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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5440_Библиотеки_им_академика_М_И_Перельмана.pdf
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procedures that begin with ligand- and structure-based pharmacophore modeling. It integrates
inventive outstanding performance alignment methods that guarantee outstanding prediction
quality while allowing screen at a level never before possible [60]. MOE: MOE offers three
approaches for generating conformers. These algorithms, which are analyzed in this work, are
summarized, and their main default settings, which were employed unless otherwise specified, are
also described [61]. MolSign: MolSign is a comprehensive package for pharmacophore modeling.
Finding pharmacophoric properties allows for a comprehensive mapping of the molecule, which
may be utilized to search databases using pharmacophores [62]. PharmaGist: PharmaGist detects
pharmacophores. The applied technique is ligand-based. Specifically, it does not require the target
receptor’s structure. Instead, the input consists of drug-like compounds with known receptor-
binding properties. The output includes potential pharmacophores computed using several flexible
alignments of the input ligands. The method directly and systematically handles the input ligands’
flexibility during the alignment phase [63]. Pharmer: Pharmer is a revolutionary computational
pharmacophore search method that grows with query breadth and complexity rather than data-
base capacity. Geometric hashing and the generalized Hough transform inspire Pharmer, although
they differ. Pharmer can search millions of structures for a given pharmacophore in minutes,
extending pharmacophore screening [64]. PharmMapper: The PharmMapper online tool is a web
server that uses reversed pharmacophore matching to identify possible drug targets by comparing
the query chemical to an inside pharmacophore database [65]. Phase: PHASE was developed to
improve pharmacophore-based lead discovery and optimization methods, which increasingly
depend on models and 3D databases [66]. QSAR-Co: QSAR is a computational technique in the
field of drug design. QSAR-Co is an open-source standalone software for model development and
predicts the module. This provides a robust multitarget model using linear discriminant analy-
sis [67]. QSAR ToolBox: QSAR Toolbox is a software application to find the QSAR models. This
incorporates data from various sources and computational tools to identify targeted chemical mol-
ecules [68]. McQSAR: It utilized the descriptor for the multirepresentation of the molecules based
on their variability and structure changes [69].
Table 4.5 List of tools and databases available in the case of LBDD.
Sl. no. Name URLs References
1 LigandScout http://www.inteligand.com/ligandscout/ [60]
2 MOE http://www.chemcomp.com/MOE-Pharmacophore_
Discovery.htm
[61]
3 MolSign http://www.vlifesciences.com/products/Functional_products/
Molsign.php
[62]
4 PharmaGist http://bioinfo3d.cs.tau.ac.il/pharma/ [63]
5 Pharmer http://smoothdock.ccbb.pitt.edu/pharmer/ [64]
6 PharmMapper http://59.78.96.61/pharmmapper [65]
7 Phase http://www.schrodinger.com/Phase/ [66]
8 QSAR-Co
https://sites.google.com/view/qsar-co [67]
9 QSAR ToolBox https://qsartoolbox.org/ [68]
10 McQSAR http://users.abo.fi/mivainio/mcqsar/index.php [69]
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4.4 Structure- and Ligand-Based Assisted Studies
In this current situation, drug design through structure-based and ligand-based revolutions is
the process for identifying novel targets and inhibitors employing multiple screening steps,
cross-validation, and many more. Most of the researchers utilized the SBDD, which states the
importance of the available target structure and how it can be helpful for novel inhibitor iden-
tification. Like the ligand of the chemical compound, natural products and approved-
investigated libraries are available, which can help get the novel ligands through the ligand-based
assisted study. Within this concept, the high-throughput virtual screening process also lies,
which has been utilized to get the top inhibitor that can be further precisely investigated. Apart
from that, various pivotal steps occurred during the successful structure- and ligand-based con-
cept, successfully performed worldwide by comprehensive researchers, and their concept
improvement was ongoing daily. So in this section, 10 selective studies of the structure-based
and ligand-based were discussed, which can be helpful for readers to get a better understand-
ing, i.e., how this concept was lying and how it leads to successful study design for the novel
discovery. The structure-based assisted successful studies and the other computational aspects
are shown in Table 4.6; similarly, the ligand-based assisted study is illustrated in Table 4.7.
Moreover, the detailed description of the mentored study in Tables 4.6 and 4.7 was discussed in
the section.
Table 4.6 List of successful structure-based assisted studies.
Sl. no. Target Remarks References
1 SARS-CoV-2 Mpro Identification of protease inhibitors employing structure-
based methods
[70]
2 COX-2 Identification of novel inhibitors through structure-based
methods along with advanced computation
[71]
3 SARS-CoV-2 Mpro Identification of druggable compounds through structure-
based methods
[72]
4 TMPK Identification of natural product-based inhibitors through
a structure-based approach
[73]
5 CTLA4, CDK8, EGFR,
mTOR, p53R2, and PR
Identification of phytochemical-based inhibitors through
a structure-based approach
[74]
6 α-glycosidase Identification of α-glucosidase inhibitors employing
structure-based methods
[75]
7 Protein-polymerase basic
protein
Identification of natural inhibitors compound-based
employing structure-based methods
[76]
8 Secreted aspartic
protease 2 (SAP2)
Identification of novel inhibitors through structure-based
methods
[77]
9 SARS-CoV-2 Mpro Identification of novel nonpeptide inhibitors through
structure-based methods
[78]
10 CYP1B1 Identification of novel inhibitors through a structure-
based approach along with experimental validation
[79]
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4.4.1 The Detailed Description of Each Tool
In this situation, several successful therapeutics have been developed to reduce the transmission
and infection rate of COVID-19. A study by Kolybalov et al. targeted the Mpro of the SRAS-CoV-2,
the most important therapeutic target, as it plays an essential role in viral mechanisms. In this
study, the author manually investigated four ligands, i.e., L1–L4, prepared by altering the binding
groups, and the docking were executed through the GOLD database. The structure was retrieved
from PDB with ID:7NG6 and prepared using authentic approaches. The binding site was identified
and set, followed by a 10 Å spherical cavity. The docked complex was further investigated through
the MD simulation followed by a 100 ns period. Based on the energy calculation and several results
validation, the author suggested that the LK2, L3, and L4 can be utilized for further optimization.
The study suggested that these identified compounds can be a potential antiviral candidate against
SRAS-CoV-2 [70]. Another study by Krishnendu et al. targeted cyclooxygenase (COX) to identify
its potential inhibitors employing the pharmacophore structure-based approaches and many more
validation steps. COX is an enzyme essential for the body’s reaction to inflammation and for mak-
ing prostaglandins, lipid substances that participate in many bodily functions. Moreover, it plays
an essential role in severe gastrointestinal irritation and cardiac toxicities and targeting this can be
helpful for a better tomorrow. In this study, a total of 190 inhibitors were reported and studied in
the year 2022, a part of 18 extensive research articles followed by various chemical structures
drawn by the ChemDraw Ultra 12.0. Further, the pharmacophore model was generated with the
help of the PHASE module available in Schrodinger, considering different characteristics. A set of
130 compounds were incorporated using the PHASE module. Using automated random selection,
these compounds were utilized to generate a 3D QSAR model. The structure of COX-2 was com-
puted from PDB having ID: 5KIR, and the models’ docking analysis was performed, followed by
the virtual screening. Additionally, the ligands were also examined for the drug-like properties
analysis. The overall study suggests that the identified molecules have better promising results
than standard rofecoxib drugs, and apart from that, the designed methodology can also be utilized
for further study, and the reliable approach shows a promising accuracy in terms of novelty [71].
Different ligand libraries were developed to revolutionize the drug discovery process, with numer-
ous compound collections that can be helpful for novel inhibitor identification. To keep the explor-
ing concept, Ghufran et al. performed another study, which utilized a reliable approach to find a
Table 4.7 List of successful ligand-based assisted studies.
Sl. no. Target Remarks References
1 DNA Topoisomerase I (Top1) Identification of potential inhibitors through
ligand-based approach
[80]
2 Histone deacetylase 3
(HDAC3)
Identification of potential inhibitors through ligand
based along with pharmacophore
[81]
3 Ld-PriS Identification of novel drug compounds through
ligand-based approach
[82]
4 CatD-statin Assessment of chemical features of the selected
inhibitor through a ligand-based approach
[83]
5 TcTIM Identification of benzimidazoles potential inhibitors
through ligand-based approach
[84]
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druggable target of Mpro protease. In this study, the Mpro protease was retrieved from the PDB
database with the ID 6LU7 for further investigation. The downloaded targets were further pre-
pared and minimized before further processing. Similarly, a set of ligands was retrieved from dif-
ferent databases followed by ZINC, ChemBridge, and in-house designed structural analogs
comprising 1600 compounds. These compounds were minimized and optimized using the molecu-
lar operating environment (MOE) framework. Afterward, the authors performed structure-based
virtual screening followed by molecular docking analysis, and the promising compound was fur-
ther evaluated through the MD simulation process. The binding free energy was also calculated to
better understand the ligand and target interaction throughout the simulation computation. The
overall study shows that the three potential active inhibitors that were identified from each set of
libraries have a promising bonding with the selected target. The authors also suggested that these
compounds can be considered the most active antiviral compounds [72]. Another study was exe-
cuted by Khan et al. to identify natural product-based promising inhibitors of the Monkeypox
virus. Monkeypox is one of the leading re-emerging diseases that will be noticed in 2022 in differ-
ent countries. In this study, authors targeted the thymidylate kinase as the structure was not avail-
able; the target was modeled considering the vaccinia virus having PDB ID: 2V54. Further, the
structure quality was validated using the subsequent servers, Procheck and ProSA-web. Further,
the natural products library was screened, followed by traditional Chinese medicines, NPASS,
SANCDB, and Coconut. Further, all retrieved ligand data were screened based on Ro5 to identify
promising ones. Afterward, the compounds were screened on the active site of the target structure
utilizing the AutoDock vina. The stability of the ligand–target complex must be crucial in the drug
design process, so the top three compounds from each dataset screened based on the target struc-
ture were further examined through the simulation process. The simulation was performed over
300 ns, and the authors calculated the binding energy. Based on the overall study, the top three
compounds from the TCM (TCM26463, TCM2079, TCM29893), NPASS (NPC474409, NPC278434,
NPC158847), SANCDB (SANC00240, SANC00984, SANC00986), and coconut (CNP0404204,
CNP0262936 and CNP0289137) databases were found as most promising based on the crucial steps
of validation. Further, the authors suggested that these compounds need experimental validation
followed by in vitro and in vivo before final administration [73]. A study by Jha et al. on identifying
phytochemicals for breast cancer employs molecular docking. In the present situation, it is one of
the leading infections worldwide, and their cases are increasing daily; moreover, there are different
cancer therapeutics available; however, their adverse effect is found to be less accurate. Therefore,
in this study, the authors retrieved the target protein associated with breast cancer from the PDB
database followed by its ID: 4OAR, 2J6M, 4DRH, 3MF1, IDQT, and 6T41. These targets were pre-
processed with the help of Discovery Studio 2020, followed by standard protocol. Moreover, the
active site within the target was identified using the CASTp server, which identified more pivotal
residue that can be considered a target active site followed by 3–5 in residue. Afterward, the ligand
was retrieved from Dr. Duke’s phytochemical and ethnobotanical databases, and their required
structure was retrieved from the PubChem databases. The identified ligand must have all the drug-
like properties; therefore, the author performed the initial screening steps to reduce the ligand
library and identify drug-like molecules. Moreover, the docking was performed using AutoDock
Vina. The identified compounds through the docking analysis were utilized for the bioavailability
and toxicity analysis. The overall study shows that among the top selected compounds with each
target, only ursolic acid, enterolactone, parthenolide, and berberine have promising activity based
on the selection criteria. However, the authors suggested that MD simulation and experimental
validation can further investigate these compounds [74]. Another study performed by Liu et al.
targeted α-glycosidase for the identification of novel inhibitors. In the current situation, diabetes is
87
one of the leading metabolic disorders, which leads to severe medical complications in human
health. In this study, the authors employed different crucial computational aspects followed by the
experimental approach. The retrieved target was further prepared through the Protein Preparation
Wizard, followed by preprocessing, energy minimization, and many more. The active site within
this target was designed based on the residues within 15 Å centered in the structure. The docking
analysis was performed, followed by the standard protocol using the Specs database to identify the
inhibitors. A total of 52 compounds were purchased in this study, and the inhibitor assay analysis
was performed through the standard protocol. Afterward, the authors performed the quenching,
kinetic assay, and cell viability assay. The overall study shows that the four compounds were found
to be the most promising ones based on molecular docking screening and experimental validation.
The authors suggest that these compounds are the most promising hit [75]. Of the four types of
influenza virus, the two types, i.e., types A and B, affect the human and are commonly known as
the flu. Several natural compounds that showed inhibitory activity toward the influenza virus tar-
get were reported. A study performed by Jin et al. targeted the RNA polymerase to identify novel
inhibitors toward it. The structure was extracted from the PDB database using PDB ID: 2ZNL and
prepared using Schrödinger’s software through the protein preparation wizard considering stand-
ard protocol followed by preprocess energy minimization and elimination of steric hindrance.
Furthermore, the grid centers were generated through the position of the PB1 subunit. Afterward,
a total of 70,000 natural compounds were collected from the different databases and utilized for
the virtual scanning process, followed by the HTVS, SP, and XP, considering the top 10% com-
pound as a selection criterion. The screened compounds were examined through the Swiss ADME
calculation for a detailed investigation of drug-like properties. The stability of the identity com-
pound with the target was validated through the simulation process. Based on the binding energy
score obtained, the overall finding suggests that compound 1 is the most stable bonding among the
most promising compounds. The authors suggest that no inhibitors are available in the market
based on the target utilized in this study, so this investigation will help understand the molecular
aspect between the target and identified novel inhibitors [76]. Another study performed by Li et al.
targeted secreted aspartic protease 2 (SAP2) for the identification of pivotal inhibitors followed by
experimental validation. In this study, a total of 713,000 compounds were utilized for the virtual
screening process and ranked by the GOLD. Within this screening, a total of 7851 molecules were
identified through the steps, and a further 582 molecules were utilized for the 100 structural
diverse candidates and further purchased. Moreover, out of these compounds, 50 were utilized for
the in vivo experimental validation. The inhibitory assay shows that among the 50 compounds, a
total of 8 compounds showed a high inhibitory rate, and within that four compounds have a large
IC50 value. Within the screened compound, one compound was reported as a Shp-2 inhibitor, and
further, the authors performed the structural activity relationship of identified pyrazolone, along
with its chemistry. The identified compounds were biologically evaluated, and their expression
was analyzed on their corresponding cell. The authors also analyzed the antifungal activity to
evaluate the inhibitor’s activity. The overall study suggests that the one derivative named 24a
showed promising inhibitor activity and was confirmed through the experimental evaluation. The
authors reported that the mentioned compound has antifungal activity, and their optimization is
ongoing [77]. SARS-CoV-2 is one of the leading causes of COVID-19 infection, and to overcome
this, several approaches were employed, and different therapeutic approaches were developed.
Similarly, another study was performed by Yang et al., which targeted Mpro for the identification
of nonpeptide inhibitors. In the viral mechanism of SARS-CoV-2, Mpro is the essential one that
plays a major role in pathogenesis. The authors retrieved the target structure with PDB ID: 6LU7
and prepared it using the protein preparation wizard followed by the standard protocol. Further,
88
the receptor grid generation program identified the receptor sites within the target using the Glide
program. Afterward, 8960 commercially available compounds were utilized in this study and pre-
pared using the LigPrep module available at Schrodinger, followed by the standard protocol.
Further, the ensemble docking approach was utilized in this study through the high-throughput
virtual screening; moreover, the selection criteria were set at 10%. Finally, 49 compounds screened
through the virtual screening were purchased from TargetMol for the experimental evaluation.
Afterward, the biological assay experiment, MD simulation, and binding calculation were per-
formed. The overall study shows that the two compounds (Z1244904919 and Z1759961356) were
the most promising throughout the experimental validation. The study suggests that these proto-
cols and identified antiviral drugs could be an initial point to combat COVID-19 [78]. Wang et al.
targeted cytochrome P450 (CYP) to identify its inhibitors through structure-based virtual screen-
ing and experimental validation in another study. The target structure was retrieved from the PDB
database with ID: 6iq5, having an inhibitor with an IC50 score of 4.4 nm. The author employed a
docking validation protocol in which 201 reported inhibitors and 5025 inactive compounds were
extracted through the DUD-E databases. Moreover, considering the collected external dataset and
FDA databases, the LigPrep module was employed for the ligand library preparation. Further, for
accurate identification, reverse docking analyses were performed to validate the docking protocol
using different methods, CDOCKER, Libdock, and LigandFit in Discovery Studio, Autodock, and
Vina. The Glide program was utilized for the virtual screening analysis on the active site of the
CYP1B1 through the standard precision (SP), followed by the MM/GBSA method. Further, the
identified inhibitors were examined through EROD (ethoxyresorufin-O-deethylase) assay.
Moreover, the authors also perform the MD simulation over 100 ns, along with the density func-
tional investigation. The cell culture and statistical analysis were also performed during the inves-
tigation. Furthermore, the overall study suggests that carvedilol could be a potential repurposed
inhibitor with IC50 of 1.11 μM [79].
4.4.2 The Detailed Description of Each Tool
In the case of anticancer and antibacterial drug development, targeting topoisomerase could
be a potential lead as it involves resolving the complication associated with the supercoiling
of DNA and its further mechanisms. In these aspects, a study performed by Pal et al. targeted
topoisomerase for the identification of its potential inhibitors employing the pharmacophore
model along with the virtual screening. In this study, 62 molecules were retrieved from the
literature review for the dataset generation and split into the test and training set, followed
by their biological activity. Further, the structure of the selected ligands was drawn through
the ChemDraw Ultra software and optimized through the CHARMM force fields. Utilizing
this set of data, the 3D QSAR pharmacophore model was generated; however, the designed
model was validated through the Cost analysis, Test set analysis, and Fischer’s randomiza-
tion test. Apart from the set of ligand libraries, they were designed for the virtual screening
process followed by the drug-like properties analysis. For the docking purpose, the topoi-
somerase structure was retrieved from the PDB database using its ID: 1T81. Finally, the MD
simulation study evaluated the most promising identified compounds. The findings show
that ZINC68997780, ZINC15018994, and ZINC38550809, based on excessive steps of valida-
tion followed by docking, simulation, and ADME analysis, have the most promising profile
and can be further utilized for the potential inhibitors [80]. Similarly, another study was
performed by Kumbhar et al. using the concept of pharmacophore modeling toward the
HDAC3 to identify potential inhibitors. In this study, 84 reported inhibitors were utilized for
89
the dataset generation, followed by training and test set data. Moreover, their structure was
designed using ChemDraw. Moreover, the model was generated using this dataset, and their
validation was performed, followed by Fisher’s randomization. Afterward, the virtual screen-
ing was performed using the diverse chemical datasets (NCI, Asinex, Chembridge, and
Maybridge chemical databases). For the virtual screening, the Fast and Flexible methodology
was employed along with the ADME analysis. The compound identified through the virtual
screening was utilized for the docking analysis with the HDAC3 (PDB code: 4A69) target
through the GOLD software. Moreover, the most pressing ones were utilized for the simula-
tion study over 50 ns and the binding energy calculation. Additionally, the PCA was also
performed for a better understanding. The overall finding shows that the 22 compounds
show a promising interaction on the selected target. However, the top three hits were further
analyzed for the simulation analysis. The study shows that the two compounds have sta-
ble interaction through the energy calculation. The authors suggest that the reported screened
compounds can be potential inhibitors and an experimental evaluation is required [81]. A
study by Bhowmik et al. utilized the concept of ligand-based virtual screening to identify
potential drugs toward the catalytic subunit of Leishmania donovani. In this study, the
authors performed the BLAST to get the gene sequence of the Ld-PriS, and the homol-
ogy modeling was performed, along with their validation, to get an understanding of the
quality of the designed model. Moreover, the structure was minimized through
the YASARA. Furthermore, the active site within the target was completed through the
COFACTOR and COACH software. Finally, the virtual screening and socking analysis were
performed using the PyRx and iGEMDOCK software. Further, the ligands were evaluated for
drug-like properties analysis and toxicity analysis. Afterward, the most promising hits were
utilized for the simulation analysis over 50 ns. The overall finding shows that the
ZINC000009219046, ZINC000025998119, and ZINC000004677901 show promising molecular
activity with the target, and their simulation confirmed that the compound has stable bind-
ing. The authors suggest that these compounds can be considered the best inhibitors [82].
Another study by Sakkiah et al. targeted Cathepsin D, a significant component of lysosomes
and an important component of catabolism and degenerative conditions. The author per-
formed the ligand-based virtual screening in this study to identify promising inhibitors.
Initially, the authors employed the pharmacophore modeling using the DiscoveryStudio v2.5
software. The compound utilized for the modeling was drawn using the ChemSketch V12
software. Fit the model generation, a total of 76 reported inhibitors were utilized for the test
and training set data generation using these compounds. The pharmacophore model was
validated. The virtual screening was also performed, utilizing 300,000 molecules for the pro-
cess, which was evaluated through the ADME properties analysis. Afterward, the molecular
docking analysis was performed, and for that, the target structure was received from the PDB
database with the PDB ID 1LYB. The overall finding suggests that the 49 hit compounds were
found promising based on the utilized selection criteria. The authors suggest that Hypothesis
1 can also be utilized for another study as it shows a new molecule for the selected target as
an inhibitor [83]. Another one by Jimenez et al. employed the virtual screening methods
along with the docking analysis for inhibitors of triosephosphate isomerase assessment. In
this study, a total of 10 compounds were utilized as a control, and their structure was drawn
through the ChemDraw software. The target structure was prepared through the UCSF
Chimera 1.14.1. Further, the molecular docking analysis was performed through PyRx soft-
ware, followed by the blind docking analysis. The benzimidazole scaffold was utilized for the
virtual screening steps, available at the ZINC15 database, and analyzed through the
90
OpenBabel program. Further, the MD analysis was performed over 100 ns, followed by the
binding energy calculation. The overall finding shows that the BP5 compound has a promis-
ing profile and a lower docking score on the human TIM compared to the utilized control.
The identified compound shows reliable activity toward drug-like properties [84].
4.5 Advancement and Challenges in SBDD and LBDD
The structure- and ligand-based drug design revolutionized the concept of drug design. It shows
very promising results in terms of accuracy and high processing activity. Through virtual screen-
ing within a bulk library, users can easily identify the most promising one. Structure-based analy-
sis can easily help understand the target structure and its activity, which can be useful before
processing the work. However, apart from the basic concept of the drug design, the fragment-
based method shows the promising concept behind the drug design in terms of identifying and
developing the drug in a simple form. To top up the concept, the researcher also employed
machine learning methods through which they trained a model that can easily help to identify
promising inhibitors. Moreover, the 3D QSAR concept was also evolving, which is helpful for the
arrangement of the reported ligand for the novel discovery. The structure and ligand base can also
be helpful in metabolic prediction, which means the candidate can be helpful in inhibiting meta-
bolic disease and reducing its effects. Moreover, with time by time, many advances were occur-
ring daily to get a more insightful impact. Similarly, as the advancement occurred, the
structure- and ligand-based study had certain drawbacks and challenges. In the case of structure
based, the binding site within the protein is difficult as it easily adopts multiple confirmations.
The target selection is also important during the drug resistance mechanism in the structure
against the identified inhibitors. As the nature of the target is hydrophobic and complicated, the
targeting of the membrane protein is still challenging; moreover, as the work is based on the com-
puter, it requires a high-performance computing system for better study. Similarly, in the ligand-
based drug design case, the ligand library’s limitations are challenging as it generates the concept
of the limited collected compounds. Moreover, as the QSAR model is based on the data retrieved
during the study, it will lead to challenges in identifying false positive leads. Moreover, several
studies employed several concepts for model generation, so the impact of the identified com-
pound also varies. In the case of the ligand-based study, the author explained the ADME calcula-
tion for the drug-like properties analysis; the accurate prediction of the properties and remains is
challenging. Apart from this, severe advantages and challenges occur daily, and to overcome
these, worldwide researchers apply various extensive concepts to deal with and handle these chal-
lenges easily.
4.6 Conclusion
The computer-assisted study has the most impactful result in the present situation in the case of
drug design. Several studies were performed along with the structure- and ligand-based drug
designs. To enhance the study, the drug target and inhibitor identification server tools and data-
base were also designed and developed, which can be helpful for numerous studies. Using these
developed databases, researchers are utilizing the concept of structure- and ligand-based drug
design by enhancing the novel discovery. Moreover, it is essential that before the drug discovery
References 91
process, the author explores the available databases and tools that are associated with the study.
This study explores the in-detail concept behind the drug design, followed by structure- and ligand-
based drug design. The mentioned tools and database can be helpful for the researcher to under-
stand inhibitor identification, virtual screening, statistical evaluation, and many more. Moreover,
several databases have certain limitations, and their available dataset is low, which requires an
excessive updation to improve the tools and databases for better accuracy.
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