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12.2 etwork Pharmacology: Practical Guide 263
Table 12.1 (Continued)
Sr. no. Database name Brief description Use of database
24 NetMiner Network analysis tool Network analysis and
visualization
25 NetPath Pathway database Signaling pathways in cancer
26 NetworkX Python library for network
analysis
Network analysis and modeling
27 OPHID Online predicted human
interaction database
Predicted human protein
interactions
28 Pajek Network analysis tool Network analysis and
visualization
29 PDB Protein Data Bank 3D structure of biological
macromolecules
30 PDTD Potential drug target database Drug target prediction
31 PharmGBK Pharmacogenomics knowledge
base
Pharmacogenomic information
32 PubChem Chemical database Chemical information and
bioassay data
33 PubMed Biomedical literature database Biomedical literature search and
retrieval
34 Reactome Pathway database Biological pathways and
reactions
35 SignaLink Signaling pathway database Integration of pathway and
network information
36 STITCH Chemical–protein interaction
database
Chemical–protein interactions
37 STRING Protein–protein interaction
database
Protein–protein interactions
38 SwissTargetPrediction Target prediction for small
molecules
Prediction of drug targets for
small molecules
39 TCMGeneDIT Traditional Chinese medicine
gene database
Information on genes related to
TCM
40 TCMSP Traditional Chinese medicine
systems pharmacology
database
(Traditional Chinese Medicine)
and drug information
41 IMPPAT (Indian
Medicinal Plants,
Phytochemistry and
Therapeutics)
Indian medicinal plants database Information of Indian medicinal
plants
42 TTD Therapeutic target database Information on therapeutic
targets
43 Ucinet Social network analysis software Social network analysis and
visualization
44 UniProtKB Universal protein resource Protein sequence and functional
information
45 TargetScan miRNA target prediction Prediction of miRNA targets in
genes
12 Network-based Methods in Drug Discovery264
Well-organized databases are crucial for in silico drug discovery, especially for plants, their
components, and chemical combinations. Databases on edible and herbaceous plant items exist;
however, traditional Indian medicine databases have not been extensively developed. These data-
bases should collect phytochemical data for drug discovery. The IMPPAT highlights Polur et al.’s [51]
compendium of Ayurvedic Indian medicinal plants and their phytochemical ingredients. However,
their efforts were limited compared to Chinese medicinal herb databases [52]. The authors provide
the rigorously produced IMPPAT database comprising 1742 Indian medicinal plants, 9596 phyto-
chemicals, and 1124 therapeutic applications to solve this deficiency. IMPPAT offers a nonrepetitive
virtual chemical library with 2D and 3D structures to be more comprehensive than previous data-
bases [53, 54]. Calculated physicochemical characteristics and phytochemical ADMET properties are
present in the database. The researchers assessed phytochemical drug-like characteristics using
cheminformatics [55]. They used Lipinski’s rule of five [56], Oral PhysChem score [56, 57], Pfizer’s
3/75 [58], GlaxoSmithKline’s 4/400 [59], Veber rule [60], and Egan rule [61]. Based on these ratings,
960 phytochemicals may be medication candidates (Table 12.2). IMPPAT also predicts correlations
between phytochemicals and STITCH human target proteins. The sentence stresses the relevance of
IMPPAT, the various descriptions of the database are represented in Figure 12.4, and also known to
Table 12.2 The table includes feature comparison tables from several databases, including IMPPAT,
Polur et al., Phytochemica, KNAPSACK, and TCMID.
Feature IMPPAT
Polur et al.
(2011)
Phytochemica
(2013)
KNAPSACK
(2010)
TCMID
(2014)
Number of Indian medicinal plants 1742 295 5 N/A 6000+
Number of phytochemicals 9596 1829 963 500,000+ N/A
Plant–phytochemical associations Present Present Present Present Present
Plant-therapeutic use associations Present Present Present N/A Present
Plant–medicinal formulation
associations
Present N/A N/A N/A Present
Phytochemical–human target protein
associations
Present N/A N/A N/A N/A
Plant part-phytochemical associations N/A N/A N/A N/A N/A
Web interface Present N/A Present Present Present
2D phytochemical structures Present N/A Present Present N/A
3D phytochemical structures Present N/A No Present N/A
Downloadable structure file formats PDB and
PDBQT, MOL2
N/A N/A Present N/A
Chemical classification Present N/A N/A Present Present
Physicochemical properties Present N/A N/A Present No
ADMET properties Present N/A N/A N/A N/A
Druggability properties Present N/A N/A N/A N/A
Cytoscape network visualization Present N/A N/A N/A N/A
Filter by physicochemical properties Present N/A N/A N/A N/A
Filter by druggability properties Present N/A N/A N/A N/A
Chemical similarity search Present N/A N/A Present N/A
Physicochemical
properties
Molecular weight (g/mol)
Log P
Number of aliphatic carbocycles
Number of aliphatic heterocycles
Number of aromatic carbocycles
Numbr of aromatic heterocycles
Total number of rings
Number of saturated carbocycles
Number of saturated heterocycles
Number of saturated rings
Number of smallest set of smallest
Rings (SSSR)
Number of Lipinski’s rule of 5
violations
Lipinski’s rule of 5
Number of Ghose rule violations
Ghose rule
Veber rule
Egan rule
GSK 4/400 rule
Pfizer 3/75 rule
Weighted quantitative estimate of
drug-likeness (QEDw) score
Drug-likeness
properties
Bioavailability score
Solubility class [ESOL]
Solubility class [Silicos-IT]
Blood brain barrier permeation
Gastrointestinal absorption
Log Kp (Skin permeation, cm/s)
Number of PAINS structural alerts
Number of Brenk structural alerts
CYP1A2 inhibitor
CYP2C19 inhibitor
CYP2C9 inhibitor
CYP2D6 inhibitor
CYP3A4 inhibitor
P-glycoprotein substrate
ADMET
properties
2D & 3D Chemical
structures
SDF MOL MOL 2
PDB PDBQT
Predicted human
target proteins
Obtained from
STITCH database
Phytochemical
classification
Smiles NP-Likeness
2D formats
NP classifier biosynthetic pathway
Classyfire Kingdom
3D formats
Indian medicinal
plant
Plant part
Phytoconstituents
Plant based gene function annotation
Taxonomical classification
Synonym and common names
System of medicine
FRLHT plant information
World flora online
Medicinal plant details
Figure 12.4 IMPPAT database features and various features of the database is highlighted in the image form.
12 Network-based Methods in Drug Discovery266
be the largest database of Indian medicinal plant chemical compounds. It is a comprehensive
platform for using cheminformatic methodologies to speed up traditional Indian medicine medica-
tion discovery [62, 63].
Therapeutic Use of Indian Botanical Species: IMPPAT also collects ethnopharmacologi-
cal data on Indian medicinal herbs. We meticulously collected Indian medicinal plant thera-
peutic uses from Indian traditional medicine literature to achieve this goal. Polur et al. [18]
created a Nutrichem9,10-derived inventory of therapeutic cures uses for 295 Ayurvedic Indian
medicinal plants in addition to literature. We have chosen and evaluated Indian medicinal
plant therapeutic data to ensure quality [64]. We purposely avoided automated text mining to
learn more about plant-therapeutic qualities. IMPPAT database includes carefully gathered
therapeutic applications of Indian medicinal plants from traditional Indian medicine books.
These texts contain a plethora of medical knowledge from experience. In addition, we used
identifiers from the Disease Ontology68, OMIM, UMLS, and MeSH databases to manually clas-
sify and standardize therapeutic applications of Indian medicinal plants. To the best of
IMPPAT’s understanding, this is the initial effort to bridge the gap between traditional Indian
medicine and the scientific terminology used in contemporary medicine. The usage of OMIM,
UMLS, and MeSH identifiers is crucial in databases that provide information regarding gene–
disease and disease–symptom associations. It will be simple to integrate these sources’ data into
IMPPAT going forward [65, 66].
12.2.1.3 Target Genes of Phytoconstituents
Targets for human phytochemical proteins indicated. Using the STITCH database (http://stitch1.embl.de/),
we were able to identify the human target proteins of IMPPAT phytochemicals. The STITCH data-
base has more anticipated chemical–protein interactions than any other source. From STITCH,
only interactions between human proteins and phytochemical targets with a total score of 700
or above were revealed (Figure 12.5). The STITCH database, like the TCM-Mesh16 database for
traditional Chinese medicine, was utilized to anticipate IMPPAT phytochemical–target human
protein interactions [62].
Recent Updates and Addons and Future Use of IMPPAT Database IMPPAT 2.0 is a rich repository of
Indian medicinal plants and their valuable properties, akin to a treasure trove brimming with
medicinal secrets. This extensive database reveals a quantity of plants that is four times greater
than its previous version, thereby doubling the known collection of therapeutic compounds
contained within them. Furthermore, it explores further by establishing correlations between
distinct botanical components and their distinct chemical compositions, so uncovering the exact
origins of natural treatments. IMPPAT 2.0 is not merely a compilation of names and lists; rather, it
is an advanced instrument for conducting scientific investigations. Every chemical is extensively
characterized, including detailed 3D structures, potential targets within the human body, and even
forecasts regarding their potential as therapeutics. It serves as a connection between old wisdom
and modern medicine by establishing a correlation between traditional healing terminology and
Western medical comprehension. However, this treasure chest still has undisclosed mysteries.
Collecting and standardizing information regarding the historic usage of these plants in
formulations continues to be a difficult task. Data sharing regulations pose an additional obstacle.
Nevertheless, the future appears promising [67].
12.2 etwork Pharmacology: Practical Guide 267
12.2.2 Network Analysis and Visualization
A Cytoscape StringApp for Visualizing and Analysing Proteinomics Data of Network pharmacology.
Also used to visualize and analyze the compound and their related genes network, features are
enlisted in Table 12.3.
The stringApp is built using the Cytoscape 3.6 App API and is implemented in Java. This app’s
primary purpose is to link Cytoscape to the databases and APIs of the STRING web service; second,
it provides visualizations, including side panels and enrichment features, that are comparable to
those on the STRING web server. Together, these two sites make it possible to use Cytoscape and
its many applications to analyze the network and its associated data by integrating the STRING
website into Cytoscape. As seen in the following use example, the clusterMaker2 app4 is helpful
for clustering STRING networks. To retrieve data from databases and the internet, the stringApp
bridge feature makes use of RESTful11 web service APIs. The application resolves protein and
protein/compound query phrases to internal database identifiers using the STRING and STITCH
API. To get the protein list for a given ailment, the DISEASES database API is called twice: first to
convert the name of the sickness to an identifier, and again to acquire the name of the illness itself.
For all three kinds of searches, StringApp lets users manually resolve confusing names. Handling
PubMed queries was discussed in the preceding section. No matter what kind of query you run,
Figure 12.5 This image demonstrates the visualization of molecular clouds. When it comes to the
phytochemicals of IMPPAT 2.0, the scaffold’s frequency is directly proportional to the structure’s size.
Source: Adapted from Lobell et al. [57].
12 Network-based Methods in Drug Discovery268
Table 12.3 This table represents the features of Cytoscape StringApp with its input and output.
Feature Input Output
Import STRING network STRING network file (.txt, .xls, etc.) Network visualization in cytoscape
Integrate data from
associated databases
Gene/protein IDs or names Annotations, pathways,
functional data from associated
databases
Visualize network
properties
None (uses existing network data) Network statistics, node degree
distribution, clustering
coefficients, etc.
Filter network by
properties
Node/edge attributes, thresholds Subnetwork containing only nodes/
edges meeting criteria
Analyze network
topology
None (uses existing network data) Identification of hubs, modules,
communities, potential functional
relationships
Perform enrichment
analysis
Gene/protein list or network cluster Overrepresented GO terms, pathways,
protein domains, etc.
Overlay experimental
data
Information derived from
experiments (such as gene
expression or protein–protein
interactions)
Visualization of data on the network,
identification of patterns and
relationships
Export network and
analysis results
None (uses current network and
results)
Network in various file formats,
analysis results in tables or charts
STRING: protein query Protein names or IDs Network of interactions for the
queried proteins
STRING: disease query Disease name or ID Network of proteins associated with
the disease
STRING: PubMed query PubMed ID Network of proteins mentioned in the
PubMed article
STITCH: protein/
compound query
Protein or compound name or ID Network of interactions between
proteins and compounds
Expand network Selected nodes in the current
network
Expanded network including
neighbors and interactions of selected
nodes
Query for additional
nodes
Node names or IDs Adds specified nodes to the current
network
Change confidence New confidence score threshold Updates network edges based on the
new confidence level
Retrieve functional
enrichment
Gene/protein list or network cluster Overrepresented functional terms or
pathways (requires additional
plugins)
Filter enrichment Enrichment results, selection
criteria
Filtered list of enriched terms based
on specified criteria
Draw charts Network data or analysis results Visual charts representing
network properties or analysis
outcomes
12.3 Ayurveda and raditional Indian Medicine 269
StringApp will be able to access the data you need from the dedicated PostgreSQL database over
the web service API. To facilitate network growth, confidence cutoff decrease, or node addition,
the second API retrieves data from nodes or edges [68].
12.2.3 Applications of Network Pharmacology in Drug Discovery
The current drug development paradigm involves creating highly selective ligands that interact
with therapeutic targets. However, many effective drugs modulate many proteins instead of one.
Recent systems biology discoveries have revealed amazing resilience in living organisms and exten-
sive connectivity among biological components. Highly specialized compounds may be less suc-
cessful in clinical settings than drugs that target several biological targets, which is undesirable [69].
The growing understanding of polypharmacology has major implications for resolving drug devel-
opment’s main failures, efficacy, and toxicity. Network biology and polypharmacology may expand
drug development targets [70]. However, the need for creative methods to verify combinations of
targets and improve structure–activity links while keeping drug-like properties hinders polyphar-
macology’s logical progress. These advances are laying the groundwork for network pharmacology,
a drug discovery paradigm change. Understanding the “drug–target–gene–disease” network is cru-
cial to developing the network pharmacology system. We can now discover or predict how indi-
vidual or mixed Chinese medicines treat diseases through their network associations. After
evaluating many literature sources, the database was searched for chemical ingredients in tradi-
tional Chinese medicines and compound medications. Database searches revealed these chemical
components’ targets. After finding the drug’s composition and target, the disease’s gene target was
found by reviewing the literature retrieval database again [71]. We created the “drug–target–gene–
disease” relationship network through integration and analysis. Afterward, a network pharmacol-
ogy system tailored to individual diseases was developed. The pharmaceutical industry has invested
much in R&D, yet there has been a dramatic decline in new treatment alternatives, leading some to
wonder if it is useful to focus drug discovery efforts on a single target. In these cases, the novel
approach to drug discovery that is network pharmacology is useful. In contrast to conventional
methods, they place an emphasis on the capacity of medications to target several disease-related
proteins or networks. The development of drug–target disease network models is facilitated by
bioinformatics and high-throughput screening. Drug effects on biological networks can be better
understood with the use of these techniques, which assess drug–target model interactions. New
insights from network biology have shed light on the mysteries of route interactions. As a result,
network pharmacology is becoming increasingly important in the fight against the most lethal dis-
eases and conditions [6]. By analyzing protein–protein interactions, network pharmacology is able
to determine how a disease manifests. Researchers are now studying an increasing variety of com-
plex disorders using multiomics and computational methods to accurately record human metabolic
responses. In this chapter, we take a look at network pharmacology in the biomedical field [11, 72].
12.3 Ayurveda and Traditional Indian Medicine
12.3.1 Overview of Ayurveda and Its Complex Formulations
An increasing number of individuals are choosing Ayurveda over allopathic treatments due to its
cost-effectiveness and reduced incidence of adverse effects. The significance of organic resource-
based goods as supplies for pharmacotherapeutic and complementary and alternative therapies is
12 Network-based Methods in Drug Discovery270
increasing in contemporary times [73]. The extensive Ayurvedic pharmacopoeia, which has been
used for centuries in different parts of India, contains minerals and metals compounds known for
their therapeutic properties [74]. According to Ayurveda, any material could have medicinal proper-
ties. Mineral and metal compositions, such Lauha Kapibadkva, Rasayana, and Bhasma, are men-
tioned in Ayurveda. Avleha, Asavaristra, Grafa Churena, and Taila are botanical remedies. Is the use
of herbs, the exclusive focus of Ayurveda, or may other therapeutic modalities that adhere to
Ayurvedic principles also make use of herbs? The moment has come to deliver information that is
both clear and precise. In order to advance Ayurveda, each task or project calls for a unique study
methodology [74]. The four primary fields of study are literacy, fundamental science, medicines, and
clinical research. The utilization of natural drug development products has been impeded by the
relatively limited quantity of active constituents and the challenges associated with the extraction
and isolation procedures in natural medicine. Hence, it is preferable to discover more efficient meth-
ods of extracting and isolating bioactive substances present in the natural environment. The active
constituents of natural medicines are chemical compounds that possess medicinal qualities [75].
Arista and Asava: Herbs that are powdered or decocted are soaked in sugar or jaggery for a set
period of time to make these sweets. Herbal active ingredients can be extracted through the slow
fermentation process that produces alcohol. To make necessary liquids, the ancients suggested fer-
menting juices or decoctions. In the course of manufacturing, ethyl alcohol is generated. Sadhana
Kalpana dose kinds (Asava, Arishta, and Kanji) are utilized in daily Ayurvedic treatment [76, 77].
The three-part mixture is then homogenized by turbidity. After the bhasma has been properly pre-
pared, it should be rubbed between the index finger and thumb. The pulverized bhasma particles
float on water and settle into finger ridges. Borax, jaggery, ghee, honey, and fruits of the Abrus preca-
torious Linn tree should never be allowed to return to their original mineral or metal condition once
heated [78]. The main methods for preparing bhasma are shodhana, which means purification, and
maarana. The terms “vira,” “vati,” and “gutika” describe the pill forms used in Ayurveda. Minerals,
animal products, and herbal medicines go into its composition. The circular dose is known as gutika.
This looks like the medicines that people take today. While Ghanavati is best chewed, Vati is best
taken by tablet. The preparation methods of Agnisadhya and Anagnisadhya vati are part of Ayurveda.
Sugar, jaggery, or guggulu are heated to create Agnisadhya vati. Anagnisadhya vati is rendered inert
when heated [79, 80]. Avaleha, Churna products sold by Avaleha are semisolid. Sugar, jaggery, or
sugar candy is added to the boiling decoction to make it sweeter. Once the mixture has cooled and
been well mixed, honey is added. The ingredients and times for medication are as follows: honey,
gingelly oil, salts, alkalis, milk, milk derivatives, and bhasma. To make ghrita, you just mix the decoc-
tion with ghee. Heating ghrita to 105–118 1C removes water [81]. The exudates of the Commiphora
mukul plant are the source of guggulu, also known as Taila Oil. When the exudates are wrapped in
cloth and cooked with Triphala or other herbs, they eventually make it into the solution. Boil until
hard, and then let it dry. After drying, add ghee for a waxy paste (Table 12.4) [82].
12.3.2 Diversity of Ingredients and Bioactive Compounds in Ayurvedic Medicines
Phytobioactive substances have been utilized as traditional medicine for centuries to treat various
disorders worldwide. Multiple studies have extensively documented their medicinal potential
since they are widely regarded as valuable natural sources for developing novel medications that
exhibit enhanced effectiveness and compatibility with living organisms. Recent publications are
progressively incorporating more attributes to their already extensive range of medicinal value [83,
84]. In contemporary lifestyle, there has been a persistent rise in oxidative stress, leading to various
illnesses including diabetes mellitus (DM), cancer, and cardiovascular issues. Improper utilization
12.3 Ayurveda and raditional Indian Medicine 271
of antibiotics leads to the development of antibiotic resistance, which presents significant health
concerns, particularly within hospital settings [85]. Artificial medications are inadequate to
address these issues. It is imperative to investigate natural phytobioactive substances for the treat-
ment of contemporary illnesses. A number of medical issues may be amenable to polyphenols,
alkaloids, and terpenoids, including cancer, inflammation, ulcers, diabetes, platelet aggregation,
microbial resistance, tumors, and oxidative stress (Table 12.5) [86, 87].
12.3.3 Indian Traditional Medicines Recently Breakthroughs in Using Different
Approaches to Treat a Variety of Diseases
Indian traditional remedies have recently achieved significant advancements in disease treatment
through several methodologies. By combining ancient wisdom with current science, these therapies
utilize substances such as phenolics and terpenoids. Formulations rich in phenolic compounds
exhibit antioxidant, anti-inflammatory, and cardiovascular advantages. Terpenoids, ranging from
Table 12.4 This table lists the many ayurvedic formulations, along with the process of their production,
the medicinal advantages of these formulations, and the versions that are commercially available.
Name of
process Process of formulation Medicinal benefits
Example of marketed
formulations
Swarasa Extraction of fresh
juice from plants
Retains active principles, easily
absorbed, used for various
ailments based on plant properties
Amla juice for immunity, Giloy
juice for fevers
Kwatha Decoction of herbs in
water
Concentrates water-soluble active
constituents, good for digestive
and respiratory issues
Triphala decoction for epresent,
Neem decoction for skin
conditions
Churna Powdered herbs or
minerals
Easy to administer, long shelf life,
used for internal and external
applications
Trikatu powder for digestion,
Ashwagandha powder for
energy
Lepa Paste of herbs for
external application
Localized action, reduces
inflammation, soothes pain
Turmeric paste for wounds,
Sandalwood paste for relaxation
Taila Medicated oil prepared
by infusing herbs in
carrier oil
Penetrates deep tissues, nourishes
skin, used for massage and pain
relief
Dhanwanthari Taila for joint
pain, Mahanarayan Taila for
muscle weakness
Ghrita Medicated ghee
prepared by boiling
herbs in ghee
Nourishes body tissues, enhances
digestion, supports specific
conditions
Ashwagandha Ghrita for
memory, Chyawanprash for
immunity
Asava/
Arishta
Fermented decoction
of herbs with honey or
jaggery
Enhances bioavailability of herbs,
promotes gut health, supports
various systems
Ashwagandha Arishta for stress
management, Dashmoolarishta
for digestion
Bhasma Calcination of minerals
or metals using herbal
fire
Increases potency, removes
impurities, used for specific
conditions like anemia, infertility
Abhrak Bhasma for respiratory
issues, Swarna Bhasma for
mental clarity
Vatika Pills made from
powdered herbs and
binding agents
Easy to administer, controlled
dosage, long shelf life
Triphala Vati for constipation,
Ashwagandha Vati for stress
Rasayan Rejuvenating
formulations for long
life and health
Antioxidant, adaptogenic,
promotes tissue repair, slows
aging
Chyawanprash, Chyavanashvati
12 Network-based Methods in Drug Discovery272
Table 12.5 The several classes of phytoconstituents were listed in a table format.
Photoactive compounds Subclass/Type Role in treating diseases
Phenolics
Simpler phenols Phenolic acids and ketones Antioxidant, anti-
inflammatory, cardiovascular
health
Phenyl propanoids; coumarins, benzofuran,
chromones, chromenes
Anti-inflammatory,
antimicrobial, anticancer
Phenolic quinines: benzoquinone,
naphthaquinones, anthraquinones
Antimicrobial, antifungal,
wound healing
Polymeric phenolics Lignins Antioxidant, anti-inflammatory
Melanins Skin protection, UV radiation
absorption
Tannins (condensed salts or gallo/epitannins) Astringent, antidiarrheal,
cardiovascular health
Xanthones Antioxidant, anti-inflammatory
Stilbenoid Antioxidant, anti-
inflammatory, cardiovascular
health
Flavonoids C15 heterocyclic nucleus of flavones Antioxidant, anti-
inflammatory, immune system
support
Terpenoids Monoterpenoids
Iridoids
Anti-inflammatory,
antimicrobial, digestive health
Sesquiterpenoids Anti-inflammatory, respiratory
health
Lactones Sedative, antianxiety, digestive
health
Diterpenoid Anti-inflammatory, anticancer
Triterpenoid saponins Immune system support,
anti-inflammatory
Steroid saponins Cardiovascular health,
anti-inflammatory
Cardenolides and bufadienolides Cardiotonic, antiarrhythmic
Phytosterols, curcum bitacums Anti-inflammatory,
cardiovascular health
Nortriterpenoids Antimicrobial, anticancer
Carotenoids Antioxidant, eye health,
immune system support
Glycosides
Steroidal glycosides
Terpenoid aglycone Cardiotonic, antiarrhythmic
Anthracene glycosides Phenolic aglycone reduced anthraquinones
Laxative, anti-inflammatory
Saponin glycosides Terpenoid aglycone Immune system support,
anti-inflammatory