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14.3.1 Structure-based virtual screening
With the emergence of the high-quality 3D structure of proteins in the last few deca-
des, the process of structure-based virtual screening has gained immense popularity.
In this approach, the large database of the compounds is screened based on the active
site properties of the target protein. Molecular docking is the most commonly em-
ployed methodology for performing the structure-based virtual screening of chemical
databases. Only those docking algorithms that perform fast docking are employed for
the virtual screening due the large number of compounds to be screened [20–22]. The
workflow of the structure-based virtual screening algorithm is given in Fig. 14.3.
A detail of the steps performed in the structure-based virtual screening is given
below:
14.3.1.1 Preparation of the chemical database
For performing any virtual screening process, the biggest prerequisite is the selection
of the chemical database. Various databases that are available commercially or in the
public domain are already given in Tab. 14.1. Depending upon the requirement of the
Fig. 14.3: Basic steps involved in the process of the structure-based virtual screening process of the
compounds.
14.3 Classification of virtual screening 319
virtual screening, one can choose from different databases such as chemical data-
bases of probable cancer molecules and databases of herbal molecules.
14.3.1.2 Performing the conformational search
This is the next step of performing the structural-based virtual screening. Broadly,
conformational search algorithms employed by the docking programs in structural-
based virtual screening are of three types [23]:
a. Conformational search performed during docking: This is the most common type
of search algorithm used for structural-based virtual screening. As in the virtual screen-
ing approach, it is required that process is performed rapidly, hence only stochastic
conformational search algorithms are employed here. Monte Carlo- and genetic algo-
rithm-based search algorithms are the two most common stochastic approaches used
for this purpose.
– Monte Carlo-based conformational search: Monte Carlo-based conformational
search is gaining popularity. Initially, this approach was used in molecular dy-
namics simulations for performing energy minimization, but later it was adopted
for the conform ational search in molecular docking also. In this methodology,
first, a random conformation of the molecule is placed inside the binding site of
the protein target, and then a random change in the conformation of the molecule
is made. Energy-based parameters of both the conformations are calculated and
it is then decided whether to accept or reject the new conformation, based on the
Boltzmann probability function. This randomization in the conformation is per-
formed when conformations of certain threshold values are obtained.
– Genetic algorithms-based conformational search: Genetic algorithm-based con-
formational search is based on the adaptive heuristic search technique focused on
the process of natural selection and genetics. Initially, conformations of the ligand
are generated based on certain parameters and these parameters are considered
analogous to the genes. From the generated population of different conformations,
a process known as “crossover” of the natural selection is performed to evolve out
those conformations that have the lowest energy parameters.
b. Conformation search performed before docking: In the above approach, confor-
mational search is performed by placing the ligand inside the binding cavity of the
target protein. In this approach, all the energy calculations for obtaining the bioactive
conformations are performed first and only then low-energy conformations in rigid
form are placed inside the active site of the target protein for performing the scoring
of those poses.
c. Incremental conformational search: Incremental conformational search is also a
rapid conformational search approach employed in structural-based virtual screen-
ing. In this approach, initially, the molecule is cleaved into small fragments around
the rotatable bonds present inside it. Then, the largest rigid fragment obtained is first
320 Chapter 14 Virtual screening approaches in the computer-aided drug designing
placed inside the binding cavity of the protein. After that, each fragment is added, one
by one, to the initial fragment in different orientations and scored based on a certain
docking score. The fragment orientation that gives the best docking score is attached
to the initial fragment and this whole process is repeated until the whole molecule is
created.
Based on these above approaches, different molecular docking programs used in the
virtual screening are given in Tab. 14.2.
14.3.1.3 Scoring functions in the structure-based virtual screening
This is the next step of the structure-based virtual screening that is performed to as-
sess the binding of the different conformations with the target protein [24]. Detailed
descriptions of the different scoring functions employed for this purpose are already
discussed in Chapter 9 (scoring of the ligand–receptor binding).
14.3.1.4 Ranking based on scoring
In the previous step, various conformations obtained from the conformational search
were assessed for the binding affinity with the binding site of the target protein. As
we already know, ligand–receptor binding is evaluated based on Gibb’s free energy
(ΔG) and the ligand that has a lower ΔG value for the binding is considered to have
better binding. The molecules with lower ΔG values are ranked better compared to
other molecules in the chemical database.
14.3.1.5 Identification of the hits
Hits are those molecules that are considered to possess good pharmacological activity
against a particular target. This is the last step of the structural-based virtual screen-
ing in which first a threshold value of ΔG for the ligand–receptor binding is selected
and then the molecules that p ossess a ΔG value better than the threshold value are
Tab. 14.2: List of different molecular docking programs used in virtual screening.
S. no. Conformational search approach Molecular docking program
. Conformational search during docking (Monte Carlo
approach)
Glide, MCDOCK, ICM, Dock Vision, and
ProDock
Conformational search during docking (genetic
algorithm approach)
AutoDock, Gold, MolDock, FlopDock,
and EADock
. Conformational search before docking FRED and SLIDE
. Incremental search algorithm FLEXx, DOCK, FLOG, and SurFlex
14.3 Classification of virtual screening 321
considered as the hits against the desired target protein [25, 26]. These hit molecules
are further selected for the further drug discovery process.
14.3.1.5.1 Advantages of structure-based virtual screening
– Target specificity: Structure-based virtual screening allows for the direct target-
ing of specific biomolecular targets, such as proteins or enzymes, involved in dis-
ease pathways, enhancing the likelihood of identifying compounds with high
affinity and selectivity.
– Rational design: By utilizing the three-dimensional struc ture of the t arget pro-
tein or receptor, structure-based virtual screening enables rational drug design,
wherein potential ligands can be designed or modified to fit into the binding site
and interact optimally with the target, increasing the chances of success in drug
discovery.
– Speed and efficiency: Virtual screening techniques can rapidly screen large com-
pound libraries against target structures, enabling the evaluation of thousands to
millions of compounds in silico, significantly reducing the time and resources,
compared to traditional experimental screening methods.
– Cost-effectiveness: Virtual screening reduces the need for expensive and labor-
intensive experimental assays by prioritizing compounds with favorable binding
interactions for further experimental validation, thereby minimizing costs and in-
creasing efficiency in the drug discovery process.
– Hit expansion: Structure-based virtual screening can identify structurally di-
verse hits with varying scaffolds that interact with different regions of the target
protein, facilitating hit expansion and diversification of the chemical space for
lead optimization and drug development.
– Identification of novel binding sites: Virtual screening can reveal potential
binding sites on the target protein that may not have been previously character-
ized, leading to the discovery of allosteric modulators or alternative binding pock-
ets for drug targeting, and expanding the scope of drug discovery efforts.
– Integration with experimental data: Virtual screening results can be integrated
with experimental data, such as biochemical assays or structure–activity relation-
ship (SAR) studies, to validate and prioritize hit compounds, guiding further opti-
mization and lead identification in the drug design process.
– Enhanced hit rates: Structure-based virtual screening often yields higher hit
rates compared to other screening methods, as it focuses on compounds with fa-
vorable binding interactions and structural complementarity with the target, in-
creasing the likelihood of identifying potent and selective lead compounds.
– Flexible workflow: Virtual screening workflows are flexible and adaptable, al-
lowing researchers to customize parameters, scoring functions, and screening
protocols based on the specific requirements of the target and project goals, opti-
mizing the screening process for maximum effectiveness.
322 Chapter 14 Virtual screening approaches in the computer-aided drug designing
– Insight into SAR: Virtual screening provides valuable insights into SAR by eluci-
dating the molecular interactions between liga nds and target proteins, guiding
the rational design and optimization of lead compounds with the desired pharma-
cological properties.
14.3.1.5.2 Limitations of structure-based virtual screening
– Dependence on protein structure availability: Structure-based virtual screen-
ing relies heavily on the availability of high-quality protein structures. The lim-
ited availability of experimentally determined protein structures for the target of
interest can constrain the application of structure-based virtual screening.
– Inaccuracies in protein structure prediction: Predicting protein structures
computationally can introduce inaccuracies and uncertainties, affecting the reli-
ability of structure-based virtual screening results. Inaccurate or incomplete pro-
tein structures may lead to false-positive or false-negative predictions.
– Binding site flexibility: Structure-based virtual screening often assumes a rigid
protein–ligand binding site, neglecting the inherent flexibility and dynamics of
protein structures. This oversimplification can result in overlooking potential li-
gand-binding modes or interactions, leading to suboptimal ligand selection.
– Scoring function limitations: The accuracy of structure-based virtual screening
results heavily depends on the scoring functions used to evaluate ligand–protein
interactions. Existing scoring functions may not fully capture the complexities of
molecular recognition, leading to inaccuracies in ranking and prioritizing
ligands.
– Chemical diversity of compound libraries: Structure-based virtual screening
requires large c ompound libraries for screening, but the diversity of available
chemical compounds may be limited. Inadequate chemical diversity in compound
libraries can restrict the identification of novel lead compounds with diverse
chemical scaffolds.
– Computational resources and time constraints: Performing structure-based
virtual screening involves computationally intensive tasks, such as molecular
docking and scoring, which require significant computational resources and time.
Limited computational resources and time constraints may hinder the screening
of large compound libraries or thorough exploration of the conformational space.
– Overlooking water-mediated i nteractions: Structure-based virtual screening
often neglects water-mediated interactions within the protein–ligand binding
site. Water molecules play a crucial role in mediating ligand binding and influ-
encing binding affinity, but their consideration adds complexity to the docking
process.
– Ignoring protein flexibility during docking: Structure-bas ed virtual screening
typically assumes a static protein conformation during docking, overlooking the
conformational flexibilit y of protein structures. Ignoring protein flexibil ity may
14.3 Classification of virtual screening 323
lead to inaccurate predictions of ligand binding and affinity, particularly for flexi-
ble binding sites.
– Validation and experimental confirmation: Structure-based virtual screening
predictions require experimental validation to confirm the binding affinity and
biological activity of the identified lead compounds. Experimental validation is
essential but time-consuming and costly, limiting the throughput and efficiency of
structure-based virtual screening.
– Addressing ligand-specific issues: Certain ligand-specific issues, such as covalent
binding, met al coordination, or allosteric modulation, may pose challenges for
structure-based virtual screening. Specialized docking protocols or scoring func-
tions may be required to address these ligand-specific interactions accurately.
14.3.2 Ligand-based virtual screening
When virtual screening is performed based on known active ligands present against
the target disease, it is known as the ligand-based virtual screening approach. In this
technique, a chemical database is searched for the potent molecules that are closely
related to the active known ligands [27]. The screening for the search of potent mole-
cules is done based on either the similarity with known ligands (2D and 3D) or in
close relation with the pharmacophore developed from the active ligand. Ligand-
based virtual screening works on the principle of similarity, i.e., compounds that pos-
sess similarity probably will have similar biological activity [28]. The complete work-
flow of the ligand-based virtual screening is given in Fig. 14.4.
Fig. 14.4: Basic steps involved in the process of the ligand-based virtual screening process of the
compounds.
324 Chapter 14 Virtual screening approaches in the computer-aided drug designing
14.3.2.1 Curation of the chemical dataset
This is the very first step of any virtual screening approach, whether it is structure-
based or ligand-based. Details of the chemical database are already discussed in the
earlier section of the chapter and various databases are given in Tab. 14.1.
14.3.2.2 Screening of the chemical database
This is the next step of the ligand-based virtual screening methodology. Based on the
different screening methodologies, the ligand-based virtual screening is classified fur-
ther. The most common screening algorithms employed in ligand-based virtual screen-
ing are similarity-based and pharmacophore-based screening.
a. Similarity search-based screening: Similarity search-based screening is the most
common method employed for the ligand-based virtual screening technique. The
chemical database in this approach is screened based on certain similarities. Similar-
ity-based screening is done by one of the following methods.
i. Small- molecule alignment-based similarity search: In small-molecule alignment-
based similarity search, each molecule of the chemical database is aligned over the
reference active known ligand. There are several molecular alignment programs
available that perform this task based on various algorithms. In this approach, the
test molecule is always kept flexible whereasthereferenceactiveligandcaneither
be kept rigid or flexible during the alignment. The most popular programs available
for performing the molecular-based similarity search are given in Tab. 14.3.
Tab. 14.3: List of popular programs available for performing the molecular-based similarity search.
S. no. Name of the
software/
program
Principle Flexibility of the
reference active
ligand
Description
. FlexS Molecular
superposition
Rigid – The test molecule is divided into small
fragments.
– Individual fragments are aligned on
the reference ligand incrementally.
. GASP Molecular
superposition
Flexible – This program is based on the Genetic
algorithm.
– Alignment is based on the mapping of
the features (Hydrogen bond
acceptor, ring center, etc.) between
the reference and test molecule.
14.3 Classification of virtual screening 325
ii. Descriptor-based similarity search: The one major drawback of the alignment-
based similarity search is that each test molecule is to be aligned with the reference
molecule, which is a very time-consuming affair. To overcome this, descriptor-
based similarity se arch approach is employed. In this approach, the descriptor
value of the reference molecule is compared with the values of each molecule of
the chemical database to search for similarity. The descriptor can be defined as the
representation of the properties of the molecule in a mathematical term, which is
calculated by a certain algorithm. For performing the similarity-based search based
on the descriptors, mostly following types of descriptors are employed:
– 1D descriptors: These are the descriptors that can be calculated easily from
the structural properties of the molecule. The most common examples of the
1D descriptors are the number of hydrogen bond acceptors/donors, the num-
ber of rotatable bonds, log P, and so on. The role of the 1D descriptors in the
virtual screening is mainly limited to the initial filtering of the chemical data-
base (such as Lipsinki’s Rule of Five).
– 2D descriptors: These are the descriptors that can be obtained from the
graphical 2D representation of the molecule. These descriptors are most com-
monly employed for performing ligand-based virtual screening processes.
Virtual screening, with the use of these descriptors, is based on either the
classical linear 2D descriptors or tree-type 2D descriptors.
Tab. 14.3 (continued)
S. no. Name of the
software/
program
Principle Flexibility of the
reference active
ligand
Description
. MEP Alignment based
on force fields
Flexible – It is also based on the Genetic
algorithm approach.
– Alignment of the molecular fields of
the test and reference molecule is
performed to assess the similarity
between them.
. MIMIC Alignment based
on force fields
Rigid – This is more advanced than the MEP
program.
– Gaussian function is applied in the
alignment, in addition to the MEP
parameters in this program.
. fFLASH D fragment-
based molecular
superposition
Rigid – This program is very fast for small-
and medium-sized molecules.
– The alignment is based on the
graph-based clique detection
technique.
326 Chapter 14 Virtual screening approaches in the computer-aided drug designing
– Classical linear 2D descriptors: These are the classical 2D descriptors that
are calculated using traditional algorithms such as topological or structural
parameters. Examples of these types of descriptors include the number of ar-
omatic rings available in the molecule, connectivity index and carbo index,
eigenvalue-based descriptors, etc. The one major drawback with the use of
linear 2D descriptors is that it can perform a similarity search between com-
pounds that share common structural features. Molecules that have struc-
tural differences but have similar biological activity cannot be screened
using the classical linear 2D descriptors.
– Tree-type descriptors: To overcome the drawback of the similarity search
using linear 2D descriptors, tree-type 2D descriptors were introduced [29]. These
descriptors were first introduced by Rarey and Dixon in their approach of “Free
tree (Ftree)” descriptors (Fig. 14.5). These descriptors are represented by a tree-
like structure in which the nodes of the tree represent the fragments or func-
tional groups of the molecule and the graph (tree) represents the overall topol-
ogy of the molecule. When searching for similarity between the two molecules,
their Ftree descriptors are compared – the comparison between the nodes finds
similarity between the functional groups, and the comparison between the over-
all trees finds similarity between the overall molecular structures of the two
compounds.
Fig. 14.5: Description of the free tree (Ftree) type of descriptors.
14.3 Classification of virtual screening 327
iii. Similarity coefficients for finding the similarity between two molecules:
When 2D descriptors are employed for virtual screening, the similarity between
the reference and test molecule is evaluated based on various similarity coeffi-
cients. The most commonly employed similarity coefficient for this purpose is the
Tanimoto coefficient, given in the following equation. Tanimoto coefficient is de-
fined as the ratio of the common features between the two molecules to the total
number of features between those two molecules. Mathematically,
Tanimoto coefficient TðÞ= c= a + b − cðÞ (14:1)
where c is the features set that is common in both molecules A and B, a is the
feature set of molecule A, and b is the feature set of molecule B. The two mole-
cules that show a Tanimoto coefficient value greater than 0.85 are considered
similar and a value below this leads to the inference that the two molecules are
not similar:
– 3D descriptors: These descriptors are based on or calculated from the 3D
spatial arrangement of the molecules. In the ligand-based virtual screening,
linear 3D descriptors play a role, which includes distance between the func-
tional groups in space and basic molecular shape properties. These 3D linear
descriptors are evaluated for a fixed conformation but it is a well-established
fact that a ligand interacts with the target protein in a flexible conformation
and also the conformation that binds with the binding site of the target pro-
tein can have a totally different conformation. To overcome this, there is an-
other approach of employing multiple conformations of a single molecule for
generating the 3D linear descriptors but this leads to huge assumptions in the
calculations. Due to all these reasons, ligand-based virtual screeni ng, based
on these 3D descriptors, is of limited use.
b. Pharmacophore-based screening: Apart from similarity-based screening, another
important approach for screening the chemical database is pharmacophore-based
screening. A complete workflow of a pharmacophore-based screening is given in
Fig. 14.6.
i. Generation of the pharmacophore: This is the very first step of the pharmaco-
phore-based approach in which a query pharmacophore is generated. This query
pharmacophore can be generated either from the active ligands against a particu-
lar target or from the internal ligand present in the binding site of the target pro-
tein. The complete methodology of the generation of these pharmacophores is
already discussed in Chapter 13 of this book.
ii. Database searching: Database searching is the second step of this process in
which a curated chemical database is searche d for probable actives. Database
searching is a two-step process:
– Pre-filtering of the chemical database: This is the initial step of the database
searching in which quick pre-filters are applied to the chemical database,
328 Chapter 14 Virtual screening approaches in the computer-aided drug designing
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