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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5960_Библиотеки_им_академика_М_И_Перельмана.pdf
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Training Set In the context of ligand-based pharmacophore modeling, a designated ensemble of
compounds exhibiting known activity serves as the foundational training dataset. Typically, this
dataset comprises a minimum of two active molecules. Within a specific training dataset,
compounds are categorized according to their activity levels, which may include classifications
such as “very potent,” “moderately potent,” and “inactive,” particularly in the context of
semiquantitative pharmacophore models. These pharmacophore models are constructed based on
shared chemical attributes found among highly active molecules, characterized by their diverse 3D
structures, as exemplified by studies conducted by Leach et al. [54], as well as Seidel et al. [55].
Test Set A test set in pharmacophore modeling is a distinct subset of compounds, separate from
the training set, and it serves a pivotal role in assessing the performance of a constructed
pharmacophore model. Its primary objective is to evaluate how effectively the model can predict
the bioactivity or properties of compounds that it has not encountered during the model-building
process. The test set typically comprises compounds with known bioactivity, which can be
categorized into two main groups: active compounds and inactive compounds. Active compounds
are those with confirmed biological activity, while inactive compounds serve as controls. Both are
essential for evaluating the model’s ability to discriminate between active and nonactive
molecules [56].
Decoys Set Decoys in pharmacophore modeling are negative control molecules used to differentiate
between active and inactive compounds, assisting in model validation and optimization. They aid
in assessing the model’s ability to distinguish between active compounds and chemically similar
yet inactive decoys, often measured through sensitivity and specificity metrics. In addition, decoys
play a vital role in refining pharmacophore models to accurately capture essential features for
biological activity. Once validated, these models are applied in VS to identify potential drug
candidates, with decoys helping prioritize compounds likely to be active against the target. Proper
selection of diverse decoys representative of the chemical space is crucial for meaningful
discrimination tests, making decoys an integral component of pharmacophore modeling in drug
discovery [53].
8.3 Diverse Approaches to Pharmacophore Modeling
There are two distinct approaches for generating pharmacophores: (i) the ligand-based method
[2, 57] and (ii) the structure-based method [58]. The ligand-based approach is indirect,
particularly applicable when a 3D structure of the target protein is unavailable. This method
relies on known active molecules or ligands (inhibitors) for the target and searches for new
inhibitors based on their functional attributes, referred to as features, which are utilized to
construct a model [59]. Initially, the model is trained using shared pharmacophoric features
from multiple known compounds, followed by model assessment and validation. On the other
hand, a structure-based pharmacophore model is developed when the crystallographic structure
of the target receptor is accessible [58]. The 3D structure of the protein complexed with a
potential inhibitor is highly desirable as it provides detailed information about residues and the
structural characteristics of the active site pocket. Consequently, the interaction between the
ligand and the receptor serves as the foundation for constructing the structure-based model. In
cases where the crystal structure is unavailable, a homology model of the target protein can be
employed [47, 50, 59, 60].
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Pharmacophore models comprise diverse chemical features such as HBDs and HBAs within
ligand-receptor complexes. The central position and the tolerance sphere play pivotal roles in
assessing the effectiveness of a pharmacophore model. In essence, a pharmacophore model encap-
sulates three fundamental aspects: feature “type,” representing the specific chemical characteris-
tic; “location,” signifying the precise spatial coordinates of the feature; and “weight,” which
denotes the degree of significance attributed to each feature. The weight factor assumes signifi-
cance during the computation of a geometric fit value for compounds [13].
8.3.1 Ligand-Based Pharmacophore Modeling
In scenarios where structural information for a macromolecular target is unavailable, the utiliza-
tion of ligand-based pharmacophore modeling emerges as a crucial computational tool in the field
of drug development. The standard procedure for this approach is to choose a set of known ligands
that interact critically with a specific macromolecular target and then extract their shared chemi-
cal characteristics from their 3D structures. There are essentially two stages to the pharmacophore
synthesis process that starts with a collection of ligands, or training set compounds:
Step One: Generating a Conformational Ensemble: To capture the natural conformational flexi-
bility of the training set ligands, it is necessary to construct a complete conformational space for
each of them.
Step Two: The process of identifying pharmacophore features and aligning ligands involves the
critical step of aligning diverse ligands within the training set and discerning their fundamental
shared chemical properties. Following the generation of conformational ensembles, pharmacoph-
ore models are constructed based on these identified properties. The fundamental approaches and
main obstacles in the field of ligand-based pharmacophore modeling are accurately dealing with
the conformational flexibility of ligands and performing accurate molecular alignment.
The domain of ligand-based pharmacophore modeling has undergone considerable advance-
ments, although it continues to encounter notable challenges, particularly in simulating ligand flex-
ibility. Addressing this issue involves two distinct approaches. The first method, referred to as the
pre-enumeration approach, entails the prior calculation and storage of numerous conformations for
each molecule in a database [61]. The second method, known as the “on-the-fly” approach, involves
concurrent conformational analysis during the pharmacophore modeling process [61]. Despite the
possible need for large amounts of storage space, the pre-enumeration method has computational
benefits due to its ability to lower the cost of molecular alignment. On the other hand, while opti-
mizing on the fly gets rid of bulk storage, it could increase CPU usage. According to the data, the
pre-enumeration method is more effective than the on-the-fly calculating method [62].
In ligand-based pharmacophore modeling, molecular alignment is the second major obstacle.
Both point-based and property-based techniques are basic types of alignment methods [62].
Aligning points – which could represent atoms, fragments, or chemical properties – through least-
squares fitting is a common practice in point-based algorithms. When working with different
ligands, it might be difficult to set specified anchor points, which is the main drawback of these
methods. Alignments are made easier by property-based methods, which use molecular field
descriptors, which are usually represented by sets of Gaussian functions. The goal function in
alignment optimization is the intermolecular overlap of Gaussian functions, which are similarity
measurements. Previous reviews of traditional molecular alignment algorithms have covered a lot
of ground [62], and new alignment methods are being introduced all the time as a result of con-
tinuous discoveries. New developments include grid-based interaction energies [63], fuzzy pattern
recognition [64], atomic property fields [65], and stochastic proximity embedding [66].
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Choosing the right compounds for the training set is another major obstacle; this may appear
like a simple but confusing process to both new and seasoned users. Several factors influence the
ultimate pharmacophore model, encompassing the nature of ligand molecules, the size of the
dataset, and the chemical diversity inherent in the compounds under consideration [61]. For
ligands that interact with the same macromolecular target, using the same method and software
with different training sets can sometimes produce completely different pharmacophore models.
As an example, three separate groups of researchers used the Catalyst software with different train-
ing sets to independently construct pharmacophore models for cyclin-dependent kinase 2 (CDK2)
inhibitors. These groups were Hecker et al. [67], Toba et al. [68], and Vadivelan et al. [69]. The
models showed significant differences in the kind of features and the amount of space they had to
work with (Figure 8.3a), an issue that will be covered more in the review’s later parts.
8.3.2 Structure-Based Pharmacophore Modeling
This approach to pharmacophore modeling is known as structure-based pharmacophore mode-
ling, and it makes direct use of the 3D structure of the target macromolecule or the complex it
forms with a ligand. An in-depth examination of the spatial correlations and complementary
chemical properties inside the target’s active site is the first step in the methodology for doing
structure-based pharmacophore modeling. Then, a pharmacophore model is assembled using
these features. Two subsets of structure-based pharmacophore modeling methods exist those that
focus on macromolecules with ligand complexes and those that focus on macromolecules alone.
Locating the ligand-binding site within the macromolecular target and identifying pivotal
Hecker model
Pharmacophore using LigandScout Cluster pharmacophore model Pharmacophore map
Toba model Vedivelan model
(a)
(b)
Figure 8.3 Pharmacophore models delineating inhibitors targeting cyclin-dependent kinase 2 (CDK2).
(a) Utilizing Catalyst software developed by Hecker, Toba, and Vadivelan, pharmacophore models specific to
CDK2 inhibitors were formulated. (b) The fundamental methodology entails generating a comprehensive
pharmacophore map of CDK2 inhibitors employing a multicomplex-based strategy. Chemical attributes are
depicted using specific color codes: green signifies HBAs, magenta denotes hydrogen-bond donors, light
blue represents hydrophobic features, and orange indicates aromatic rings. Source: Yang [70]/with
permission of Elsevier.
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interaction sites between the macromolecule and the ligands are facilitated through the utilization
of the macromolecule–ligand–complex-based method [71].
A persistent challenge in structure-based pharmacophore modeling, specifically within the
macromolecule-based method, involves difficulties associated with identifying an excessive num-
ber of chemical characteristics within a single-binding site of the macromolecular target. This
issue is compounded by the absence of clear prioritization criteria in some instances. 3D database
screening and other similar applications are difficult for pharmacophore models with more than
seven chemical characteristics. Therefore, it is crucial to carefully choose a small set of chemical
properties – usually between three and seven features – to build a workable pharmacophore
hypothesis, which is not always an easy process. A further difficulty is that the model may only be
developed from a single macromolecule–ligand complex or a single macromolecule, which means
that the resultant pharmacophore hypothesis cannot represent QSARs. A most frequent pharma-
cophore model and a multicomplex-based comprehensive map were suggested in a recent study as
potential solutions to these problems [71]. Drawing from an extensive repository comprising 124
crystal structures of human CDK2 inhibitor complexes, this study employed a multicomplex-based
methodology to construct a comprehensive pharmacophore map for CDK2.
After utilizing LigandScout [70] to identify the chemical properties of each compound, a com-
plete pharmacophore map was generated by clustering all these features. Figure 8.3b shows that
the known map included almost all the important chemical characteristics necessary for CDK2–
inhibitor interactions. Except for an aromatic ring feature (depicted in orange) present in the
Hecker model, all pharmacophore features identified in the published ligand-based models,
including the Hecker model, Toba model, and Vadivelan model (refer to Figure 8.3a), are encom-
passed within the comprehensive map. This is an important finding. Accordingly, it appears that
these ligand-based models stand in for parts of the whole map. It appears that ligand scaffolds
relate to the aromatic ring characteristic in the Hecker model, which is the sole exception.
A structure-based pharmacophore modeling technique is unable to detect a chemical property
present in tiny molecular scaffolds that functions as a pseudo-pharmacophore feature as it does
not represent an interaction between ligands and macromolecules. A simplified model is neces-
sary for real-world applications since the comprehensive pharmacophore map is too limited and
not good for VS. One workable approach is to focus on the most common characteristics, those
that are known to be critical for the efficacy of CDK2 inhibitors. A most-frequent-feature phar-
macophore model was thus constructed by meticulously selecting and merging the seven most
prominent characteristics, which were present in 124 complexes with a probability greater than
25%. In addition to showing that this most-frequent-feature model can successfully distinguish
between known CDK2 inhibitors and molecules in an inactive focused dataset, validation studies
have also shown that it can accurately predict the activities of a diverse range of CDK2 inhibitors
in an active external dataset [71].
8.4 Application of Pharmacophore Modeling
A 3D pharmacophore can be constructed either by utilizing a set of active ligands or by employing
complexes derived from apo-state proteins (targets) and ligands. Subsequently, VS is applied to
assess a chemical library (ligands) against these 3D pharmacophores. Compounds that meet the
criteria outlined in the query pharmacophore are subjected to further validation through experimental
techniques, as outlined by Schaller et al. [72].
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8.4.1 Applications of Pharmacophore-Based Virtual Screening
A VS process involves the systematic application of a pharmacophore model to screen extensive
chemical libraries. This results in the creation of a virtual hit list containing molecules that align
with the model’s specifications. Subsequent experimental testing often confirms the activity of
these compounds. Consequently, VS serves as a valuable tool for efficiently screening large
compound collections, thereby augmenting the pool of active molecules in chemical databases
available for further experiments. There is a consensus within the scientific community that VS is
a valuable strategy to complement traditional high-throughput screening (HTS) initiatives
[73, 74]. This preference is attributed to its substantially higher true positive hit rates compared to
random testing procedures [75–77]. Although various prospective trials have been docu-
mented [78, 79], reported hit rates from prospective pharmacophore-based VS typically range
from 5% to 40%. In contrast, a random selection of test compounds to identify active molecules
often yields hit rates lower than 1%, exemplified by rates such as 0.075% for peroxisome
proliferator-activated receptor (PPAR) [80], 0.55% for glycogen synthase kinase-3, and 0.021% for
protein tyrosine phosphatase-1B [77].
8.4.1.1 Drug Discovery
Pharmacophore-based VS proves particularly advantageous in the initial categorization of
compound classes and the refinement of compound properties, offering utility across various
stages of the drug development pipeline, as expounded upon in subsequent sections.
Lead Identification The “cherry-picking” technique, which prioritizes lead identification,
represents a prevalent application of pharmacophore-based VS. These programs are commonly
employed to minimize the number of compounds subjected to biological screens, thereby
optimizing the selection of molecules for experimental testing. The overarching objective is the
discovery of novel lead compounds tailored to target specific diseases, with the potential for further
development into therapeutic drugs addressing the identified illnesses. This application has been
substantiated in numerous recent research studies [81–86]. For instance, Ha et al. utilized a ligand-
based pharmacophore modeling strategy to uncover new ligands targeting the CXCR2 chemokine
receptor [86]. Lepailleur et al. [87] identified novel compounds with enhanced binding affinity to
the 5HT4 receptor through pharmacophore-based VS, aiming to discover new histamine H3
receptor antagonists. The first example of a compound exhibiting a dual mode of action was
deemed advantageous for the treatment of Alzheimer’s disease [87].
Structure–Activity Relationship A pharmacophore model encapsulates essential functionalities for
a substance to exhibit bioactivity, delineating the potential binding mode of active compounds to
their specific targets. The primary objective of a pharmacophore model is to discriminate between
active and inactive molecules, ideally within the same chemical series. This characteristic makes
pharmacophore modeling particularly valuable for elucidating complex structure–activity
relationships (SARs). The presence or absence of specific chemical groups, denoted as
pharmacophore characteristics, within individual molecules may account for observed variations
in the biological activities of a given compound set in experimental settings. The process of
constructing a pharmacophore model provides an opportune moment to commence the elucidation
of SARs, shedding light on the underlying mechanisms driving biological activity. For a deeper
understanding of how chemicals interact with the P-glycoprotein drug binding site, Ferreira et al.
employed pharmacophore models [88]. Notably, active compounds isolated in-house from
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Euphorbia species did not incorporate the nitrogen atom, a component postulated to play a crucial
role in modulators according to previous studies. The researchers utilized a dataset comprising
modulators from literature, compounds from their collection, and inactive molecules to assess the
numerous refined pharmacophore models they developed. Their final model indicated that
P-glycoprotein modulators must exhibit hydrophobic interactions and an HBA characteristic.
Furthermore, even with the addition of another HBA/HBD characteristic, the mapping of active
chemicals remained consistent [88].
Moreover, pharmacophore models play a pivotal role in recapitulating established SARs, thereby
contributing to the discovery of novel bioactive compounds. In 2002, Flohr et al. conducted experi-
mental investigations utilizing the natural peptide urotensin II and synthetic analogs to identify
crucial interactions necessary for binding to the urotensin II receptor [89]. Leveraging existing
SAR information, pharmacophore models were constructed and employed to screen a chemical
library comprising small, drug-like molecules. Subsequent experimental testing of the virtual hits
led to the identification of six new scaffold classes, notably featuring nonpeptidic molecules in
prominent roles [89].
Scaffold Hopping Pharmacophore features delineate general chemical functions rather than specific
functional groupings. Furthermore, the identification of virtual hits occupying 3D locations critical
for biological activity is integral to pharmacophore models, primarily relying on localized functional
similarities among active chemicals. It is noteworthy that the exact 2D architectures of the mapping
molecules are not constrained in any particular manner. Pharmacophore models exhibit flexibility
to accommodate structurally diverse hits, even as their composition is influenced by the 2D
structures of the molecules utilized in their generation and refinement. This characteristic of
pharmacophore modeling extends its utility to both natural products and synthetic chemicals,
enabling exploration across a broad spectrum of chemical space. Crucially, this flexibility facilitates
the implementation of the “scaffold hopping” strategy, allowing for the discovery of new scaffolds
not previously associated with the target. A comprehensive examination of pharmacophore
modeling within the framework of scaffold hopping has been extensively covered in a prior
article [90]. Recent research has leveraged pharmacophore modeling to identify new ligands for the
TRPV1 channel, representing the transient receptor potential vanilloid type 1 [91]. While initial hits
may exhibit weak target contacts, they serve as intriguing starting points for optimizing the chemical
process. Such studies often prioritize the discovery of novel chemical scaffolds over the identification
of highly active molecules in theoretical validation datasets, resulting in lower overall similarity
scores [91].
The significance of scaffold hopping in the pharmaceutical industry is underscored by its capac-
ity to explore compounds without encountering intellectual property concerns. This attribute is
particularly relevant in a broader context, as scaffold hopping enables the discovery of compounds
with limited available data. This scenario frequently arises when examining chemicals found in
consumer goods and environmental contaminants, both of which often possess characteristics dis-
tinct from traditional drugs.
8.4.2 Applications in Drug Target Fishing
Computer-Aided Pharmacological Design (CADD) emerges as a potent approach for unraveling
the mechanisms of action of pharmacological compounds, particularly in cases where these mech-
anisms are not well-established. Chemometrics-based similarity search algorithms are employed
to identify related compounds with known action mechanisms [92]. While pharmacophore queries
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prove beneficial for discovering specific chemicals, pharmacophore modeling encompasses various
other applications. A notable use case involves employing the compound itself as a query to
identify an appropriate pharmacophore model. These models can either be sourced from existing
databases or manually crafted [93]. Moreover, this strategy facilitates the identification of a
biological target for a specific chemical with an unknown biological function. A notable example
is the discovery of numerous potential therapeutic targets through the analysis of various plant
metabolites [79].
8.4.3 Applications in Ligand Profiling
Within ligand profiling, pharmacophore modeling serves to predict potential targets, anticipate
side effects, and propose novel therapeutic targets. Both structure-based and ligand-based phar-
macophore modeling are employed in ligand profiling, although the former is more preva-
lent [94]. Instead of molecular docking limitations, pharmacophore modeling offers an
alternative for ligand profiling [95]. A study utilizing ligand profiling based on pharmacophore
models achieved accurate target assignments. The investigation involved screening a dataset
with 2108 pharmacophore models using 16 metabolites from Ruta graveolens. Experimental
trials were conducted to validate computational results, with a specific focus on acetylcholinest-
erase (AChE), the coat protein of the human rhinovirus (HRV), and cannabinoid receptor type 2
(CB2). The binding patterns elucidated through pharmacophore modeling consistently aligned
with experimental data [79].
8.4.4 Applications in Docking
Various strategies exist for the integration of molecular modeling techniques based on docking
and pharmacophores, offering the potential to mitigate certain limitations inherent in each
method and enhance overall outcomes. In the realm of docking, pharmacophore models can be
employed as guiding constraints based on pharmacophoric properties. Within the docking pro-
cess, these models serve to restrict the number of compounds subjected to docking, and post-
docking, they function as filters for ligand selection and pose ranking [57]. Prior to initiating
molecular docking, these pharmacophore models are applied as filters to assess whether mol-
ecules meet the fundamental chemical and structural requirements specified by the query [96].
To pre-filter the ligand database, pharmacophores are employed as search queries preceding
the docking assessment. Certain molecular docking programs permit the inclusion of specific
types of ligand–protein interactions in docking poses, facilitating pharmacophore-constrained
docking [97]. Another approach to enhance the quality of top-ranking docking results involves
the utilization of pharmacophore models after docking to evaluate the optimal binding mode of
a chemical [98].
Illustratively, a search for potential inhibitors of Chk-1 (checkpoint kinase-1) within a chemical
library employed a hybrid approach combining pharmacophore and docking methodologies.
Initially, small molecules possessing HBA and HBD properties, capable of interacting with the
adenine region of Chk-1, were screened using a pharmacophore model. Subsequently, docking
procedures were executed to identify poses, considering these specific interactions [99]. For each
ligand, multiple poses were recorded and subsequently rescored. Ultimately, the methodology
identified chemical entities with potential binding affinities [100]. In a distinct study, new dual
adenosine A1/A2A receptor antagonists were discovered through the application of docking and
pharmacophore modeling [101].
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8.4.5 Applications in ADMET
When drugs exhibit deviations from their intended effects within the body, it is often attributed to
issues related to ADMET [102]. Consequently, there is a consensus that the early assessment of
ADMET characteristics plays a crucial role in the drug development process. In the initial stages of
drug development, pharmacophore modeling approaches are employed to predict ADMET fea-
tures to reduce the incidence of unsuccessful attempts in the search for new medications [22].
Through the comparison of the chemical characteristics of tested compounds with medicines
possessing well-documented ADMET profiles, pharmacophore models can be utilized to identify
potential interactions with drug-metabolizing enzymes [103]. For instance, interactions between
known medicines and cytochrome P450 (CYP) enzymes have been leveraged to develop a pharma-
cophore model estimating the binding of drug-like compounds to specific CYP enzymes, with a
focus on potential degradation by these enzymes [104]. Similarly, an ADMET pharmacophore
approach has been employed to model 5′-diphospho-glucuronosyl transferase enzymes due to
their involvement in drug excretion processes [105].
8.4.6 Modulation of the Immune System
Small compounds capable of influencing or modulating immune responses are termed modulators.
Toll-like receptors (TLRs) represent one such example, playing a pivotal role in eliciting innate
immune responses in reaction to recognized infection-related molecular patterns. According to
Murgueitio et al. [106], an auspicious strategy for addressing various ailments, including cancer,
autoimmune diseases, neurological disorders, and allergies, involves the development of modulator
molecules targeting TLRs. In a study, structure-based VS was employed in conjunction with experi-
mental validation to identify a novel TLR8 modulator. Molecular docking was utilized to analyze
the diverse binding mechanisms of the screened molecule. Subsequently, molecules were catego-
rized based on optimal binding positions, and the preferred position was employed to construct a
3D model of the pharmacophore. This pharmacophore model was then applied to VS processes.
8.5 Emerging Trends in Pharmacophore Model Development
Pharmacophore modeling, encompassing both structure-based and ligand-based techniques,
stands as a computational tool widely employed in various computer-assisted biological
applications. Numerous CADD initiatives heavily rely on its utilization. Furthermore, VS and
investigations into protein function extensively depend on the integration of 3D pharmacophore
models. Recent advancements have showcased the application of 3D pharmacophore models in
combination with molecular dynamics (MDs) simulations, providing dynamic insights into
macromolecular–ligand interactions within a simulated cellular physiological environment. The
integration of newly developed 3D pharmacophores and machine learning methods has facilitated
the screening of diverse types of novel compounds [107].
8.5.1 Involvement of Machine Learning
The field of drug discovery has experienced substantial advancements owing to the availability of
extensive datasets and the enhancement of computational capabilities. Over recent years, there
has been a growing integration of pharmacophore modeling with machine learning, a branch of
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artificial intelligence (AI). Machine learning methods, particularly deep learning, have demonstrated
increased efficiency, especially in the era of large-scale data, leading to the emergence of numer-
ous pharmacophore-based machine learning algorithms. Machine learning has also played a piv-
otal role in refining scoring functions [72].
Prominent machine learning methods trained on pharmacophores include DeepSite, PharmIF
(Pharmacophore-based Interaction Fingerprint), and Hot Spots guided Receptor-based
Pharmacophores (HSPharm). HSPharm employs random forest decision trees trained with phar-
macophoric descriptors to reduce the number of pharmacophore attributes [108]. PharmIF, on the
other hand, utilizes a Support Vector Machine (SVM) trained with pharmacophoric fingerprints to
rank docking poses of small compounds [107]. DeepSite employs a Convolutional Neural Network
(CNN) trained with pharmacophoric descriptions to detect cavities and determine binding
affinities [37].
8.5.2 Prediction of Pharmacokinetic Properties
Accurately forecasting the ADMET features of a compound is imperative in evaluating its druga-
bility and forms a critical prerequisite for the rational design of effective therapeutic compounds.
Pharmacophore modeling serves as one approach to predict ADMET characteristics, allowing the
identification of drugs with similar profiles as other studied compounds. This involves precise
predictions of ADMET properties and binding affinities using a set of pharmacophore models [4].
In a recent study, a novel strategy amalgamating pharmacophore modeling and QSAR approaches
were employed, focusing on the hexadecahydro-1H-cyclopenta[a]phenanthrene framework
(HHCPF). This innovative method facilitated a comprehensive understanding of the intricate inter-
play between the characteristics of target binding sites and specific pharmacophore features within
a group of medicinal compounds. The investigation incorporated drug compounds based on HHCPF
and their target proteins through docked complexes. Subsequently, various target structures
obtained from renowned databases such as UniProt, DrugBank, and the Protein Data Bank (PDB)
were meticulously compared with the diverse ADMET characteristics of these medications [4].
8.5.3 Structural Biology and Protein Functionality Studies
Pharmacophore modeling serves as a prominent approach for gaining mechanistic insights into
protein functionality. It is particularly valuable for dissecting the ligand-dependent functions of
receptors. Notably, G-protein-coupled receptors (GPCRs), a class of vital drug targets implicated in
various diseases, exhibit intricate pharmacophore attributes and display flexibility in their ligand-
dependent activities. An effective study delved into the receptor-specific binding patterns within
closely related GPCR members of the same protein family. This analysis involved scrutinizing
binding patterns and identifying key residues located in the loop regions. In addition, some inves-
tigations have explored the concept of ligand bias, where ligands demonstrate preferences for spe-
cific signaling pathways mediated by their target GPCRs. Certain ligands have displayed
conformational constraints, particularly in the extracellular regions [23].
8.5.4 Integration with MDs Simulations
The active sites residing on the target molecule exhibit substantial flexibility. Consequently,
relying solely on a structure-based pharmacophore model featuring a solitary conformation of
the drug–target interaction may fail to capture the full spectrum of involved features in this
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interaction. To address this challenge posed by active site flexibility, dynamic pharmacophore models
prove invaluable. MDSs simulations capitalize on the inherent dynamism of protein–ligand
complexes within the cellular milieu. These MD simulation approaches can construct dynamic
pharmacophore models that align with the adaptable nature of target active sites or binding
pockets. An advanced strategy in 3D pharmacophore modeling involves the integration of mul-
tiple conformations into a pharmacophore model using MD simulations. Given the dynamic
nature of both the target and ligands, the resulting target–ligand complex and their interaction
patterns mirror this dynamism. MD simulations offer an effective means of mapping the
dynamics inherent to these complexes [109].
Advancements in the field have led to the creation of various software tools that combine MD
simulations and the generation of 3D pharmacophore models. One notable approach is the
Hydration Site-Restricted Pharmacophore (HSRP), which operates on the principle of con-
straining pharmacophore features to hydration sites identified on protein molecules. These
hydration sites are characterized by unfavorable thermodynamic properties, as determined
through MD simulations. HSRP effectively serves as a filter, enabling the selection of ligands
that exhibit greater relevance and favorable entropic characteristics when binding to the target
protein [109].
The Dynophore methodology, with a focus on spatiotemporal feature analysis through statis-
tical approaches, integrates 3D pharmacophore models with MDs simulations. Utilizing con-
formers from MD simulations, the Dynophore program assesses the chemical interaction
characteristics of protein–ligand complexes. It evaluates various interaction properties, includ-
ing charges, hydrogen bonds, and lipophilic contacts, using the MD trajectory data. The statisti-
cal ranking of these properties is determined based on their interaction patterns within the
protein complex. Subsequently, a 3D pharmacophore model is generated using the best-fitting
statistical model.
For optimizing 3D pharmacophores derived from MD simulations, two prevalent
approaches are MYSHAPE and the Common Hits Approach. In the Common Hits Approach,
3D pharmacophore models obtained from MD simulations are categorized based on their
interaction patterns, and VS utilizes these models to construct a virtual ligand library. In
contrast, MYSHAPE employs MD simulations of protein–ligand complexes to optimize
shape-dependent pharmacophore characteristics. The GRAIL method elucidates the
molecular interaction fields of pharmacophore models generated in MD simulations. This
method can calculate atomic densities of water, ligand, and protein molecules constituting
the interaction complex. The PyRod algorithm, incorporating heuristic scoring methods for
water molecules near the protein’s binding site, generates a 3D model of the pharmacoph-
ore. It also examines site-specific hydration-related pharmacophore models, which are
subsequently employed in VS [110].
Proteins are simulated using cosolvent-based MD in the presence of ethanol and water mol-
ecules employing the AutoDock Bias with Solvent Sites method. This approach analyzes the
hydroxyl and methyl groups of ethanol to identify their binding locations or hotspots using MD
trajectory data. In docking simulations, AutoDock incorporates the computed ethanol-free
energies [72], extensively studied and contrasted with earlier approaches. A novel method uti-
lizing cosolvent simulations to create 3D pharmacophore models is Pharmmaker. VS of ligand
molecules makes use of these pharmacophore models, and the druggability of several probe
compounds is evaluated using DruGUI in a case study following cosolvent simulations.
Pharmmaker assesses binding sites within protein–ligand interactions to identify the most
druggable binding site, constructing a 3D pharmacophore model for VS using snapshots of
these interactions [111].