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5.5.2 Docking Algorithms and Scoring Functions
Docking algorithms serve as computational methodologies designed to explore the conformational
space of ligands within the binding site of receptors. These algorithms meticulously search for
ligand poses that optimize binding interactions, taking into account crucial factors such as
hydrogen bonding, electrostatics, and van der Waals forces. To assess and rank the quality of
docking poses, scoring functions are employed, which evaluate predicted binding energy
or affinity. Lower energy scores typically indicate more favorable binding interactions,
guiding researchers toward the most promising ligand–receptor complexes for further
investigation [34, 36–41].
5.5.3 Validation of Docking Results
Validation is integral to evaluating the accuracy and reliability of docking outcomes in drug discov-
ery. Experimental validation involves laboratory testing of predicted ligand–receptor interactions,
often utilizing techniques like X-ray crystallography or binding assays to confirm the predicted
binding modes. Additionally, cross-validation procedures involve docking studies on datasets with
known binding affinities, enabling assessment of the predictive capabilities of the docking method.
Visualization and analysis of docking poses and interactions further contribute by offering insights
into the specific ligand–receptor binding modes, facilitating the refinement and optimization of
drug candidate designs [6, 7, 36, 42–48] (Figure 5.3).
Ligand
Database
Small
molecule
Docking
Binding complex
Algorithm/
scoring
Hydrogen bonding
Electrostatic forces,
Vander Waals force
Generate
rank list
Possible
complex
formation
Protein
Figure 5.3 Workflow for the docking in drug discovery process.
104
5.6 Pharmacophore Modeling
5.6.1 Concept of Pharmacophores
The concept of pharmacophores refers to a spatial arrangement of atoms or functional groups
within a molecule that is responsible for its biological activity by interacting with specific structural
features of a target receptor or enzyme. Pharmacophores represent the essential elements of a
molecule that contribute to its pharmacological effects, such as binding affinity and selectivity.
Understanding pharmacophores is crucial in drug design and discovery [41], as it helps in identifying
and optimizing compounds with desired biological activities [46].
A pharmacophore model typically consists of specific features, including hydrogen bond donors
and acceptors, hydrophobic regions, aromatic rings, and positively or negatively charged groups,
which mimic the complementary binding sites on the target protein or receptor. These features are
often represented as points in three-dimensional space, defining the spatial arrangement and ori-
entation required for optimal binding to the target.
Pharmacophore-based drug design involves the identification of pharmacophoric features in
known active compounds or ligands and the subsequent use of these features to search chemical
databases for potential new drug candidates with similar pharmacological properties.
Pharmacophore modeling techniques, such as ligand-based or structure-based approaches, play a
vital role in rational drug design by guiding the selection and optimization of lead compounds with
improved potency and selectivity [49].
5.6.2 Generating Pharmacophore Models
Generating pharmacophore models is a crucial step in computer-aided drug design, where the aim
is to identify and characterize the key structural and chemical features of biologically active mole-
cules. Pharmacophore models provide valuable insights into the molecular interactions between
ligands and their target receptors, guiding the design of novel compounds with optimized pharma-
cological properties. The process of generating pharmacophore models involves several steps [50–52].
1) Data collection: Biologically active compounds with known activities against a specific target
are collected from experimental assays or databases.
2) Structural alignment: The three-dimensional structures of the collected ligands are aligned
or superimposed to identify common structural motifs or pharmacophoric features.
3) Feature mapping: Pharmacophoric features such as hydrogen bond donors, acceptors, aromatic
rings, and hydrophobic regions are identified within the aligned ligand structures. These features
represent the essential chemical interactions required for binding to the target receptor.
4) Pharmacophore generation: Based on the identified pharmacophoric features, a pharmaco-
phore model is generated. This model represents a spatial arrangement of these features in
three-dimensional space, defining the essential chemical properties necessary for ligand bind-
ing and biological activity [53].
5) Validation and optimization: The generated pharmacophore model is validated using vari-
ous techniques, such as cross-validation or decoy testing, to assess its predictive power and
robustness. Optimization of the model may involve refining feature positions, adjusting feature
types, or incorporating additional constraints based on experimental data.
6) Virtual screening and lead identification: Once validated, the pharmacophore model is
used for VS of compound databases to identify potential lead compounds that match the
pharmacophoric features of the model. Hits obtained from VS can then be further evaluated
experimentally for their biological activity and drug-like properties.
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By elucidating the structural requirements for ligand–receptor interactions, pharmacophore
models play a crucial role in rational drug design, facilitating the discovery of novel therapeutic
agents with improved potency and selectivity.
5.6.3 Applications in Lead Discovery
Pharmacophore modeling is a pivotal tool in lead discovery and drug design, facilitating the iden-
tification and optimization of lead compounds with desired pharmacological properties by eluci-
dating the essential structural and chemical features necessary for ligand–receptor interactions.
This computational technique plays a crucial role in rational drug design, guiding the selection
and optimization of candidate compounds with improved potency, selectivity, and pharmacoki-
netic profiles. Pharmacophore modeling involves the systematic identification and characteriza-
tion of key chemical features within a set of biologically active compounds known as ligands or
inhibitors. These features represent the essential interactions between the ligands and their target
receptors or proteins, such as hydrogen bond donors and acceptors, hydrophobic regions, aromatic
rings, and positively or negatively charged groups. By generating pharmacophore models, research-
ers can gain valuable insights into the structural requirements for ligand binding and biological
activity. These models serve as three-dimensional representations of the spatial arrangement and
orientation of pharmacophoric features, guiding the design and optimization of novel compounds
with similar pharmacological properties.
Pharmacophore modeling is particularly useful in lead discovery, where it aids in the identification
of structurally diverse compounds that share common pharmacophoric features with known active
compounds. These models are also valuable in the VS of compound databases, enabling the rapid
identification of potential lead compounds that match the pharmacophoric features of the model.
Overall, pharmacophore modeling serves as a powerful tool in lead discovery and drug design,
offering insights into ligand–receptor interactions and guiding the rational design of novel thera-
peutic agents with optimized pharmacological properties.
Pharmacophore modeling plays a crucial role in lead discovery and drug design [18]:
● Virtual screening: Pharmacophore models are used to screen compound databases to identify
molecules with matching pharmacophoric features, potentially serving as lead compounds.
● Lead optimization: During lead optimization, pharmacophores guide structural modifications
to enhance a compound’s affinity and selectivity for the target.
● Poly-pharmacology: Pharmacophores can aid in designing compounds that interact with mul-
tiple targets, enabling the development of multitarget drugs.
● Fragment-based drug design: Pharmacophore analysis helps in fragment-based drug design
by identifying critical fragments to build upon.
● Exploring novel targets: Pharmacophores can be used to investigate the potential of existing
drugs against new targets by matching pharmacophoric features.
5.7 Quantitative Structure–Activity Relationship (QSAR)
5.7.1 Basics of QSAR
QSAR is a computational modeling technique used in drug discovery, environmental chemistry,
and toxicology to establish quantitative relationships between the chemical structure of com-
pounds and their biological activities or properties. QSAR models predict the biological activity or
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property of a compound based on its structural features, enabling the rational design and optimi-
zation of new compounds with desired activities.
The basics of QSAR involve several key concepts and steps [54–56]:
Data collection: QSAR analysis begins with the collection of a dataset comprising chemical
structures of compounds (ligands) and their corresponding biological activities or properties, typi-
cally obtained from experimental assays or literature.
Descriptor calculation: Molecular descriptors, which quantitatively represent the structural
and physicochemical properties of compounds, are calculated for each compound in the dataset.
Descriptors can include molecular size, shape, electronegativity, hydrophobicity, and various other
molecular properties.
Data preprocessing: The dataset may undergo preprocessing steps such as removing dupli-
cates, handling missing values, and scaling descriptors to ensure uniformity and improve model
performance.
Model development: QSAR models are built using statistical or machine learning (ML) tech-
niques to correlate the calculated molecular descriptors with the observed biological activities or
properties. Common modeling methods include multiple linear regression, partial least squares
regression, support vector machines (SVMs), random forests, and neural networks [57].
Model validation: QSAR models are validated using techniques such as cross-validation, exter-
nal validation with an independent test set, or bootstrapping to assess their predictive performance
and generalization ability.
Model interpretation: QSAR models provide insights into the relationship between chemical
structure and activity or property by identifying the most important descriptors contributing to the
model’s predictions. Interpretation of QSAR models helps in understanding the underlying mech-
anisms governing the observed biological activities or properties.
Model application: Once validated, QSAR models can be used to predict the activities or prop-
erties of new compounds not present in the training dataset. These predictions guide the selection
and optimization of lead compounds with desired activities or properties in drug discovery and
other applications.
QSAR models are valuable tools in computational chemistry and drug design, enabling the effi-
cient screening and optimization of large compound libraries and reducing the need for costly and
time-consuming experimental assays.
5.7.2 Model Development and Validation
In QSAR model development, the initial step involves selecting relevant molecular descriptors,
chosen based on chemical intuition and prior knowledge of their expected influence on biologi-
cal activity. Following descriptor selection, various mathematical techniques, including linear
regression, multiple linear regression, or ML algorithms, are employed to construct QSAR mod-
els that relate these descriptors to biological activity. Model validation is crucial to ensure predic-
tion reliability, achieved through partitioning the dataset into training and testing sets [58]. The
model is trained on the training set and then evaluated on the testing set to assess its predictive
accuracy. Cross-validation techniques, such as k-fold cross-validation, further enhance model
robustness by partitioning the dataset into multiple subsets for comprehensive performance
evaluation. QSAR models also provide insights into structure–activity relationships by identify-
ing which molecular features contribute most significantly to observed activity, thereby guiding
further compound design endeavors.
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5.7.3 QSAR in Virtual Screening
QSAR models are versatile tools utilized across various stages of drug discovery and development.
Integrated seamlessly into VS workflows, they aid in prioritizing compounds from vast chemical
libraries based on predicted activity against specific targets. During lead optimization, QSAR guides
chemical modifications to enhance lead compound activity and selectivity. Furthermore, QSAR
models predict absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties,
facilitating the selection of drug candidates with favorable pharmacokinetic profiles. Additionally,
these models assess compounds’ potential to interact with multiple targets, facilitating the design
of multitarget drugs with improved therapeutic efficacy and reduced side effects [57] (Figure 5.4).
5.8 Machine Learning and AI in Virtual Screening
5.8.1 Introduction to Machine Learning and AI
Introduction to ML and artificial intelligence (AI) in drug discovery involves the application of
computational techniques to analyze biological and chemical data, accelerate the drug develop-
ment process, and discover novel therapeutics. ML and AI methods play a crucial role in various
stages of drug discovery, from target identification and validation to lead optimization and clinical
trial design. In the context of drug discovery, ML and AI techniques are used to:
Target identification and validation: ML algorithms analyze biological data, such as genomics,
proteomics, and transcriptomics data, to identify potential drug targets involved in diseases. AI
methods help prioritize and validate these targets based on their relevance and druggability [57].
Structure description
Chemical properties
Molecular size
shape,
electronic
properties
chemical
substituents.
C
6
H
5
OH
OH
Statistical analysis,
correlation between
computer molecular
descriptors and observed
biological activities.
Linear regression
multiple linear regression
machine learning
algorithms
Activity
prediction
Correlation analysis
Molecular descriptor
Molecular Structure
QSAR
0D
2D
3D
Validation
Screening
Figure 5.4 Workflow for the QSAR in the drug discovery process.
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Compound screening and virtual screening: ML models predict the biological activity of
chemical compounds against target proteins using QSAR models or ligand-based approaches. VS
methods, powered by AI algorithms like molecular docking and pharmacophore modeling, enable
the rapid screening of large compound libraries to identify potential lead compounds.
Lead optimization: ML techniques aid in lead optimization by predicting the binding
affinity and ADMET properties of lead compounds. AI-driven approaches, such as generative
models and de novo design algorithms, help generate novel chemical compounds with desired
properties.
Biomarker discovery: ML and AI methods analyze multiomics data to identify biomarkers
associated with disease progression, treatment response, or adverse drug reactions. These bio-
markers can inform patient stratification and personalized medicine approaches.
Clinical trial design and patient recruitment: ML algorithms optimize clinical trial design
by analyzing patient data to identify optimal trial parameters, predict patient responses to treat-
ment, and stratify patient populations. AI-powered recruitment platforms match eligible patients
with clinical trials based on their clinical and molecular profiles.
The integration of ML and AI in drug discovery holds the promise of accelerating the identifica-
tion and development of novel therapeutics, reducing the time and cost associated with traditional
drug discovery approaches, and ultimately improving patient outcomes [16].
5.8.2 Feature Selection and Model Training
Feature selection: In the context of VS, feature selection involves choosing relevant molecular
descriptors or characteristics that best represent chemical compounds. These descriptors can
include structural, physicochemical, or biological properties. Feature selection is a crucial step as
it influences the model’s performance.
In ML and AI model development for VS, after selecting relevant features, models are trained
using datasets containing information on compound structures and their corresponding bio-
logical activities. Commonly employed algorithms include random forest, a versatile ensemble
learning method capable of handling diverse data types and providing feature importance
rankings; SVMs, effective for classification tasks such as distinguishing active and inactive
compounds; and deep learning, particularly utilizing neural network architectures like convo-
lutional neural networks (CNNs) and recurrent neural networks, which have demonstrated
effectiveness in analyzing molecular structures and properties. Model performance is then
evaluated using various metrics such as accuracy, sensitivity, specificity, and the area under
the receiver operating characteristic curve. To ensure robustness, cross-validation techniques
are often employed [59].
5.8.3 Applications in Virtual Screening
● Enhanced virtual screening: ML and AI techniques can significantly improve the efficiency
and accuracy of VS. They can prioritize compounds for experimental testing, reducing the time
and resources required.
● Multitarget screening: ML models can be designed to predict compound activities against
multiple targets simultaneously, facilitating the discovery of multitarget drugs.
● ADMET prediction: ML models are employed to predict ADMET properties of compounds,
aiding in the selection of drug candidates with favorable pharmacokinetic profiles.
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● Drug repurposing: ML and AI can identify existing drugs that may have potential for new
therapeutic uses by analyzing their chemical structures and known activities.
● Toxicity prediction: ML models can predict the toxicity of compounds, helping to eliminate
potentially harmful drug candidates early in the drug discovery process [60].
5.9 Hit-to-Lead Optimization
5.9.1 Prioritizing Hits from Virtual Screening
In the drug discovery process, after conducting VS to identify potential drug candidates from a pool
of compounds, the next critical step is hit-to-lead optimization. This phase involves prioritizing
and refining the initial “hits” obtained from VS to develop promising lead compounds. Here is an
explanation with an example:
Example: Imagine a pharmaceutical company is searching for a new drug to treat a specific
cancer type. After VS, the company has identified a set of chemical compounds that have shown
potential for inhibiting a key protein involved in cancer growth [61].
5.9.2 SAR Analysis and Iterative Design
5.9.2.1 SAR Analysis (Structure–Activity Relationship)
SAR analysis is a fundamental approach in drug discovery, focusing on elucidating the connection
between the chemical structure of compounds and their biological activity. Researchers meticu-
lously investigate how minor alterations to the chemical structure influence crucial pharmacologi-
cal properties such as potency and selectivity. By discerning these relationships, SAR analysis aids
in pinpointing structural features pivotal for eliciting the desired biological effects, thereby inform-
ing rational drug design and optimization strategies [62].
Example: In the cancer drug discovery scenario, SAR analysis involves systematically modify-
ing the chemical structure of the initially identified compounds. Researchers change functional
groups, alter substituents, or adjust the compound’s scaffold while assessing the impact on its abil-
ity to inhibit the cancer-associated protein. Through SAR analysis, they may find that specific
chemical groups enhance the compound’s potency, leading to the identification of more promising
lead candidates [7].
5.9.2.2 Iterative Design
Hit-to-lead optimization typically follows an iterative process guided by SAR insights, where com-
pounds undergo successive modifications. These modifications are based on SAR findings aimed at
enhancing the compounds’ biological activity. The modified compounds are subsequently subjected
to biological assays and testing to evaluate their improved potency and selectivity. This cycle of
design, synthesis, testing, and analysis iterates until lead compounds exhibiting the desired efficacy,
safety, and pharmacokinetic properties are identified. This iterative approach ensures the refine-
ment and optimization of lead candidates for further development as potential drug candidates.
Example: In our cancer drug discovery case, researchers synthesize and test multiple versions
of the initially identified compounds with structural changes guided by SAR insights. They dis-
cover that a specific modification significantly enhances the compound’s ability to inhibit the can-
cer protein while maintaining low toxicity. This compound becomes a lead candidate and moves
forward in the drug development process [63].
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5.9.3 ADME/Tox Considerations
5.9.3.1 ADME (Absorption, Distribution, Metabolism, Excretion)
ADME stands for absorption, distribution, metabolism, and excretion, and it represents a set
of pharmacokinetic properties that collectively determine the fate of a drug within the body.
Understanding ADME is crucial in drug discovery and development as it influences the efficacy,
safety, and pharmacokinetic profile of a drug candidate.
Absorption: Absorption refers to the process by which a drug enters the bloodstream after
administration. It involves the movement of the drug across biological barriers such as the
gastrointestinal tract (oral administration), skin (topical administration), or mucous membranes
(intranasal or sublingual administration). Factors affecting absorption include the drug’s
physicochemical properties (e.g., solubility and lipophilicity), formulation, and the presence of
transporters or efflux pumps.
Distribution: Distribution involves the transportation of a drug from the bloodstream to vari-
ous tissues and organs in the body. Factors influencing drug distribution include blood flow, tissue
permeability, protein binding, and the drug’s lipophilicity. Distribution can impact the concentra-
tion of a drug at its target site and its efficacy.
Metabolism: Metabolism, also known as biotransformation, refers to the enzymatic conversion
of a drug into metabolites, typically in the liver. Metabolism can lead to the activation or inactiva-
tion of a drug and plays a crucial role in determining its duration of action and potential for toxic-
ity. The major enzyme systems involved in drug metabolism are the cytochrome P450 enzymes
(CYPs) and phase II conjugation enzymes.
Excretion: Excretion involves the removal of a drug and its metabolites from the body, primarily
through the kidneys (urine) and liver (bile). Other routes of excretion include feces, sweat, saliva,
and exhalation. Renal excretion, facilitated by glomerular filtration and tubular secretion, is par-
ticularly important for hydrophilic and water-soluble drugs, while hepatic excretion is more rele-
vant for lipophilic drugs.
In drug discovery and development, assessing ADME properties early in the process helps
identify potential issues that may impact a drug candidate’s pharmacokinetic profile, efficacy, or
safety. Optimization of ADME properties is often pursued to enhance the bioavailability, distri-
bution, metabolism, and elimination of a drug, thereby improving its overall therapeutic profile.
Understanding ADME is essential for predicting and optimizing the pharmacokinetic behavior
of drug candidates, ultimately contributing to the selection of promising candidates for further
development and clinical evaluation [64, 65].
5.9.3.2 Toxicity Considerations
Toxicity considerations are paramount in drug discovery, as they directly impact the safety and
success of potential drug candidates. Understanding and mitigating toxicity risks is crucial to
advancing compounds through preclinical and clinical development stages.
Predictive toxicology: Employing predictive toxicology approaches early in drug discovery
helps identify potential toxicities before compounds enter clinical trials. Computational models,
such as QSAR models and ML algorithms, analyze chemical structures to predict toxicological
endpoints, such as mutagenicity, carcinogenicity, hepatotoxicity, cardiotoxicity, and nephrotoxicity.
These predictive models enable the prioritization of safer compounds for further development.
In vitro toxicity screening: In vitro assays assess the toxicological effects of drug candidates
on cellular and molecular targets, providing valuable data on mechanisms of toxicity.
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High-throughput screening platforms evaluate compound cytotoxicity, genotoxicity, and organ-
specific toxicity using cell-based assays, organ-on-a-chip models, and biochemical assays.
These assays help identify compounds with potential safety liabilities early in the drug dis-
covery process.
In vivo toxicology studies: Preclinical toxicology studies conducted in animal models evaluate
the safety profile of drug candidates before initiating human clinical trials. Acute, subacute, and
chronic toxicity studies assess systemic toxicity, organ toxicity, and potential adverse effects
following repeated dosing. Pharmacokinetic studies determine compound exposure levels and
distribution in tissues, guiding dose selection and formulation strategies.
Safety pharmacology: Safety pharmacology studies assess the potential adverse effects of drug
candidates on vital physiological systems, including the cardiovascular, central nervous, and
respiratory systems. These studies evaluate compound effects on cardiac electrophysiology,
respiratory function, and central nervous system activity to identify safety concerns that may
impact patient safety.
Regulatory requirements: Regulatory agencies, such as the Food and Drug Administration
(FDA) and the European Medicines Agency (EMA), require comprehensive toxicity data as part of
the Investigational New Drug (IND) application for clinical trials. Toxicity studies conducted in
compliance with regulatory guidelines ensure the safety of human subjects participating in clinical
trials and support drug approval processes.
By integrating toxicity considerations throughout the drug discovery process, researchers can
identify and mitigate potential safety risks early, leading to the development of safer and more
effective drug candidates for patient use [64, 66, 67] (Figure 5.5).
Virtual screening and lead discovery
Target identification
Lead discovery
Virtual screening
SBVS
(Structure-
based)
LBVS
(Ligand -
based)
Lead optimization
Preclinical evaluations
Clinical trials
Market launch
Database
Library
Hits
1. Identification of potential drug candidates
2. Examples of lead discovery
3. Impact of early lead
identification
- Utilize virtual screenign techniques.
- Conduct molecular docking simulations.
- Identification of small molecules binding to
specific protein targets.
- Discovery of natural products with
therapeutic properties through screening
plant extracts.
- Accelerates drug
development process.
- Reduces costs by
expediting
optimization
and testing
processes.
Figure 5.5 Refined workflow for employing virtual screening approaches in the drug discovery process.
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5.10 Case Studies and Examples
5.10.1 Exploration Protocol for Mutant-targeted PI3K Inhibitors
The PIK3CA gene stands out as one of the frequently mutated oncogenes in various human cancers.
It is responsible for encoding the p110α catalytic subunit of phosphatidylinositol 3-kinase, isoform
α (PI3Kα). This particular protein plays a pivotal role in orchestrating signaling cascades that govern
critical cellular processes such as proliferation, survival, and growth. Notably, a common mutation
observed in PI3Kα involves a histidine-to-arginine substitution at position 1047 (H1047R) [68]
within exon 20. Initially, the mutant H1047R PI3Kα [69] crystal structure (PDB ID: 3HIZ) was built
using a combination of homology and loop modeling techniques. Subsequently, the atomistic model
of the full-length H1047R mutant was generated utilizing Modeller 9v8. This resultant model was
then solvated in water and subjected to molecular dynamics (MD) simulations lasting approximately
70 ns, employing the NAMD package. Following the simulation, the final snapshot was extracted
and utilized for binding site identification calculations through the SiteMap module of Schrödinger
v2.4. Notably, a non-ATP binding site proximal to the H1047R mutation emerged as one of the top-
ranking sites and was thus selected for the present study. Subsequent to binding site identification,
VS was executed using the docking program Glide 5.7 [49]. The drug-like subset of compounds from
the HitFinder collection within the Maybridge database (accessible via www.maybridge.com)
served as the basis for the VS exercise [69]. Employing the glide standard precision (SP) mode, all
structures were docked and scored. Subsequently, the top 10,000 ranked structures from the SP filter
underwent redocking and rescoring utilizing the glide extra precision (XP) mode. Complexes
corresponding to the top-ranked 1000 compounds resulting from the XP processing were then
subjected to further post-processing via the ChemBioServer. To eliminate poses distant from the
energy minimum, the van der Waals filter of the server was utilized to exclude compounds with
steric clashes. The remaining compounds underwent physicochemical property filtering according
to the “Jorgensen rule of 3” [70], alongside toxicity filtering based on a database available in
ChemBioServer, which cataloged known toxic moieties. Subsequently, hierarchical clustering was
executed for the remaining compounds utilizing the Tanimoto coefficient and the ward clustering
linkage, aiming to maximize chemical diversity while minimizing visual inspection efforts. Finally,
the resulting cluster representatives underwent visual inspection, leading to the selection of the
10 most promising compounds for purchase and subsequent in vitro assay testing. Remarkably, 4
out of the 10 purchased molecules exhibited inhibition of PI3Kα protein activity in vitro at
micromolar concentrations, underscoring the successful application of the described workflow in
drug discovery [69]. The process is schematically outlined in Figure 5.6.
5.10.2 Enhancing Virtual Screening Hit Rate: Implementation on the RXRα
Nuclear Receptor
Utilizing multiple resolved protein structures of the same protein presents a promising avenue for
enhancing hit rates in biological assays of compounds chosen through in silico methods. This
strategy is particularly pertinent in the case of receptors, which constitute the primary targets for
pharmaceutical interventions. Notably, numerous crystal structures of specific receptors with both
agonists and antagonists have been documented. We advocate for the utilization of these available
structures in conjunction with the below-developed protocol to elevate the efficiency of identifying
compounds capable of modulating the target receptor effectively. In this context, we utilize the
nuclear receptor retinoid X receptor alpha (RXRα)[71, 72] as a model for a selective SBVS screening