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The second approach is to deduce the pharmacophore features from the apo-
structure of the target protein. It is a tedious task as compared to the previous one. In
this approach, initially, the active cavity of the target protein is identified, followed by
the clustering of the hotspots inside the binding cavity where interaction with the li-
gand can occur. These hotspots are then combined to identify all the pharmacophoric
features required of a potent ligand. FLAP, GBPM, Pocket V2, Snooker, and Sybyl are
popular programs that elucidate the pharmacophore features from the active site of
the target protein.
13.4.2.4 Selection of the pharmacophoric features
Once all the possible pharmacophore features are identified, the next step is to select
only those features that are important and necessary for the possibly potent ligand.
Careful selection of only those features is required at this step that correlate well with
the biological activity. The different approaches used for this purpose include the
following.
a) Energy-based selection: Energy-based selection is one of the most popular meth-
ods for the selection of the desired pharmacophoric features in the modeling of
the pharmacophore. In this approach, if the ligand-bound inside the active cavity
of the protein is known, then probe docking or molecular dynamics-based scoring
is performed for selecting the desired pharmacophore features. This energy-
based scoring based on probe docking or dynamics simulations is performed at
different sites of interaction of the bound ligand within the cavity of the protein.
The features or points that show highly favorable interactions, based on energy
calculations, are then selected for developing the pharmacophore.
If the protein structure is devoid of internally bound ligands, and pharmaco-
phore features are identified from the cavity of the apo-protein, then the hydra-
tion site analysis approach is applied. This approach is based on the principle that
functional groups that can replace water from the active cavity of the protein
structure contribute significantly to the generation of the potent ligand and thus
become an indispensable member of the pharmacophore model.
b) Selection if more than one internally bound ligand is available for the target
protein: This approach is employed if there is more than one internal ligand avail-
able for the same target protein. In this approach, the individual pharmacophore ob-
tained from each of the internal ligands is superimposed and aligned with each
other, and the common features are selected for obtaining the significant pharmaco-
phore model. SQUID and FLAP are the two most popular algorithms that employ this
approach for the selection of the pharmacophore features.
c) Selection based on making changes in the amino acid residues inside the
binding cavity of the protein: In this approach, amino acids present inside the
binding cavity of the protein are changed with other amino acids and its impact
on the binding of the ligand is then assessed. Amino acids that contribute signifi-
13.4 Types of pharmacophore modeling (mapping) 309
cantly to the l igand binding are then studied further and only those pharmaco-
phoric features that satisfy the requirement of those amino acids are selected to
obtain a prominent pharmacophore.
13.4.2.5 Refinement of the pharmacophore model
This is the last step of the structure-based pharmaco phore modeling appr oach. Till
now, we have studied the development of the pharmacophore model by selecting
only those features that corr elate well with the biological activity. In this approach,
the final refinement of the developed model is performed to develop a robust and sig-
nificant pharmacophore model. Approaches employed for the refinement of the phar-
macophore include the following.
a) Use of active ligands for the training of the structure-based pharmacophore
model: The active ligand approach is very useful for the further refinement of
the pharmacophore model. This not only enhances the robustness of the pharma-
cophore model but also reduces the detection of false positives through virtual
screening. In this methodology, screening of the known active ligands is per-
formed on the developed pharmacophore and the ability of the pharmacophore is
assessed to identify them as active. Any refinement, if required, is done on the
pharmacophore model, based on the screening results of the active ligands. The
Ligand profiler of the Acclerys program performs this task very efficiently.
b) Applying shape constraints to the pharmacophore model: Shape, volume, and
geometry are crucial parameters in any of the CADD approaches. Based on the
shape and geometry requirement of the binding site of the target protein, a shape
restrain can be applied to the final pharmacophore model developed. The phar-
macophore features that are in accordance with the shape restraints are included
in the final refined pharmacophore and those that are outside the shape restrains
are excluded.
13.4.2.6 Advantages of structure-based pharmacophore modeling
Structure-based pharmacophore modeling is a powerful approach in CADD, offering a
structured and rational methodology for designing drugs with improved specificity,
affinity, and overall efficacy. It has become an essential tool in the arsenal of drug
discovery, contributing to the development of more effective and safer medications:
– Informed drug design: Structure-based pharmacophore modeling leverages the
3D structure of a target protein, enabling a more informed and precise drug de-
sign process. This approach can account for the specific structural features and
binding sites of the target.
– Understanding binding interactions: It provides insights into the key interac-
tions between ligands and target proteins. This understanding is crucial for de-
signing molecules that can mimic or disrupt these interactions effectively.
310 Chapter 13 Pharmacophore mapping in computer-aided drug designing (CADD)
– Enhanced specificity: By considering the target’s structure, structure-based
pharmacophore modeling can lead to the development of highly specific ligands,
minimizing off-target effects, and improving the safety profile of drugs.
– Rational ligand optimization: This technique allows for the rational optimiza-
tion of lead compounds. Researchers can modify existing ligands to enhance bind-
ing affinity and selectivity by matching them with the target’s 3-D structure.
– Virtual screening efficiency: It significantly improves the virtual screening pro-
cesses by eliminating compounds that do not fit the pharmacophore model, re-
ducing the number of compounds that need to be experimentally tested.
– Binding site prediction: Structure-based pharmacophore modeling can help pre-
dict potential binding sites on the target protein, which is valuable for identifying
allosteric binding sites or other druggable regions.
– Ligand–receptor flexibility: This approach accounts for the flexibility of both
ligands and target proteins, which is critical in understanding how ligands can
adapt to the binding pocket.
– Acceleration of lead discovery: It accelerates the lead discovery process by pro-
viding a structural basis for identifying potential drug candidates. This saves time
and resources in the drug development pipeline.
– Personalized medicine: Structure-based pharmacophore modeling can be ap-
plied in the context of patient-specific target proteins, contributing to personal-
ized medicine and tailored drug therapies.
– Diverse applications: Beyond drug discovery, this technique finds applications
in understanding protein–protein interactions, enzyme– substrate interactions,
and other molecular recognition events.
13.4.2.7 Limitations of structure-based pharmacophore modeling
Structure-based pharmacophore modeling, while a valuable technique in CADD, has
its limitations that should be considered. Some of these limitations include:
– Dependence on available structural data: Structure-based pharmacophore
modeling relies on the availability of experimentally determined protein–ligand
complex structures. If such structures are lacking for the target of interest, the
method cannot be applied effectively.
– Static representation: It typically provides a static representation of the ligand-
binding site. It may not capture the conformational flexibility of the binding site or
ligands, which can be crucial for accurately predicting ligand–receptor interactions.
– Inaccurate complex structures: Experimental structures can sometimes contain
inaccuracies, especially when obtained through X-ray crystallography or NMR. In-
accurate structural data can lead to flawed pharmacophore models.
– Limited coverage: Structure-based pharmacophore modeling is limited to known
ligands and target structures. It may not identify novel ligands or alternative bind-
ing modes that were not present in the training set.
13.4 Types of pharmacophore modeling (mapping) 311
– Sensitivity to water molecules: Water molecules in the binding site can influ-
ence ligand–receptor interactions. However, these are often neglected in pharma-
cophore modeling, potentially leading to inaccurate predictions.
– Challenging for multitarget pharmacophores: Creating pharmacophores for
multiple targets (polypharmacophores) can be complex and may not capture all
the nuances of different binding sites.
– Computationally intensive: Generating accurate structure-based pharmaco-
phores can be computationally intensive, especially for large protein–ligand com-
plexes. This can limit its practicality for high-throughput virtual screening.
– Limited to protein targets: Structure-based pharmacophore modeling is primar-
ily applicable to protein targets, and it may not be suitable for other biomolecular
targets, such as RNA or DNA.
– Ignoring protein dynamics: It does not account for dynamic protein–ligand in-
teractions or induced-fit effects, which are critical for understanding binding af-
finity and specificity.
– Overlooking solvent effects: It often simplifies the binding site to a dry environ-
ment, neglecting the influence of solvent molecules and their role in ligand
binding.
Despite these limitations, structure-based pharmacophore modeling remains a valu-
able tool in CADD. Researchers should be aware of its constraints and consider com-
plementing it with other methods to enhance the accuracy and reliability of drug
discovery efforts.
13.5 Applications of the pharmacophore modeling approaches
Pharmacophore modeling is a valuable tool in CADD, providing insights into molecular
interactions and accelerating the drug discovery process. Its diverse applications make
it a fundamental component of modern drug development efforts. Pharmacophore
modeling is a versatile and powerful approach in CADD, offering various applications
in the drug discovery process. Below are some key applic ations of pharmacophore
modeling in CADD:
– Virtual screening: Pharmacophore models are used to screen large chemical da-
tabases, identifying compounds that match the pharmacophoric features. This ac-
celerates the identification of potential drug candidates.
– Lead compound identification: Pharmacophore modeling helps in the discovery
of lead compounds by specifying the key interactions required for a molecule to
bind to a specific target.
– Structure-based drug design: Pharmacophore models can be integrated with 3D
structures of target proteins to design new compounds that fit the pharmaco-
phore and interact effectively with the target.
312 Chapter 13 Pharmacophore mapping in computer-aided drug designing (CADD)
– Hit optimization: Pharmacophore models guide medicinal chemists in optimiz-
ing the properties of lead compounds, ensuring they maintain the essential phar-
macophoric features while improving their drug-like properties.
– Bioisosteric replacement: Pharmacophore modeling assists in identifying suit-
able bioisosteric replacements for certain functional groups, allowing for the de-
sign of analogs with improved properties.
– Polypharmacology studies: Pharmacophore models can be employed to predict
the interactions of compounds with multiple targets, which is essential for under-
standing potential off-target effects and designing multi-target drugs.
– Toxicity prediction: Pharmacophore modeling is used to predict the potential
toxicity of compounds by identifying features associated with toxic effects.
– Pharmacokinetics and ADMET prediction: Pharmacophore models can help es-
timate a compound’s absorption, distribution, metabolism, excretion, and toxicity
(ADMET) properties.
– Fragment-based drug design: Pharmacophore models can guide the assembly of
fragments into larger compounds with the desired pharmacophoric features.
– Personalized medicine: Personalized pharmacophore models can be developed,
based on individual patient data, allowing for the d esign of patient-specific
therapies.
– Chemical probe design: Pharmacophore modeling assists in the development of
chemical probes used to investigate the biological function of a specific target.
– Natural product-based drug discovery: Pharmacophore models can be applied
to identify natural product compounds with pharmacophoric features that make
them suitable for drug development.
– G Protein-coupled receptor (GPCR) ligand discovery: Pharmacophore model-
ing is used in the discovery of ligands for GPCRs and other membrane proteins.
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Chapter 14
Virtual screening approaches in
the computer-aided drug designing
Virtual screening is now an integral part of the drug dis covery process, offering rapid and
cost-effective method for identifying potential drug candidates. – Wolfgang Sippl
14.1 Introduction
The cornerstone of any drug design approach is the development of a potent chemical
entity with the desired pharmacological profile. In the preceding chapters, various
methodologies of computer-aided drug design (CADD) have been explored, including
molecular docking, quantitative structure–activity relationship (QSAR, 2D and 3D),
pharmacophore modeling, and de novo design. These methodologies are instrumental
in achieving the aforementioned objective. Virtual screening can be understood as the
application of these established CADD methodologies to identify potent drug molecules.
Most simply, virtual screening can be defined as the in silico process used in the
early stages of drug discovery for finding out or identifying potential lead molecules
or compounds from the pool of a chemical database by employing any knowledge-
based screening approaches such as molecular docking, pharmacophore-based simi-
larity, and QSAR-based similarity. The concept of virtual screening can be best under-
stood by comparing it with the process of filtration using a funnel (Fig. 14.1). A large
pool of molecules is allowed to pass through a funnel having certain filters and the
molecules that do not hold good with the applied filters are excluded and those that
match the filter requirement pass and emerge out as the possible hits [1–3]. The term
“virtual screening” was first used by Hovarth D in the year 1997 in his publication [4].
Before diving deep into the concept and methodology of virtual screening, one must
have clear knowledge of the different chemical databases available for performing
the virtual screening process.
14.2 Chemical databases for the virtual screening in CADD
Chemical databases are the libraries of either small fragments, lead-like, nearly drug-
like, or drug-like compounds that are either available commercially or in the open-
source domain. Irrespective of the virtual screening technique, these chemical data-
bases can be used for finding our leads for a particular drug target. The detail of the
different types of chemical databases available is given in Tab. 14.1.
https://doi.org/10.1515/9783111434858-014
Conformer
generation
ADME
Docking
simulations
Pharmacophore
screening
Experimental
validation
Database of Molecules
Hit Compounds
(with potential activity)
Fig. 14.1: Overview of the concept of virtual screening for finding the active hit compounds from a
database.
Tab. 14.1: List of available databases.
S. no. Name of the
database
Link Availability Approximate
no. of available
compounds
Zinc
database
https://zinc.docking.org/ Open-
source
million []
ChemDB
database
https://cdb.ics.uci.edu/ Open-
source
million []
Pubchem
database
https://pubchem.ncbi.nlm.nih.gov/ Open-
source
million []
Chemspider
database
https://www.chemspider.com/ Open-
source
million []
Drugbank
database
https://go.drugbank.com/ Open-
source
. million []
NCI database https://cactus.nci.nih.gov/download/roadmap/ Open-
source
. million []
14.2 Chemical databases for the virtual screening in CADD 317
14.3 Classification of virtual screening
Virtual screening is also classified similar to the classification of the CADD process
[17–19]. Broadly, virtual screening is classified into two types: structure-based virtual
screening and ligand-based virtual screening (Fig. 14.2).
Tab. 14.1 (continued)
S. no. Name of the
database
Link Availability Approximate
no. of available
compounds
Chembl
Database
https://www.ebi.ac.uk/chembl/ Open-
source
. million []
eMolecules
database
https://www.emolecules.com/ Commercial million []
Chemspace
database
https://chem-space.com/search Commercial . billion []
Maybridge
database
https://www.thermofisher.in/chemicals/en/
brands/maybridge.html
Commercial , []
ChemDiv
database
https://www.chemdiv.com/drug-discovery-serv
ices/in-silico-cadd-drug-discovery-services/
Commercial . million []
Asinex
database
https://www.asinex.com/screening-libraries-(all-
libraries)
Commercial . million []
Fig. 14.2: Classification of the virtual screening technique into the structure- and ligand-based virtual
screening methods.
318 Chapter 14 Virtual screening approaches in the computer-aided drug designing
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