receptor interactions [7].
8
Pharmacophore Modeling in Drug Design
Rahul Ghosh, Sharanya Roy, Gourav Rakshit, Nisha Kumari Singh,
and Nigam Jyoti Maiti
Department of Pharmaceutical Sciences & Technology, Birla Institute of Technology, Ranchi, Jharkhand, India
168
In drug development, there is a pivotal phase dedicated to the thorough investigation of interac-
tions between ligands and proteins [8]. The application of pharmacophore modeling enhances the
precision of this approach. Pharmacophores were initially proposed in 1970 as a method to gain a
better understanding of the interactions between small molecules (ligands) and receptors, particu-
larly in cases where detailed structural information was unavailable. Pharmacophore modeling
plays a prominent role in CADD, seamlessly integrating with similarity analysis and quantitative
structure–activity relationship (QSAR) studies [9].
Fundamentally, the objective of pharmacophore characterization is to shed light on the spatial
arrangement of functional groups or fragments within an active compound that primarily contrib-
utes to its biological efficacy. The identification of pharmacophore properties derived from ligand–
receptor interactions at binding sites enables the screening of extensive chemical libraries,
encompassing numerous potential novel entities. The construction of a robust pharmacophore
model hinges on the accurate depiction of compounds with biological activity [10–12].
A distinguishing hallmark of the pharmacophore concept lies in the amalgamation of atom-
centric models with their pharmacophoric properties. This amalgamation intentionally incorpo-
rates critical atoms or groups essential for biological interactions. It encompasses features such as
positively or negatively charged moieties, aromatic motifs, hydrophobic clusters, hydrogen bond
acceptors (HBAs), and hydrogen bond donors (HBDs). To facilitate comprehension, pharmacoph-
ore aspects are often represented as points, such as the centroid of five- or six-membered rings.
Inter-feature distances, crucial for the structural definition of pharmacophore models, delineate
these feature sites. The amalgamation of different features and the distances between them eluci-
date the chemical properties and spatial configurations of a pharmacophore.
Pharmacophore modeling stands as a pivotal stride within the intricate tapestry of drug design,
serving as a potent tool for sifting through potential inhibitors by leveraging the intricate tapestry
of their pharmacophore attributes [13]. The journey of drug design traverses a multifaceted land-
scape that commands substantial financial investments, often reaching into the millions, in pur-
suit of the elusive “magic bullet” capable of targeting and remedying specific diseases. Pioneers
within the biopharmaceutical sphere are driven to harness the formidable potential embedded
within the pharmacophore modeling paradigm, aiming to discern bioactive entities through a sub-
strate of foundational biological insights. This endeavor not only augments the precision of the
process but also compresses the temporal horizons of the drug design narrative [8, 14–18].
At the core of drug design, the pharmacophore model is delineated into two primary facets:
structure-based pharmacophore modeling and ligand-based pharmacophore modeling, which is
depicted in Figure 8.1. The former hinges upon preexisting cognizance of ligand properties or
documented inhibitor–receptor complexes. In contrast, the latter thrives on the diversity of phar-
macophore attributes, encompassing hydrogen bonding, hydrophobic interactions, aromatic con-
tacts, metal-mediated associations, and charged interactions, all marshaled in the pursuit of
fashioning novel therapeutic agents [19].
A wealth of online bioinformatics tools stands ready to empower researchers in their pharmaco-
phore modeling ventures, making these sophisticated methods widely accessible. Present-day, a
generalized pharmacophore-based computational approach unfolds through several sequential
steps. This journey commences with the quest for the 3D structure of a biological target implicated
in a specific ailment. Pharmacophore modeling enters the scene, complemented by virtual screen-
ings (VSs) of compound databases. This dual strategy endeavors to unearth molecules displaying
propitious attributes for future therapeutic endeavors. Hits are then subjected to thorough physico-
chemical evaluations, as these attributes emerge as pivotal determinants in establishing their
potential as potent inhibitors [20].
8.1 Introduction 169
Expanding the scope, the biological activities of the chosen molecules undergo scrutiny using
predictive servers that illuminate enzyme-catalyzed metabolic pathways, facilitating the identifi-
cation of high-scoring hits. The following assessments use the Lipinski Rule of Five to examine
bioavailability and ADME-Tox characteristics. As a result, compounds with promising bioavaila-
bility, bioactivity, and ADMET features should be given priority for in vitro testing. The pharmaco-
phore concept thus catalyzes hastening the drug design trajectory [2].
Aligning medications with individual genetic profiles is one area where the pharmacophore
method has expanded into modern personalized medicine. The pharmacophore idea has several
practical uses, including but not limited to target discernment, off-target prediction, VS, and
ADME-Tox modeling. The use of the pharmacophore paradigm in molecular docking simulations
improves the accuracy of binding posture prediction even more. A vital subset within the sphere
of CADD, pharmacophore modeling encapsulates innovation and potential at the crossroads of
modern scientific advancement [21].
Data collection
Ligand-based
pharmacophore
modelling
Structure based
pharmacophore
modelling
Model generation
Virtual screening
Post processing
Best performing
model
Renement and
validation of the model
In vitro and in vivo
validation
No
Yes
Figure 8.1 Flowchart illustrating the procedure of computational drug design.
170
8.1.2 Historical Perspective and Evolution of Pharmacophore Concepts
The origin of the pharmacophore concept is attributed to Paul Ehrlich, who introduced a novel
approach to developing dyes by examining chromophores, the molecular components responsible
for coloration. In 1890, Ehrlich provided the initial definition of a pharmacophore as “a molecular
framework that bears (phoros) the essential attributes contributing to the biological activity (phar-
macon) of a drug.” In contemporary scientific terminology, a pharmacophore is defined, as estab-
lished by Peter Günd, as “a collection of structural features within a molecule that is recognized at
a receptor site and is accountable for the molecule’s biological activity” [22]. Peter Günd has fur-
ther explored the evolutionary history of the pharmacophore concept in his review [22].
The pharmacophore concept reached its full potential when 3D database search software became
available in the 1990s. The pioneering computer program MOLPAT [6], designed to identify phar-
macophore patterns, was created by Günd, Wipke, and Langridge at Princeton University in 1974.
The demand for 3D structure search software grew alongside the development of rapid 3D struc-
ture generation programs like CONCORD [23], CORINA [24, 25], AIMB [26], and WIZARD [27].
Pharmaceutical companies contributed to the development of 3D search software, with examples
such as ALADDIN [28] (originally by Abbott Laboratories, later commercialized by Daylight
Chemical Information Systems, Inc.) and 3D-Search [29] (developed by Lederle Laboratories),
while academic and government institutions introduced CAST-3D [30] (Chemical Abstract
Services), DOCK [31] (University of California at San Francisco), and CAVEAT (University of
California at Berkeley). Subsequently, the Marshall group devised a pharmacophore method
rooted in ligand structures, known as the “active analog” approach [22]. They applied this approach
to a group of ACE inhibitors [32] and validated the pharmacophore model against available experi-
mental data, demonstrating a strong correlation [33].
The advent of commercial 3D searching systems marked a significant milestone in the field with
the release of MACCS-3D by Güner et al. [22]. Over the subsequent four years, critical advance-
ments were made, paving the way for the technology available today. This period saw the develop-
ment of key 3D searching technologies, including ChemDBS-3D [34] (Chemical Design Inc.,
USA), UNITY [35] (Tripos Inc., USA), and Catalyst [36] (Accelrys Inc., USA). The demand for
pharmacophore development software surged as these 3D searching technologies became widely
accessible. While many of these 3D searching software tools included built-in query generation
capabilities, specialized pharmacophore generation software also emerged. Notable examples
included DISCO [37] by Martin et al. (Tripos Inc., USA), HipHop [36] by Barnum et al. (Accelrys
Inc., USA), and GASP [38] by Jones et al. (Tripos Inc., USA). Simultaneously, predictive models
rooted in QSAR, such as CoMFA (Tripos Inc., USA) [39] by Cramer et al., Apex-3D (Accelrys Inc.,
USA) by Golander and Vorpagel, and HypoGen [40] by Teig et al. (Accelrys Inc., USA), also came
into existence. A comprehensive exploration of the usage and validation of pharmacophore devel-
opment software can be found in the pharmacophore book [8, 28, 41–43]. In Table 8.1, we have
given commonly employed server names with descriptions.
8.2 Essential Concepts in Pharmacophore Hypothesis Generation
Ideally, IC50 values serve as a reliable metric derived from in vitro experimentation, encompassing
target-based and cell-free methodologies, with certain factors mitigated, such as cell efflux, cellular
uptake, and metabolic influences. Cell-based assays, on the other hand, rely on discerning target
interactions with ligands, which may induce modifications in protein expression, protein
171
Table 8.1 Programs and servers used in pharmacophore modeling.
Server name Description
CATALYST-HipHop [36] CATALYST has been incorporated into the BIOVIA Discovery Studio and
comprises essential algorithms for pharmacophore generation, specifically
HipHop and HypoGen. HipHop is employed for aligning active ligands
concerning a particular target, enabling the identification of 3D
conformations featuring common pharmacophoric elements through the
superimposition of diverse molecular structures
CATALYST-HypoGen [40] By assimilating data derived from biological analyses, pharmacophore
modeling establishes hypotheses that facilitate the quantitative estimation of
molecular activity. This integration enables a more streamlined approach to
pharmacophore modeling by establishing meaningful connections between
structural features and activity data
GASP [44] GASP is a component of the SYBYL package that employs a genetic
algorithm to identify pharmacophores. Unlike conventional pharmacophore
determination methods, GASP conducts conformational searches in real
time and seamlessly integrates this process into its program workflow. Before
superimposing them onto each input chemical, the analysis involves the
examination of conformational changes utilizing a singular low-energy
structure and random spinning
LigandScout [45] While LigandScout offers the capability to conduct structure-based and
ligand-based pharmacophore modeling, it stands out as one of the
pioneering software applications primarily designed for specialized
structure-based pharmacophore modeling. It is particularly favored for
applications where the 3D structure of the target protein in complex with
its ligands is available
GALAHAD [46] This software utilizes an adapted genetic algorithm that addresses specific
limitations found in the GASP program, resulting in enhanced performance.
It accelerates computational processing by employing preconstructed
molecular structures as an initial reference point
MOE [47] MOE possesses the capability to conduct both ligand-based and structure-
based pharmacophore modeling. The process of constructing these models
involves pairwise alignment of active ligands. For optimal results, it is
advisable to reduce the size of the training dataset by clustering molecules
with similar characteristics
PHASE [48] This tool is included in the Schrodinger package. It serves as a practical
method employed in the field of drug discovery, whether the receptor
structure is available. It generates a hypothesis based on one or more ligands,
protein–ligand complexes, and apoproteins. It features a specialized
algorithm specifically tailored for optimizing lead compounds and
conducting virtual screening
PharmaGist [49] This is an openly accessible web server utilized for the generation of
ligand-based pharmacophores. This online tool identifies pharmacophores
through numerous flexible alignments of the input molecules
Pharmer [50] In contrast to conventional molecular library screening methods, this
pharmacophore technique conducts searches based on the breadth and
intricacy of the query. The source code is accessible under an open-source
license, and the technique is well-regarded for its exceptional speed
PharmMapper [51] This publicly accessible web service is employed to identify potential targets
for input ligands. Utilizing semirigid pharmacophore mapping, the
methodology involves the calculation of pharmacophores
172
degradation, protein trafficking, and perturbations in cell membrane stability. When dealing with
inactive compounds, it is imperative to elucidate the underlying reasons for their lack of activity,
whether attributed to the absence of target interactions, metabolic processes, or efflux pump
activity. It is noteworthy that in vitro data, often acquired through fluorometry-based or
spectrophotometry-based techniques, may be prone to inaccuracies, partially due to the presence
of chromophoric groups. Therefore, meticulous scrutiny is essential when interpreting such data.
In the contemporary scientific landscape, an immense volume of data, spanning both in silico and
in vitro realms, has been exponentially accumulating and populating various databases. For
instance, activity data can be readily accessed from publicly available sources, including but not
limited to open PHACT [52] and PubChem [53]. However, when extracting activity data from
these sources, caution must be exercised, as a single compound may exhibit activity against
multiple targets and be the result of various bioassays. It is essential to acknowledge that minor
discrepancies exist in virtually every database; for example, a typical release of chEMBL may con-
tain approximately 5% inaccuracies in chemical structures, 3% erroneous target information, and
1% errors in data deposition. Hence, users should exercise due diligence when selecting and utiliz-
ing data from these repositories.
8.2.1.1 Partitioning Initial Data into Distinctive Datasets
In accordance with contemporary scientific practices, the raw initial dataset is meticulously
partitioned into three distinct subsets, namely, the training set, the test set, and the decoy set. This
systematic division serves as a pivotal step in the process of constructing a robust pharmacophore
model, as elucidated by some research (Figure 8.2).
Collection of data
Compounds
with no known
IC50 (decoys)
Compounds with
known IC50
(actives)
Training of the
model
Test set
Training set
Validation of model
Virtual screening for
unknown databases to
find the potential hits
Figure 8.2 An overarching procedure for partitioning the data into many datasets.