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30
Drug Repurposing and Computational Drug Discovery: Strategies and Advances
purpose.13 In the structure-based approach the investigational compound is
tested toward a set of biological targets to determine its binding affinity.
Several software tools and databases are available to the researchers for the
purpose, namely, AUTODOCK, GOLD, GLIDE, and a few more.
14
FIGURE 2.1 Computational approaches in drug discovery.
2.2.1 LIGAND-BASED APPROACHES
Ligand-based drug design (LBDD) plays an approachable role in the drug
discovery process when no information is available regarding the three-
15
dimensional structure of potential drug targets.
Ligand-based approaches
evaluate ligands/molecular scaffolds that are known to interact with the
targets of interest. Basically, strategies employed by this method are assortment of chemical species having chemical resemblance to known ligands with
a couple of similarity measures and constructing QSAR models predicting
biological activity from the chemical makeup. Of note, this method is
primarily used for in silico design and screening of novel ligands as potential
drug candidates. The approach is a valuable tool for optimizing the ADMET
properties of drug likely candidates. Three-dimensional quantitative structure
activity relationship (3D QSAR) and pharmacophore modeling are the most
commonly used techniques in ligand-based approaches for giving predictive
31 Computational Drug Discovery and Drug Repurposing
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models for lead generation and optimization.
16
Target fishing and reverse
docking methods are also being used in the ligand-based drug discovery
process.
17
QSAR analysis is the most efficient tool in establishing a statistically significant correlation between chemical structures and their pharmacological
18
activities.
parameters known as molecular descriptors.
The structural information is defined in terms of a series of
19
These molecular descriptors
are classified as topological, geometrical, thermodynamic, electronic, and
20
constitutional types.
Table 2.1 lists out the type of software programs
available for computation of molecular descriptors. Linear regression,
multiple regressions, partial least squares, and principal component analysis
or regression are the statistical approaches covered under linear methods
of QSAR analysis. Artificial neural networks (ANN), k-nearest neighbors
(kNN), and Bayesian neural networks are the nonlinear methods of QSAR
21
modeling.
QSAR model construction involves a sequential preprocessing
and transformation of data sets division, feature/descriptor selection, model
development and validation, and finally followed by interpretation and
domain analysis.
19
To generate a perfect QSAR model, proper selection of
compounds (minimum 20 with reliable and comparable bioactivity data),
molecular descriptors for ligands with no autocorrelation to avoid over
fitting, and use of internal and external model validation methods for determining applicability and productivity is too crucial.
22
TABLE 2.1 Software for Computation of Molecular Descriptors.
Software Description Website Availability
ADAPT Geometrical, topological, http://research.chem.psu.edu/ Free
physicochemical, electronic pcjgroup/adapt.html
ADMET Constitutional, functional www.simulations-plus.com Commercial
Predictor group counts, topological,
E-state, 3D descriptors,
molecular patterns, acid–base
ionization, empirical estimates
of quantum
ALOGPS2.1 log P, log S www.vcclab.org Free
status
32
TABLE 2.1 (Continued)
Software Description Website Availability
ADRIANA. Topological, constitutional, www.molecularnetworks.com Commercial
Code functional group counts,
ACD/logP log S, log P, log D, pKa www.acdlabs.com Commercial
CDK Topological, geometrical, http://cdk.github.io Free
Dragon Topological, constitutional, www.talete.mi.it Commercial
JOElib Topological, counting, www.ra.cs.uni-tuebingen.de Free
MOE Topological, structural keys, www.chemcomp.com Commercial
MOLD2 1D, 2D www.fda.gov Free
MOLGEN- Constitutional, topological, www.molgen.molgenqspr. Commercial
QSPR geometrical, etc. html
Open 166-bit, MOLPRINT2D www.openbabel.org Free
BABEL MACCS, structural key
PADEL Molecular fingerprints, 1D, www.padel.nus.edu.sg Free
PowerMV Constitutional, atom pairs, www.niss.org/PowerMV Free
Drug Repurposing and Computational Drug Discovery: Strategies and Advances
status
E-state, meylanflags,
moriguchi molecular patterns,
3D descriptors, etc.
electronic, constitutional
2dautocorrelations,
geometrical, GETAWAY,
WHIM, RDF, functional
groups, etc.
geometrical properties, etc.
physical properties, etc.
fingerprints, daylight
fingerprint (FP2)
2D, 3D descriptors
fingerprints, BCUT
Data quarrying and analysis methodologies aid in speeding up the process
23,24
of target assessment.
Big data analysis and artificial intelligence methods
(deep neural networks) are embroidering to the computational approaches,
which has resulted in advanced and fruitful drug development.25 Free sources
such as Open-Targets,26 UniProt, and ChEMBL27 offer useful starting point to
cover areas of disease association, protein annotation, and potential ligands,
33 Computational Drug Discovery and Drug Repurposing
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respectively. In recent times, machine learning algorithms are applied for
28
target identification and drug discover
y.
Polypharmacological studies indicate that most drug molecules are not target
29
specific and hit multiple targets.
This possibility has led to the concept of
the “chemical space” to entitle and consider all ensembled organic molecules
in the quest for finding new drugs.30 Information regarding all the organic
molecules can be extracted from various databases such as Pubchem,
32
ZINC,
Chemspider,
CTD,37 HMDB,38 SMPDB,39 Drugbank.
33
ChemDB,
34
BindingDB,
40
35
ChEMBL,
27
NCI open,
31
36
Chemioinformatics deals with collection, storage, analysis, and manipula-
41
tion of large quantities of chemical data.
Computer-aided drug design,
structural representation, and chemmetrics are the primary elements of the
cheminformatics.
42
The chemical structure representations can be linear,
2D or in 3D format, or as SMILES (simplified molecular input line entry
43
specification).
CAS Draw, DIVA (diverse information, visualization, and
analysis), Structure Checker Accord, MarvinSketch, PowerMV, ArgusLab,
Babel, Chimera, CLIFF, Dragon, Grace, JOELib, ORTEP, Packmol, Polar,
Biosoft, Q-chem, KOWWIN, etc., are some commonly used chemioinformatic software tool packages.
44
The atomic and electronic groups in a molecule are transformed into
pharmacophoric features and designated as hydrogen bond donors or
acceptors, cationic, anionic, aromatic, or hydrophobic attributes called as
45
pharmacophore fingerprints.
The pharmacophore model is generated first
by translating protein–ligand interactions. Ligand-based 3D pharmacophore
models can be developed when no 3D structure of the macromolecule target
is available.
and optimization.
46
3D pharmacophore models are used for hit and lead discovery
47
Accelrys’ (CA, USA), Discovery Studio Schrödinger’s
(NY, USA) PHASE, Chemical Computing Group’s (QC, Canada), Molecular
34
Drug Repurposing and Computational Drug Discovery: Strategies and Advances
Operating Environment (MOE), and Inte:Ligand’s (Maria Enzersdorf,
Austria) LigandScout are some frequently utilized pharmacophore modeling
software. Advanced approaches employing 3D pharmacophore concept
48
such as AutoDock Bias,
mapper
52
are popular among researchers.
PyRod,49 SILCS-Pharm,50 PharmIF,51 and Pharm-
The concept of “drug-likeness” riddles out molecules with unsolicited properties from screening libraries and reduces attrition risk at the later stages
of drug discovery. A variety of machine learning techniques are available to
the modeling of drug-likeness like artificial neural networks (ANN), support
53
vector machine (SVM), and recursive partitioning.
of drug-likeness (QED),
54
relative drug likelihood (RDL),55 and Gaussian
Quantitative estimate
scoring function (GAU)56 are few quantitative approaches used for defining
druggability. Recently, artificial intelligence (AI) approaches are applied for
model construction for specific ADMET endpoint evaluation,53 like Naïve
57
Bayesian Classifiers (NBC) approach
approaches.
58
ADMET studies are indispensable steps in any drug discovery
and recursive partitioning (RP)
program as it provides pharmacokinetic and pharmacodynamic, that is,
toxicity (T) properties of lead molecules and reduce the risk of experimental
cost, time, and drug failure. Progresses in combinatorial chemistry and highthroughput screening have comprehensively amplified the number of small
molecules for which early data on ADMET are available as reference.
59
Table 2.2 depicts some software application tools for ADMET screening.
Figure 2.2 describes sequential steps in toxicity prediction modeling.
TABLE 2.2 Software Application Tools for ADMET Screening.
Software tools Feature application Web address
ADMET lab Systematic ADMET screening http://admet.scbdd.com/
ADMET ADMET property screening https://www.simulations-plus.com/
predictor
ADVERpred Prediction of adverse effects of http://www.way2drug.com/
drugs adverpred/
eMOLTOX Prediction of molecular toxicity http://xundrug.cn/moltox
LIVERTOX Screening of hepatotoxicity https://livertox.nih.gov/
Molinspiration Screening of molecular properties http://www.molinspiration.com/
software/admetpredictor/
TABLE 2.2 (Continued)
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Software tools Feature application Web address
Mousetox Small molecule cytotoxicity
prediction
SOM
prediction
QikProp Schrodinger tool for ADMET
SwissADME Assessment of ADMET http://www.swissadme.ch/
Knowledge based prediction of
ADMET properties
prediction
parameters
http://enalos.insilicotox.com/
MouseTox/
http://www.scfbio-iitd.res.in/
software/
drugdesign/som.jsp
https://www.schrodinger.com/
qikprop
35 Computational Drug Discovery and Drug Repurposing
FIGURE 2.2 Sequential toxicity modeling.
2.2.2 STRUCTURE-BASED APPROACHES
Structure-based drug design (SBDD) methods are a prominent component
60
of modern medicinal chemistry.
Molecular docking, structure-based virtual
screening (SBVS), and molecular dynamics (MD) are frequently used SBDD
strategies for analysis of molecular recognition events of binding energetic,
molecular interactions, and induced conformational changes.
61
based approaches use structural data obtained experimentally or through
computational homology modeling. The accessibility of 3D macromolecular
targets allows a meticulous inspection of the binding site topology along
Structure-
36
Drug Repurposing and Computational Drug Discovery: Strategies and Advances
with electrostatic properties, such as charge distribution as well. Current
methods allow for the design of ligands containing the necessary features for
efficient modulation of the target receptor.
62
The advances in computational biology have made molecular modeling a
63
reliable method in the drug discovery pipeline.
Comparative modeling or
homology modeling uses target sequence information for the prediction of
three-dimensional structures.
sents thoughtful applications of algorithms of protein structure prediction.
64
The approach of molecular modeling repre-
65
The computational methods for protein structure prediction include comparative modeling, fold recognition, first-principles methods with database information, and first-principles methods without database information.66 CASP
(critical structure prediction assessment, a biennial collective project) plays
a key role in protein structure prediction.67 RasMol, PyMOL, Chimera, etc.,
are some visualization tools for visualizing and analyzing predicted models
defining the protein structure.
68
Docking precisely fits ligand into the specific receptor-binding site and evalu-
69
ates their binding strength.
Molecular docking can be flexible or rigid. Flexible docking is a well-thought-out good approach with better prediction than
conventional docking.70 Molecular docking is divided into three sections;
ligand–receptor preparation based on force field estimations followed by
applying flexible or rigid docking methods and setting search strategies for
ligand ratification.71 Mapping of binding site is done following ligand and
receptor preparation by GRID calculations.
72
Various algorithms are applied
in the docking process such as fragment-based, Monte Carlo and molecular
dynamic based. empirical-based, and knowledge-based scoring functions are
usually applied for docking studies.
73,74
Some frequently used software tools
for docking studies are depicted in Table 2.3. Analyzing docking outputs is
essential as it explains the number and nature of bond present in the ligand
protein complex.75 LigPLOT (2D format),
79
format)
are highly used software tools for analyzing docking results.
76,77
PyMOL,78 and Chimera (3D
37 Computational Drug Discovery and Drug Repurposing
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TABLE 2.3 Some Frequently Used Software Tools for Docking Studies.
Software Algorithms License
AUTODOCK Genetic Lamarckian Open, GNU, GPL
SWISSDOCK Evolution based optimization Open, academic
AUTODOCKVINA Local optimization Apache
GOLD Genetic algorithms Commercial
GLIDE Hybrid optimization Commercial
CDOCKER Hybrid optimization and shape mapping Commercial
iGEMDOCK Genetic Commercial
Arguslab Hybrid optimizations Open
MOE-DOCK Hybrid optimizations Commercial
Molecular dynamics simulations are useful tools for the drug discovery process.
It is advantageous over molecular docking studies as it facilitates the evaluation of the binding energetic and kinetics of the receptor–ligand interactions
80
at atomic levels.
changes in the biomolecules exhausting the molecular dynamic theories.
GROMACS,82 AMBER,83 CHARMM-GUI,
Numerous tools are available to explore the atomic level
81
84
NAMD,85 and LAMMPS,
86
etc.
are commonly used software packages for molecular dynamic simulations.
2.2.3 SYSTEMS-BASED APPROACHES
Merging of genomics, proteomics, and metabolomics databases with
system pharmacology models aims at generating a disease-specific network
that would build confidence in particular target identification that is the
87
crucial step in drug discovery and identification process.
Systems-based
approaches connect protein targets to their physiological arena, seeing
a much wider systemic viewpoint of their environment in conjunction of
their molecular details.88 Drug targeting for diseases with complex pathophysiology involving complicated signaling transduction cascades mostly
benefits from a system-based approach.
87
Quantitative systems pharmacology or network pharmacology is an
emerging discipline that aims to integrate methods of systems biology with
38
Drug Repurposing and Computational Drug Discovery: Strategies and Advances
pharmacodynamic concepts to foster advanced small-molecule and macromolecule drug discovery and development.89 40% of current drug discoveries are based on the concept of network pharmacology.90 Establishing drug
target disease network by utilizing high-throughput omics technologies is
91
the major goal of network pharmacology.
Through unbiased investigations on potential target spaces network pharmacology endeavors to discover
newer leads and targets and also repurpose prevailing drug molecules for
diverse therapeutic conditions.
92
The area of network pharmacology has
been broadly divided into the experimental and computational approaches.
Graph theory, statistical methods, data mining, modeling, and information
visualization methods are the computational approaches. The experimental
approaches include integrated clinical trials and high-throughput omics
techniques. Data mining, big data analytics, network construction, interactions prediction, and network analysis are key methodologies in the network
93
pharmacology approach.
done through public databases and further experimental analysis.
Data mining in network pharmacology can be
91
In contrast to QSAR modeling proteochemometric (PCM) modeling is based
94
on the similarity of a group of ligands and a group of targets.
Commonly
PCM has mainly been applied to data sets of G protein-coupled receptors
(GPCRs), in particular the rhodopsin-like class A receptors, dopamine,
histamine, adrenergic, and melanocortin receptors.
95
This versatile approach
is deployed for hit identification for orphan targets, as well as modeling of
orthosteric and allosteric ligands.
96
Table 2.4 summarizes a few more PCMapplied studies. Repurposing of mebendazole and celecoxib was done using
a proteochemometric modeling known as TMFS (train-match-fit-streamline)
method applying a variety of descriptors.
TABLE 2.4 Summary of Few PCM Applied Studies.
Target protein Method applied No. of
Aromatase Bayesian linear regression,
random forest, support
vector machine
Carbonic
anhydrases
k-nearest neighbor 9 549 [99]
97
No. of ligand
proteins
studied
1 10 [98]
molecules
screened
Reference
TABLE 2.4 (Continued)
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Target protein Method applied No. of No. of ligand Reference
proteins molecules
studied screened
Cyclooxygenases Gradient boosting machine, 11 3228 [100]
elastic net, random forest
Dengue virus Partial least squares 4 45 [101]
NS2B-NS3
proteases
HIV-reverse Support vector machine 2 48,221 [95]
transcriptases and
proteases
Kinases Deep neural network, 284 19.9 million [102]
random forest, gradient
boosting machine
Nuclear receptors Decision tree, logistic 11 7267 [103]
regression, ridge classifier,
random forest
Serine proteases Partial least squares, 24 5863 [104]
random forest
39 Computational Drug Discovery and Drug Repurposing
Pathway analysis is also known as enrichment analysis which examines
data acquired by high-throughput technologies for correlating physiological
mechanism to macromolecular targets and comprises input phase, analysis
105
phase, and output phase.
in the input phase.
106
Null hypothesis generation is the important step
Analysis phase comprises mathematical computations
defined by a specific set of algorithms that are used by widely available
open-source software programs such as BioConductor
107
and GitHub
108
projects. Finally, the output phase comprises visualization and analysis of
results. The relevant pathways are ranked arranged in a hierarchal manner
by applying statistical attributes.
2.2.4 CASE STUDY
Rath et al. (2021) used the computational approach and designed the novel
mesalamine–coumarin conjugate. The macromolecular targets chosen for
docking (using AUTODOCKVINA) were COX-2(cyclooxygenase-2),
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