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Contributors
Kumar Nallasivan Palani
Department of Pharmaceutical Sciences, SNS College of Pharmacy and Health Sciences,
Coimbatore, India
Ghanshyam Parmar
Department of Pharmacy, Sumandeep Vidyapeeth, Vadodara, Gujarat, India
Ashish Patel
Ramanbhai Patel College of Pharmacy, Charusat University, Changa, Gujarat, India
Arpita Paul
Department of Pharmaceutical Sciences, Faculty of Science and Engineering, Dibrugarh University,
Dibrugarh, Assam, India
Sivasubramanian Piramanayagam
Department of Pharmaceutical Sciences, SNS College of Pharmacy and Health Sciences,
Coimbatore, India
Mithun Rudrapal
Department of Pharmaceutical Sciences, School of Biotechnology and Pharmaceutical Sciences,
Vignan’s Foundation for Science, Technology & Research (Deemed to be University), Guntur, India
Chita Ranjan Sahoo
Central Research Laboratory, Institute of Medical Sciences and SUM Hospital, Siksha ‘O’
Anusandhan Deemed to be University, Bhubaneswar, Odisha, India
Malavika Saji
Amity Institute of Molecular Medicine and Stem Cell Research (AIMMSCR), Amity University,
Noida, Uttar Pradesh, India
Ashish Shah
Department of Pharmacy, Sumandeep Vidyapeeth, Vadodara, Gujarat, India
Tripti Sharma
Department of Pharmaceutical Chemistry, School of Pharmaceutical Sciences, Siksha
‘O’Anusandhan Deemed to be University, Bhubaneswar, Odisha, India
Ramendra K. Singh
Bioorganic Research Laboratory, Department of Chemistry, University of Allahabad, Prayagraj, India
Vishal Kumar Singh
Bioorganic Research Laboratory, Department of Chemistry, University of Allahabad, Prayagraj, India
Manish Kumar Tripathi
Department of Pharmaceutical Engineering and Technology IIT (BHU), Varanasi, Uttar Pradesh, India
Alok Shiomurti Tripathi
Amity Institute of Pharmacy, Lucknow, Amity University, Noida, Uttar Pradesh, India
Abd. Kakhar Umar
Department of Pharmaceutics and Pharmaceutical Technology, Faculty of Pharmacy,
Universitas Padjadjaran, Jatinangor, Indonesia
Mohammad Yasir
Amity Institute of Pharmacy, Lucknow, Amity University, Noida, Uttar Pradesh, India
James H. Zothantluanga
Department of Pharmaceutical Sciences, Faculty of Science and Engineering,
Dibrugarh University, Dibrugarh, Assam, India
Abbreviations
3D QSAR three-dimensional quantitative structure activity relationship
ACD available chemicals directory
ACEs acetylcholinesterases
AD atopic dermatitis
ADHD attention deficit hyperactivity disorder
ADMET absorption, distribution, metabolism, excretion, and toxicity
ADR adverse drug reactions
AF atrial fibrillation
AI artificial intelligence
AIDS acquired immune deficiency virus
AK adenylate kinase
AKI acute kidney injury
ALRs AIM2 like receptors
ALS amyotrophic lateral sclerosis
ANN artificial neural networks
ANVISA Agencia Nacional de Vigilancia Sanitaria
APC activated protein C
APL acute promyelocyticleukemia
APP amyloid precursor protein
ARG1 arginase 1
AROC area under the receiving operator curve
AUPRC area under precision-recall curve
AUROC area under the receiver operating characteristic
BBB blood-brain barrier
BEAR binding estimation after refinement
BiRW bi-random walk
CA carbonic anhydrase
CAD coronary artery disease
CADD computer-aided drug design
CANDO computational analysis of novel drug opportunities
CASP critical structure prediction assessment, a biennial collec-
tive project
CATNIP creating a translational network for indication prediction
CD Crohn’s disease
xii Abbreviations
CETP cholesteryl ester transfer protein
CETSA cellular thermo-stability assay
CHD congenital heart disease
CLP cecum ligation and puncture
CMC comprehensive medicinal chemistry
CNS central nervous system
COMT catechol-o-methyltransferase
COSMIC catalogue of somatic mutations in human cancer
COVID-19 coronavirus disease 2019
COX cyclooxygenase
COX-2 cyclooxygenase-2
CPUs central processing units
CQ chloroquine
CVDHD cardiovascular disease herbal database
CVDs cardiovascular disorders
DAMPs damage-associated molecular patterns
DAPD diabetes-related proteins database
DENV dengue virus
DIVA diverse information, visualization, and analysis
DKD diabetic kidney disease
DL deep learning
DM diabetes mellitus
DNA deoxyribonucleic acid
DNN deep neural networks
DS discovery studio
DTI drug-target interactions
EAD epoxy anthraquinone derivatives
EBOV Ebola virus
ECFPs extended connectivity fingerprints
EHRs electronic health records
EMEA Europe by European Medicine Agency
ENL erythema nodosumleprosum
EORD European Organisation for Rare Diseases
EVD Ebola virus disease
FALS familial type ALS
FDA Food and Drug Administration
FEP free energy perturbation
FN false-negative
FP2 fingerprint 2D
GARD genetic and rare diseases
GAU Gaussian scoring function
GEO Gene Expression Omnibus
GIP glucose-dependent insulinotropic peptide
GLP glucagon-like peptide
GLP-1 glucagon-like peptide 1
GLP1 glucagon-linked peptide-1
GM-CSF granulocyte-macrophage-colony stimulating factor
GOF gain of function
GP Gaussian process regression
GPCR G-protein coupled receptors
GPUs graphics processing units
GTEx genotype-tissue expression
GWAS genome-wide association studies
HBA hydrogen bond acceptor
HCC hepatocellular carcinoma
HCQ hydroxychloroquine
HCS high content screening
HCV hepatitis C virus
b-HEX b-N acetylhexosaminidase hexosaminidase A
HGP human genome project
HLA human leucosite antigen
HMDB human metabolome database
HMGB1 high mobility group box 1 protein
HPLC high-performance liquid chromatography
HTS high-throughput screening
IBD inflammatory bowel disease
IC inhibitory concentration
ICAM-1 intercellular adhesion molecule 1
ICD International Classification of Diseases
IE information extraction
IgE immunoglobulin E
IL interleukin
IMiDs immune modulating drugs
IND investigational new drug
iNOS inducible nitric oxide synthase
IP intellectual property
IPR intellectual property rights
iPSCs induced pluripotent stem cells
xiii Abbreviations
IR information retrieval
JEV Japanese encephalitis
KD knowledge discovery
KEGG Kyoto Encyclopedia of Genes and Genomes
kNN k-nearest neighbors
KPLS kernel-based PLS
LBDD ligand-based drug design
LDK Lenaldekar
LINCS library of integrated network based cellular signatures
LMWH low molecular weight heparin
LOF loss of function
LUTS lower urinary tract symptoms
MAB multi-armed bandit
MACCS molecular ACCess system
MAO-B monoamine oxidase-B
MARS multivariate adaptive regression splines
MCP monocyte chemoattractant protein
M-CSF macrophage colony-stimulating factor
MD molecular dynamics
MDR multidrug resistance
MERS Middle Eastern respiratory syndrome
MHLW Ministry of Health, Labour, and Welfare
MIP-1α macrophage inflammatory protein-1α
ML machine learning
MMP matrix metalloprotien
MND motor neuron disease
MoA mechanism of action
MOE molecular operating environment
MPA mycophenolic acid
MPI message passing interface
mRNA messenger RNA
MS multiple sclerosis
MTD maximum tolerated dose
mTOR mammalian target of rapamycin
MTU methylthiouracil
NAM negative allosteric modulators
NAMD nanoscale molecular dynamics
NBC naïve Bayesian classifiers
NCBI National Centre for Biotechnology Information
xv Abbreviations
NCEs new chemical entities
NEN niclosamide ethanolamine
NER name entity recognition
NF-kB nuclear factor kappa-light-chain-enhancer of activated B
cells
NFT neurofibrillary tangles
NGS next generation sequencing
NK natural killer
NLRs NOD-like receptors
NMR nucleo magnetic resonance
NN neural network
NN neural networks
NORD National Organization for Rare Disorders
Nrf2 nuclear factor erythroid 2 (NFE2)-related factor 2
NRTIs nucleoside reverse transcriptase inhibitors
NSAIDs nonsteroidal anti-inflammatory drugs
NTD neglected tropical diseases
OD orphan diseases
ODA Orphan Drug Act
ODC ornithine decarboxylase
p38 protein kinase 38
PAD peripheral artery disease
PAM positive allosteric modulators
PAMPs pathogen-associated molecular patterns
PCB paclitaxel coated balloon
PCM paracoccidioidomycosis
PCM proteochemometric
PCNA proliferating cell nuclear antigen
PD psychotic depression
PD Parkinson’s disease
PDB protein database
PDE-5 phosphodiesterase-5
PDE5is phosphodiesterase 5 inhibitors
PDIF protein atom score contributions derived interaction
fingerprint
PDL1 programmed death-ligand 1
PDTD potential drug target database
PGD2 prostaglandin D2
PNS peripheral nervous system
PPAR peroxisome proliferator–activated receptor
PPI protein-protein interactions
PPMS primary progressive multiple sclerosis
PPV positive-predictive value
PRISM profiling relative inhibition simultaneously in mixtures
PVR pulmonary vascular resistance
QED quantitative estimate of drug-likeness
QSAR quantitative structure–activity relationship
R&D research and development
RA rheumatoid arthritis
RAGE receptor for advanced glycation end products
RAR-α retinoic acid receptor alpha
RCSB-PDB Research Collaboratory for Structural Bioinformatics-
Protein Data Bank Website
RD reverse docking
RDBD Rare Disease Repurposing Database
RDCRN rare diseases clinical research network
RDL relative drug likelihood
rDNA recombinant DNA
RF random forests
Rg radius of gyration
rGyr radius of gyration
RLRs RIG-like receptors
RLS restless legs syndrome
RMSD root-mean square deviation
RMSF root-mean square fluctuation
RNA ribonucleic acid
RNS reactive nitrogen species
ROS reactive oxygen species
RP recursive partitioning
RRMS relapsing-remitting phase
SALS sporadic ALS
SAR structure-activity relationship
SARS severe acute respiratory syndrome
SARS-CoV-2 syndrome coronavirus 2
SBDD structure-based drug design
SBVS structure-based virtual screening
SE standard error
SEA similarity ensemble approach
Abbreviations xvii
SGB stochastic gradient boosting
SGLT2 sodium-glucose transporter 2
SLE systemic lupus erythematosus
SMA spinal muscular atrophy
SMILES simplified molecular input line entry specification
SNP single-nucleotide polymorphisms
STEMI ST-segment elevation myocardial infarction
SVM support vector machines
T1D type 1 diabetes
T2D type 2 diabetes
TCGA the cancer genome atlas
TI thermodynamic integration
TLR toll-like receptors
TLR2 toll-like receptor 2
TM text mining
TMFS train-match-fit-streamline
TMS template modeling score
TNF-α tumor necrosis factor alpha
TP true- positives
TTD therapeutic target database
TZA thiazolidinediones
VCAM-1 vascular cell adhesion protein 1
VEGF vascular endothelial growth factor
vHTS virtual HTS
VS virtual screening
WHO World Health Organization
WHO work of heart
WOMBAT world of molecular bioactivity
ZIKV Zika virus
α-Syn α-Synuclein
Preface
Drug repurposing (DR) is also known as drug repositioning, drug reprofiling,
drug redirection, and therapeutic switching. It can be defined as a process of
identification of new pharmacological indications from old/existing/investigational/FDA-approved drugs, and the application of the newly developed
drugs to the treatment of diseases other than the drug’s original/intended
therapeutic use.
Traditional drug discovery is a time-consuming, laborious, highly
expensive, and risky process. The novel approach of drug repositioning
has the potential to be employed over traditional drug discovery program
by reducing the high monetary cost, longer duration of development, and
increased risk of failure. In recent years, the drug repositioning strategy
has gained considerable momentum with about one-third of the new drug
approvals corresponding to repurposed drugs which currently generate
around 25% of the annual revenue for the pharmaceutical industry.
The application of computational tools and techniques for the prediction
and exploration of the pharmacological effects of developing drug candidates
or lead molecules offers a signicant hope in the current drug discovery
programme because it is inexpensive, time-saving, less risky, and accurate.
The use of computational techniques in discovery research does not only
help in the discovery of new drugs from leads or existing drug molecules, but
can also be useful for the repurposing of existing drugs.
This book is primarily aimed at delivering information on various
computational techniques, tools and databases utilized for drug repurposing
and identifying the uses of existing drug candidates on different emerging
diseases. The most recent coronavirus diseases-2019 (COVID-19) pandemic
is a clear indication that there is a dire need for advanced in silico tools and
computational techniques for drug discovery.
The book titled Drug Repurposing and Computational Drug Discovery:
Strategies and Advances provides recent advances in drug repurposing
and computational approaches pertaining to drug discovery research with
potential applications in various major and emerging therapeutic areas. The
content of the book comprising a denite number of interesting chapters is
based on some specic and relevant topics aiming at the focal theme of the
book. Each chapter delineates up-to-date and in-depth information in a lucid,
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