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ment of chronic obstructive pulmonary disease by means of AI-based drug discovery
process (Figure 6.16) [66].
6.1.5 Application of bioinformatics tools in drug development
Zanamivir is a neuraminidase inhibitor. It is used in the treatment and prophylaxis of
influenza caused by influenza A and B viruses. When the structure of the influenza
neuraminidase protein was determined by X-ray crystallography, the topology of the
active site was elucidated allowing for the first time the design of an inhibitor pre-
venting the virus escaping its host cell from infecting others. This achievement was
accomplished using a structure-based drug design approach. Various sialic acid ana-
logs were developed, aided by computer-assisted modeling of the active site [67].
Similarly, zolamide is a carbonic anhydrase inhibitor and an antiglaucoma agent
that decreases the production of aqueous humor. It was developed by the help of
structure-based drug design. Another success of structure-based drug design is devel-
opment of the antihypertensive drug captopril. It is an angiotensin-converting enzyme
(ACE) inhibitor used for the treatment of hypertension and congestive heart failure.
The structure-based pharmacophore modeling is applied for the identification of p53
upregulated modulator of apoptosis (PUMA) inhibitors. PUMA is a proapoptotic pro-
tein and member of the Bcl-2 protein family. Its expression is regulated by the tumor
suppressor p53 [68].
Basu et al., carried out the computational study to evaluate the FDA approved drug,
Fostamatinib as one of the most significant drug molecule to target the COVID-19
spreader proteins [68]. The docking is executed by using Molegro Virtual Docker (ver-
sion: 6.0). The docking of Fostamatinib and the crystal structure of COVID-19 main pro-
tease (6LU7) involves measurement of the effectiveness of the drug on COVID19. Due to
molecular docking of Fostamatinib with COVID-19 main protease, the docking score ob-
tained from Moldock and Rerank are −140.495 and −102.464, respectively. These findings
confirm that Fostamatinib might be considered as one of the drug candidates for treat-
ment of COVID-19 infections [69].
Zhavoronkov et al. reported Prulifloxacin (fluoroquinolone class antibiotic), Bicte-
gravir (HIV integ rase inhibitor ), Nelfinavir (HIV protease inhibitor), and Tegobuvir
(inhibitor of hepatitis C virus RNA replication) as repurposing drug candidates for the
treatment of COVID-19 infections. By the help of high-throughput computational
screening, these drug candidates are selected and found to possess high binding affin-
ity towards main protease of SARS-CoV [70].
Duarte et al. performed bioinformatic analysis to test whether the identified com-
pounds were physically interacting with the SARS-CoV-2 RNA-dependent RNA poly-
merase or main protease enzymes. The commonly prescribed FDA-approved drug
molecules identified for drug repositioni ng against COVID-19 include flupentixol, re-
serpine, fluoxetine, trifluoperazine, sunitinib, atorvastatin, raloxifene, butoconazole,
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and metformin. Molecular docking simulations study was performed on the RNA-
dependent RNA polymerase (RdRp) (PDB ID: 6M71) and the main protease (Mpro)
(PDB ID: 6Y2E) of SARS-CoV-2, using default settings in the Protein-Ligand ANT System
(PLANTS). From the results, it was observed that flupentixol is associated with a
highly negative τ score (τ = −95.9) in the connectivity mapping analysis. It suggests
that flupentixol is associated with a transcriptional signature negatively correlated
with SARS-CoV-2 infection [71].
Grifoni et al. also utilized the immune epitope database and analysis resources to
characterize the sequence similarity between SARS-CoV and SARS-CoV-2 through homol-
ogy modeling. The epitope prediction identified the potential B- and T-cell epitopes for
SARS-CoV-2 that will be the potential targets for the development of therapeutics and
vaccines for treatment of COVID-19 infection [72].
Famotidine is a histamine H₂ receptor antagonist medication and used to prevent
heartburn due to acid indigestion. It is appro ved by the FDA and reported to have
potency to treat peptic ulcer, gastroesophageal reflux, and Zollinger-Ellison syndrome.
Recently, it was reported that the use of Famotidine in hospitalized patients with
COVID-19 in the United States was associated with improved clinical conditions with
reduced risk of death. Gupta et al. performed molecular docking of Famotidine with
different targets of SARS-COV-2. Further, various steps are followed that include MD
simulation study and post-dynamics analysis such as RMSD, RMSF, hydrogen bond,
and PCA to determine the stability and binding mechanism of the complex of Famoti-
dine with the suitable targets. MD simulations studies provide significant information
for scrutinizing the internal motions, conformational changes, stability of protein-
ligand complexes and are found to be effective in designing inhibitors and mutational
analysis of SARS-COV-2 [73].
Amprenavir was designed and developed as a potential inhibitor of the human
immunodeficiency virus (HIV) protease by using protein mode ling and MD simula-
tions. Similarly, SBDD approach is applied to afford thymidylate synthase inhibitor
(raltitrexed) for the treatment of HIV. SBVS is applied for the identification of topo-
isomerase II and IV inhibitor (norfloxacin), which is an antibiotic commonly used in
the treatment of urinary tract infection (UTI). Fragment-based screening is involved
in the discovery of a carbonic anhydrase inhibitor (dorzolamide). It is used against
glaucoma, and cystoid macular edema. The antitubercular drug, isoniazid was discov-
ered through SBVS and pharmacophore modeling. INH is an enoyl-acyl-ACP reductase
(InhA) inhibitor. Similarly, flurbiprofen is a nonsteroidal anti-inflammatory drug
(NSAID) and is used in the treatment of rheumatoid arthritis, osteoarthritis, and so
on. This drug targets cyclooxygenase-2 (COX-2) that was developed through the molec-
ular docking approach (Table 6.8) [74–78].
6 Role of integrated bioinformatics in structure-based drug design 125
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Table 6.8: List of drugs developed with bioinformatics tools using drug design strategies.
Name of drug Indication Function Year of
approval
Atazanavir Antiretroviral drug used to treat HIV/
AIDS
HIV protease inhibitor
Abiraterone Hormone-refractory prostate cancer Androgen synthesis inhibitor
Crizotinib Non-small cell lung cancer (NSCLC) Anaplastic lymphoma kinase (ALK)
inhibitor
Captopril Hypertension and congestive heart
failure (CHF)
ACE inhibitor
Cimetidine Treatment of heartburn and peptic
ulcers
H-receptor antagonist
Dorzolamide Antiglaucoma agent Carbonic anhydrase inhibitor
Darunavir Antiretroviral drug used to treat HIV/
AIDS
Nonpeptidic HIV- protease inhibitor
Erlotinib NSCLC, pancreatic cancer Epidermal growth factor receptor
(EGFR) kinase inhibitor
Fosamprenavir Antiretroviral prodrug used to treat
HIV/AIDS
HIV protease inhibitor
Gefitinib NSCLC EGFR kinase inhibitor
Imatinib Leukemia Tyrosine kinase inhibitor
Indinavir Antiretroviral drug used to treat
HIV/AIDS
HIV protease inhibitor
Lapatinib Breast cancer EGFR/ERBB inhibitor
Lopinavir Antiretroviral drug used to treat HIV/
AIDS
Peptidomimetic HIV protease inhibitor
Nelfinavir Antiretroviral drug used to treat HIV/
AIDS
HIV influenza protease inhibitor
Oseltamivir Antiviral drug used to treat influenza-
A and influenza-B
Neuraminidase inhibitor
Ritonavir Antiretroviral drug used to treat HIV/
AIDS
HIV protease inhibitor
Saquinavir Antiretroviral drug used to treat HIV/
AIDS
HIV- protease inhibitor
Sorafenib Renal cancer VEGFR kinase inhibitor
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6.2 Future perspective
Computational tools like molecular docking, de novo design, molecular similarity cal-
culation, virtual screening, pharmacophore-based modeling, and pharmacophore
mapping have b een extensively applied to find out the therapeutically active drug
molecules. Structural genomics, bioinformatics, and computational tools continue to
explode with new advances and further success in structure-based drug design. Bioin-
formatics provides a novel, user-friendly, fast and reliable tool for conducting drug
design experiments, with the incorporation of a series of elite molecular modeling al-
gorithms in one platform.
6.3 Conclusion
Significant advances and application of bioinformatics in the field of pharmacokinetic
and pharmacodynamic studies are helpful for drug discovery process. The integration
of bioinformatics and computational approaches play significant roles in the costly,
complex, and highly challenging drug designing and discovery process. With the ad-
vancement of more sophisticated bioinformatics tools and the use of high-throughput
screening for target proteins, structure-based drug design techniques become an in-
dispensable method for the development of target-based therapies.
List of abbreviations
ADME Absorption, distribution, metabolism and excretion
AI Artificial intelligence
AIDS Acquired immunodeficiency syndrome
ANNs Artificial neural networks
BACE Beta-site amyloid precursor protein cleaving enzyme
Table 6.8 (continued)
Name of drug Indication Function Year of
approval
Tipranavir Antiretroviral drug used to treat
HIV/AIDS
Nonpeptidic HIV- protease inhibitor
Zanamivir Antiviral drug used to treat
influenza-A and influenza-B
Neuraminidase inhibitor
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CADD Computer-aided drug design
COX Cyclooxygenase
DL Deep learning
GA Genetic algorithm
HIV Human immunodeficiency Virus
HTS High-throughput screening
INH Isoniazid
LBDD Ligand-based drug design
MD Molecular dynamics
ML Machine learning
NMR Nuclear magnetic resonance
NSAID Nonsteroidal anti-inflammatory drug
PCA Principal component analysis
PDB Protein data bank
PWM Position weight matrix
SBDD Structure based drug design
SBVS Structure-based virtual screening
UTI Urinary tract infection
VdW Van der Waals
VS Virtual screening
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6 Role of integrated bioinformatics in structure-based drug design 131
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Vipul Kumar, Rakhi, Sahil Kumar
✶
, Kalicharan Sharma,
and Rajesh K. Singh
7 Molecular recognizable tools in X-ray
crystallography in computer-aided
drug design
Abstract: Many computational techniques have been employed to facilitate the pro-
cess of drug discovery. Various in silico techniques are efficiently supporting the
pharmaceutical industry to launch new drug molecules in the current era. Although
many in silico approaches are playing vital role in the process of drug discovery,
some molecular recognizable tools like X-ray crystallography are providing pivotal
platforms in computer-aided drug design (CADD) and drug discovery. X-ray crystallog-
raphy plays a significant role in the area of CADD through its invaluable contributions
of the structural underpinnings of protein–ligand interactions. This chapter focuses
on the molecular recognizable tools employed in X-ray crystallography for CADD.
Keywords: X-ray crystallography, molecular recognizable tools, CADD, SBDD, LBDD,
electron density maps
7.1 Introduction
The drug discovery process has always been expensive, time consuming and requires
exhaustive efforts to become a successful medication to mankind. The process of dis-
covering new drugs and developing them costs between US $800 million to US
$1.8 billion and takes an average of 10–15 years [1–3]. The growth of high-throughput
screening (HTS) and the expansion of drug discovery were both facilitated by devel-
opments in combinator ial chemistry that increased the number of compound data-
bases, spanning enormous chemical spaces. However, during the last several years,
the number of novel molecular entities that were successfully introduced into the
✶
Corresponding author: Sahil Kumar, Department of Pharmaceutical Chemistry, Delhi Institute of
Pharmaceutical Sciences and Research (DIPSAR), Delhi Pharmaceutical Sciences and Research
University, New Delhi 110017, India, e-mail: skm@dpsru.edu.in
Vipul Kumar, Rakhi, Department of Pharmaceutical Chemistry, Delhi Institute of Pharmaceutical
Sciences and Research (DIPSAR), Delhi Pharmaceutical Sciences and Research University, New Delhi
110017, India
Kalicharan Sharma, Department of Pharmaceutical Analysis, School of Pharmaceutical Sciences, Delhi
Pharmaceutical Sciences and Research University, New Delhi 110017, India
Rajesh K. Singh, Department of Pharmaceutical Chemistry, Shivalik College of Pharmacy, Nangal,
Rupnagar 140126, Punjab, India
https://doi.org/10.1515/9783111207117-007
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