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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5728_Библиотеки_им_академика_М_И_Перельмана
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which quickly excludes those compounds that are not considered for further
searching. The most commonly employed pre-f iltering approach is feature-
based (0D descriptor-based) matching as the calculation of these types of de-
scriptors is very fast and simple, yet very effective. It is performed by first
calculating the features of the query pharmacophore and then calculating
those features of the molecules of the chemical database. Molecules that con-
tain features similar to the query pharmacophore are retained and the rest
are excluded.
– Pharmacophore matching: Once the pre-filtering is applied to the chemical
database, the next step of the database searching is to perform the 3D align-
ment of the molecules on the query pharmacophore. The molecules that align
well with the query pharmacophore are considered hits and the rest are dis-
carded. Pharmacophore matching is the most crucial step of any pharmaco-
phore-based virtual screening as the accuracy of this step determines the
quality of the overall screening process.
Fig. 14.6: Basic steps involved in the process of pharmacophore-based virtual screening.
14.3 Classification of virtual screening 329
The most common approach for identifying the possible hits from the chemi-
cal database is to calculate the Root Mean Square Deviation (RMSD) value of the
test molecule and the query pharmacophore, when aligned together. A threshold
value of less than 01 of RMSD is considered acceptable for selecting a molecule in
the pharmacophore matching.
There are several programs/software available for performing the pharmaco-
phore-based virtual screening, which employs different approaches for pharma-
cophore matching but ultimately serves the same purpose of identifying the
probable hits from the chemical database. The most popular software employed
for this purpose are depicted in Tab. 14.4.
c. Hit-list analysis: The overall quality of the pharmacophore-based screening is as-
sessed through certain parameters, which include:
i. Sensitivity: The sensitivity of the screening process is defined as the ratio of the
true positives to the total active compounds as per the following equation. Its
Tab. 14.4: List of some popular software for pharmacophore-based virtual screening.
S. no. Name of the
program
Description
Phase of
Schrodinger
– It offers two types of searching modes, i.e., standard or on-the-fly
approach.
– In the standard approach, predefined conformations of the test molecules
are used for alignment.
– The on-the-fly approach module allows for generating new conformations
of the molecules.
– Molecules are ranked based on their fitness score.
Catalyst – Similar to the phase program, it also offers standard and best flexible
search mode for the generation of conformations.
– The RSMD calculation is employed for the selection of hits from the
chemical database, based on the alignment.
LigandScout – All conformations that can match with the query pharmacophore are
considered.
– A pattern-matching approach is used for the molecular alignment
process.
– Molecules are ranked based on the RMSD score or molecular volume
overlap score.
MOE – Alignment is done with predefined conformations of the dataset as no
new conformation can be generated.
– Annotation points are created from the pharmacophore generated from
the database molecules.
– The ranking is done based on the matching of the database molecule’s
annotation points with that of the query pharmacophore.
330 Chapter 14 Virtual screening approaches in the computer-aided drug designing
value ranges from 0 to 1, where a value of 0 indicates that the screening process
is not sensitive while a value of 1 indicates that the process of virtual screening is
highly sensitive in retrieving the actives:
Sensitivity =
TP
TP
+ FN (14:2)
where TP represents the true positives detected by the software and FN is the
false negatives detected by the screening algorithm.
ii. Specificity: It is another parameter for the analysis of the quality of the screening
algorithm, which is defined as the ratio of the detected true negative to the sum of
thedetectedtruenegativeandfalsepositivesbythealgorithm(equation(14.3)).Its
valuealsorangesfrom0to1,where1represents the highly specific algorithm:
Sensitivity =
TN
TN
+ FP (14:3)
where TN is the detected true negative and FP is the false positive detected by the
algorithm.
iii. Yield of actives : Yield of actives is the ratio of the detected true positive s de-
tected by the program to the total number of compounds present in the chemical
database as per the following equation. The active value yield can be used for
comparing the performance of two or more pharmacophore screening algorithms
when screening is performed on the same chemical database:
Yield of actives =
TP
n
(14:4)
where TP is the detected true positives and n represents the total number of com-
pounds, including their all conformations present in the chemical database.
14.3.2.3 Identification of the hits
This is the final step of the ligand-based virtual screening approach in which probable
hits are identified based on the above screening approaches. Optimization of the hits
is further performed to convert them further to possible lead molecules.
14.3.2.3.1 Advantages of the ligand-based virtual screening approach
– Utilization of known ligands: Ligand-based virtual screening leverages known
ligands with desired pharmacological properties as reference molecules for
screening large compound libraries, facilitating the identification of structura lly
similar compounds with potential biological activity.
– Efficient filtering of compound libraries: Ligand-based virtual screening effi-
ciently filters large compound libraries to prioritize molecules with high similarity
14.3 Classification of virtual screening 331
to known active ligands, reducing the number of compounds requiring experimen-
tal testing and conserving resources.
– Flexibility in target selection: Ligand-based virtual screening can be applied to
a wide range of drug targets, including proteins, enzymes, and receptors, making
it adaptable to diverse therapeutic areas and enabling the exploration of multiple
drug design possibilities.
– Complementary to structure-based approaches: Ligand-based virtual screen-
ing complements structure-based virtual screening approaches by focusing on li-
gand similarit y rather than protein structure, allowing for the identification of
bioactive compounds with diverse modes of action and binding sites.
– Fast and cost-effective: Ligand-based virtual screening offers a rapid and cost-
effective screening method compared to experimental assays, accelerating the
drug discovery process and reducing the time and resources required for lead
identification and optimization.
– Identification of novel chemical scaffolds: Ligand-based virtual screening can
identify novel chemical scaffolds and pharmacophore features that may not b e
readily apparent from protein structures alone, expanding the chemical diversity
of potential drug candidates.
– Prediction of SAR: Ligand-based virtual screening enables the prediction of SARs
by correlating ligand similarity with biological activity data, guiding the rational
design of analogs with improved potency and selectivity.
– Accessibility of computational tools: Ligand-b ased virtual screening methods
are supported by a wide range of computational tools and software packages,
making them accessible to researchers with varying levels of expertise and
resources.
– Versatility in data types: Ligand-based virtual screening can utilize various
types of molecular descriptors, such as 2D fingerprints, 3D pharmacophore fea-
tures, and molecular fingerprints, allowing for flexibility in data representation
and analysis.
– Integration with experimental data: Ligand-based virtual screening can be in-
tegrated with experimental data, such as bioassay results and ADMET (absorp-
tion, distribution, metabolism, excretion, and toxicity) properties, to enhance the
predictive power of virtual screening models and prioritize compounds with fa-
vorable drug-like properties.
14.3.2.3.2 Limitations of the ligand-based virtual screening approach
– Dependence on available ligand data: Liga nd-bas ed virtual screen ing relies
heavily on the availability and quality of ligand data for constructing pharmaco-
phore models or similarity searches. Limited or biased ligand datasets can lead to
inaccurate predictions.
332 Chapter 14 Virtual screening approaches in the computer-aided drug designing
– Lack of structural information: Unlike structure-based methods, ligand-based
approaches do not utilize structural information about the target protein, which
can limit their ability to identify novel ligands that bind to different binding sites
or exhibit allosteric effects.
– Sensitivity to ligand conformation and flexibility: Ligand-based methods may
struggle to accurately predict ligand–receptor interactions when dealing with flexi-
ble ligands or binding site flexibility. Conformational changes in ligands and recep-
tors can lead to false negatives or inaccuracies in binding affinity predictions.
– Difficulty in handling diverse chemical spaces: Ligand-based methods may
struggle to identify structurally diverse ligands that do not resemble the com-
pounds in the training set used to generate the pharmacophore models or similar-
ity matrices.
– Limited prediction accuracy: The predictive accuracy of ligand-based virtual
screening methods is influenced by factors such as the quality of the training
data, choice of similarity metrics, and the reliability of the pharmacophore mod-
els. In some cases, these methods may yield false positives or false negatives.
– Inability to capture protein flexibility: Ligand-based approaches typically do
not account for protein flexibility, which can influence li gand binding affinity
and specificity. This limitation may lead to inaccuracies in predicting ligand–receptor
interactions.
– Challenge of handling multi-target ligands: Ligand-based methods may strug-
gle to identify ligands that interact with multiple targets or exhibit polypharma-
cology. Designing ligands with desired selectivity profiles can be challenging
using ligand-based virtual screening alone.
– Risk of overfitting: Overfitting occurs when ligand-based models capture noise
or spurious correlations in the training data, leading to poor generalization per-
formance on unseen data. Careful validation and cross-validation procedures are
necessary to mitigate this risk.
– Computational resource intensiveness: Ligand-based virtual screening meth-
ods can be computationally intensive, particularly when dealing with large ligand
databases or complex similarity calculations. This may limit their practical utility
for high-throughput screening applications.
– Challenges in predicting binding modes: Ligand-based methods may struggle to
accurately predict the binding mode or orientation of ligands within the target
binding site, which is critical for understanding structure-activity relationships
and rational drug design.
14.3 Classification of virtual screening 333
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References 335
Index
0D-QSAR 19
1D-QSAR 21
2D-QSAR 11, 21, 74, 77, 80, 83, 105
3D-QSAR 12, 22, 28, 80–83, 99, 105, 133
4D-QSAR 23
5D-QSAR 23
6D-QSAR 24
ab initio method of loop modeling 284
absorption, distribution, metabolism, and
excretion 221
absorption, distribution, metabolism, excretion,
and toxicity 13, 221
acid–base dissociation constant 225
ADME 221
ADMET 2, 13, 44, 221, 240, 262
apparent volume of distribution 229
artificial evolution approach 282
artificial intelligence 248
Austin model 1 156
Basic Local Alignment Tool 277
bioactive conformer 184
bioactive conformers 179
bioavailability 227
biological Hammet equation 27
blood–brain barrier (BBB) score 232
CADD 1–2, 5, 8, 139, 146, 166, 169, 171, 182, 192,
221, 223, 249, 265, 269, 289, 293, 297, 312, 316
Central Nervous System Multiparameter
Optimization (CNS MPO) score 231
clearance 235
clinical trials 4
cluster analysis 118
CoMFA 83–88, 90–92, 96, 99
comparative molecular field analysis 83
comparative molecular similar indices analysis 96
computer-aided drug design 1, 6, 316
computer-aided drug designing 139, 269
CoMSIA 96, 99
conjugate gradient energy minimization 168
cost analysis approach 302
Craig plot 59
de novo drug design 248, 266
de novo drug designing 262, 269
density functional theory 151
derivative-based energy minimization 163
descriptor-based similarity search 326
dipole moment 43–44
distribution 229
distribution coefficient 40
downhill simplex method 173
drug development process 2
drug-likeness 240
electronic parameters 40
empirical-based scoring 195
energy minimization 160–161, 171, 179
energy-based scoring 285
energy-based selection 309
excretion 234
exhaustive search algorithm 186
first-order energy minimization 166
Fisher’s randomization test 303
flexible docking 201
force field 145
force field-based energy minimization 285
fragment-based search algorithms 186
Free Wilson approach 28
Free-Wilson analysis 68
Free-Wilson model 74
Fujita-Ban model 71–72
genetic algorithm 189
genetic algorithm-based conformational
search 320
genetic function approximation 127
GFA 127
global minima 177, 184
global minimum 161, 178
Goodness of Hit (GH) scoring approach 303
Hammet constant 59
Hammett’s constant 41
Hammet’s constant 44
Hammet’s equation 27
Hansch analysis 54–55, 58, 61, 72–74
Hansch and Free Wilson’s approaches 28
Hansch/Free Wilson model 28
haptophore 294
Hartree-Fock calculations 149
https://doi.org/10.1515/9783111434858-015
Hartree-Fock method 152
high-throughput screening 2
Hohenburg-Kohn theorem 152
homology modeling 12, 269, 289–290
Huckel’s theory 154
hybrid methods of loop modeling 284
hydrogen bonding 255
hydrogen bonding parameters 44
hydrophobicity substituent 59
identification of the pharmacophore feature 308
incremental conformational search 320
International Union of Pure and Applied
Chemistry 12
knowledge-based scoring function 195
LBDD 6
ligand-based de novo drug designing 262
ligand-based drug designing 6
ligand-based drug designing (LBDD) 6
ligand-based pharmacophore modeling 297–298,
302, 304–305
ligand-based virtual screening 318
ligand–ligand docking 204
limited-memory Broyden-Fletcher-Goldfarb-
Shanno (L-BFGS) algorithm 169
Lipinski’s rule of five 241
lipophilicity parameters 35
local minimum 161
machine learning 248
machine learning algorithms 230
machine learning scoring functions 196
metabolism 234
MLR 112, 116, 123
molar refractivity 46
molecular docking 6, 10, 179, 182–183, 186, 192,
207, 230, 251, 269, 316, 319
molecular docking programs 199
molecular dynamics 12, 161, 179, 269
molecular dynamics simulation 6
molecular dynamics-based optimization 286
molecular mechanics 139–140, 179
molecular modeling 161
Monte Carlo methods 188
Monte Carlo search algorithm 188
Monte Carlo simulation 189
Monte Carlo-based conformational search 320
Monte Carlo-based optimization 286
Muegge (Bayer) filter of drug-likeness 241
multiple linear regression 90, 112
multiple sequence alignment 277
multivariate data analysis 118
MVDA 118, 128
Newton-Raphson method 169, 172
non-bonding energy terms 143
nonderivative energy minimization method 176
nonderivative energy minimization methods 173
Organization for Economic Co-operation and
Development 78
pairwise sequence alignment 276
parametric method 3 157
Pariser-Parr-Pople 154
partial least square analysis 122
partial least square (PLS) method 90
partial least squares 118
partition coefficient 36, 55, 224
PCA 118
Pearson’s correlation coefficient 107, 109
pharmacophore 294, 296
pharmacophore mapping 293
pharmacophore modeling 6, 12, 297, 312
pharmacophore-based screening 328
phase I clinical trials 4
phase II clinical trials 4
phase III clinical trials 4
phase IV clinical trial 5
physicochemical properties that influence
biological activities 35
plasma half-life 236
plasma protein binding 230
PLS 122
preclinical studies 2
primary structure of a protein 271
principal component analysis 90, 118
protein–ligand binding 193
protein–ligand docking 203
protein–nucleic acid docking 204
protein–protein docking 203
proximity measurement 129
338 Index
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