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25
2.1 Introduction
2.1.1 Introduction to Drug Design and Discovery
The process of finding possible new therapeutic entities through the application of computational,
experimental, translational, and clinical models is known as drug discovery. Despite improve-
ments in biotechnology and our understanding of biological systems, the process of finding new
therapeutics is still exceedingly time-consuming, expensive, challenging, and inefficient [1]. The
entire process of creating a new medication, from conception to market release, can take 12–15
years and exceed one billion dollars. Based on the knowledge of a biological target, the inventive
process of finding new medications is known as drug design [2]. Preclinical research on animal
and cell models as well as human clinical trials is all part of the process of developing and discover-
ing new drugs [3]. Afterward, the drug must receive regulatory approval before being put on the
market. Identification of screening hits, medicinal chemistry, and optimization of those hits to
boost their affinity, selectivity (to lower the risk of adverse effects), efficacy/potency, metabolic
stability (to lengthen the half-life), and oral bioavailability are all important components of mod-
ern drug research. Following the discovery of a chemical meeting each of these criteria, the process
of developing a medicine will start before any clinical trials are conducted [4].
Drug design, in its most basic form, is the design of molecules that complement the molecu-
lar target that they interact and bind to in terms of charge and shape. In the big data era,
bioinformatics methods and computer modeling techniques are often, but not always, used in
drug design. Apart from small molecules, biopharmaceuticals, particularly therapeutic
antibodies, are becoming a significant class of medications. Significant progress has also been
made in computational methods for enhancing the stability, selectivity, and affinity of these
protein-based treatments [5].
2
Bioactive Small Molecules and Drug Discovery
Ashish Shah
1
, Vaishali Patel
2
, Sathiaseelan Perumal
3
, Riddhi Dave
4
,
5
, Ghanshyam Parmar
1
, and Jay Mukesh Chudasama
1
1
2
3
4
5
26
2.1.2 Brief History of Small-Molecule Drug Discovery
Numerous small-molecule medications have enhanced patient outcomes and advanced medicine
throughout history. While some of these pioneering medications remain on the market today, oth-
ers have vanished from use but played a significant role in opening the door for more effective
therapies within their particular use. For almost a century, small molecules have been crucial in
producing medical advancements for human ailments. The quick development of biopharmaceu-
tical research and technology creates opportunities for fresh, imaginative methods of creating
small-molecule medications [6].
For almost a century, small-molecule medications have served as the foundation of the pharma-
ceutical business. Small-molecule medications, which are defined as any organic substance with a
low molecular weight, offer several unique therapeutic benefits. The majority are oral medications
that can cross cell membranes and reach intracellular targets [7]. Because of these qualities, small-
molecule medicines are becoming more popular in the pharmaceutical industry and are excellent
candidates for therapeutic development. Because of their inadequate efficacy or severe side effects,
the medications now employed in clinical practice are insufficient, even with the developments in
molecular genetics and the creation of new, efficient drug discovery techniques. For this reason,
creating novel, effective medications is crucial to the prevention and treatment of disease [8].
2.1.3 Importance of Bioactive Small Molecules in Drug Discovery
Their inherent qualities, such as their capacity to pass through biological barriers and affect vari-
ous distinct biological targets, have played a significant role in their effectiveness as medications.
Majority of small-molecule medications have oral bioavailability, which allows for conventional
oral administration as a tablet [9]. When compared to biologics, this convenience factor is a signifi-
cant benefit, even though the pharmacological impact size may be comparable. For example,
recombinant biologic erythropoietin (EPO) can be treated for renal anemia with small-molecule
inhibitors (HIF-PH), which can be taken as a pill instead of an injection or infusion. The modular
structure and chemical synthesis accessibility of tiny molecules are further essential features [10].
Because of this, their chemical structures may be quickly varied, and their attributes can be
improved in a systematic way. The unaltered tiny molecule is the primary component of the major-
ity of pharmaceutical preparations. The unaltered small molecule is typically the active ingredient
in drug formulations. Prodrugs, or appropriate reversible chemical changes, can be created to com-
pensate for possible drawbacks such as low water solubility. Targeted activation or transport of a
pharmaceutical component toward a target tissue can also be accomplished using a comparable
method. Lastly, most medication formulations and administration routes are compatible with
small molecules, and they often have a high shelf life [11]. For a more comprehensive overview of
small-molecule properties, see Table 1.
2.2 Importance of Computational Methods in Bioactive
Small-Molecules Discovery
Computational techniques play a crucial role in the search for bioactive small molecules, making
them a powerful and efficient strategy for drug development. Molecular docking, synthetic
screening, molecular dynamics (MD) models, and quantitative structure–activity relationship
(QSAR) models are all components of these methodologies. The ability of computational
27
methods to predict potential bioactive compounds and their interactions with target proteins,
thereby decreasing the number of candidates that need to be experimentally validated, is a sig-
nificant advantage in the process of drug discovery [12]. The intricate molecular mechanisms
underlying drug–target interactions can be comprehended more effectively through the utiliza-
tion of these approaches, thereby facilitating the rational development of novel compounds pos-
sessing enhanced pharmacological attributes [13]. Computer programs also assist in the search
for novel chemical entities with favorable therapeutic characteristics by enabling the identifica-
tion of structurally diverse molecules. Integrating computational methodologies provides an effi-
cient and cost-effective method for identifying potential therapeutic candidates, as traditional
drug development can be resource-intensive and time-consuming. Computational methodolo-
gies are essential in the detection of bioactive small molecules since they enhance the velocity,
efficacy, and cost-efficiency of pharmaceutical research and development. Consequently, this
enables us to tackle medical demands that have not been fulfilled and enhance the results for
patients [14].
2.2.1 Structure-Based Methods
Computational techniques for the discovery of small bioactive compounds rely heavily on structure-
based methods. To aid in the development of pharmaceuticals, these techniques make use of the
three-dimensional structures of biological macromolecules, such as proteins or nucleic acids. One
of the most well-known methods in this area is molecular docking, which uses computational
methodologies to model the binding geometry of small molecules (ligands) with target proteins in
order to anticipate how these two would interact [15]. To aid in the selection of potential medica-
tion candidates, this method makes it easier to identify potential binding sites and evaluate the
binding strength of ligands [16, 17]. In order to find compounds that can bind to a certain target
protein, structure-based virtual screening methodically searches through large compound librar-
ies [18, 19]. This approach helps reduce the time and resources needed for hit identification by
prioritizing compounds for experimental testing. By showing how biomolecular complexes behave
dynamically over time, MD simulations improve structure-based methods and provide a better
understanding of ligand binding and stability [14]. Also, structure-based methods are useful for
developing fragment-based drugs or designing novel small compounds through de novo ligand
design. In de novo ligand design, new compounds with desired properties are generated computa-
tionally, whereas in fragment-based approaches, smaller molecular fragments are found and opti-
mized to form a ligand with high binding affinity. Finding bioactive tiny compounds relies heavily
on structure-based approaches. By utilizing these tools, we may make informed predictions about
ligand–target interactions, virtually screen compound libraries, analyze molecules’ dynamic
behaviors, and rationally create new therapeutic candidates [20]. The use of these computational
techniques considerably improves the efficacy and efficiency of medication development
(Table 2.1).
2.2.2 Ligand-Based Methods
In order to infer information about possible medication candidates, ligand-based approaches to
bioactive small-molecule discovery examine the features and qualities of already-known bioactive
molecules. An alternative to using target protein structures, these strategies make use of ligand-derived
data [21]. The process of discovering new drugs often makes use of a few essential ligand-based
methods enlisted in Table 2.2.
28
QSAR models are used to create mathematical relationships between the chemical struc-
tures of ligands and their observed biological activities. QSAR models evaluate datasets of
known bioactive chemicals and predict the activity of new molecules to aid in the design of
more potent drugs or those with fewer adverse effects. Pharmacophore models are extremely
useful for determining the essential structural and spatial components involved in ligand
binding to a target [22]. These models guide medicinal chemists in constructing compounds
that imitate the required interactions, thereby facilitating the design of new molecules by
emphasizing crucial characteristics. Molecular fingerprints enable the comparison of ligand
structures by converting molecular information into a binary format. Comparable finger-
prints facilitate the virtual screening of potential drug candidates and the search for similar
Table 2.1 Structure-based methods for drug discovery.
Structure-based
methods Description
Docking process Uses simulated binding geometry to predict how a tiny chemical (ligand) will
interact with a target protein. A useful tool for determining binding affinity and
locating possible binding locations
Structure-based
virtual screening
Systematically screens large compound libraries to identify molecules with the
potential to bind to a specific target protein. Prioritizes compounds for
experimental testing, saving time and resources
Molecular dynamics
simulations
Provides insights into the dynamic behavior of biomolecular complexes over
time. Enhances understanding of ligand binding and stability, contributing to a
more comprehensive analysis
De novo ligand design Generates new molecules computationally with desired properties. Facilitates
the design of novel small molecules for specific targets
Fragment-based drug
discovery
Identifies and optimizes smaller molecular fragments that can be combined to
form a high-affinity ligand. A strategic approach to drug design
Table 2.2 Essential ligand-based methods for drug discovery.
Ligand-based
methods Description
QSAR Statistical approaches are employed to establish a relationship between the chemical
structure and biological activity of a set of ligands. By analyzing the structural
properties of novel compounds, QSAR models can predict their activity
Pharmacophore
modeling
Discovers shared spatial or structural characteristics needed for ligand–target
interactions. New compounds with comparable pharmacological characteristics can
be designed using this three-dimensional arrangement of features as a template
Molecular
fingerprints
Creates a binary fingerprint using molecular characteristics like substructures or
chemical patterns. To help with ligand similarity searches and virtual screening,
fingerprints that are structurally similar are useful
Machine learning
approaches
Uses various machine learning algorithms to draw conclusions about ligand
bioactivity from data, including neural networks and random forests. These
techniques improve predicting abilities by handling complicated interactions among
massive datasets
29
ligands by highlighting structural similarities. Neural networks and random forests are two
machine learning methods that analyze large amounts of ligand data to identify patterns that
can provide insights about the behavior of chemicals in the body. Ligand-based approaches
offer valuable tools for the discovery of bioactive small molecules by utilizing data from
known ligands to forecast the activities and characteristics of novel compounds [23, 24].
These methodologies are highly effective in forecasting the pharmacological characteristics
of novel drugs and in identifying intricate patterns and correlations in intricate datasets.
When there is an absence or inadequate amount of structural data available for the target
protein, numerous strategies seem to be effective [25].
2.2.3 Network-Based Methods
Network-based approaches utilize network theory and analysis to comprehend and forecast
the interactions among biological entities, such as proteins, genes, or tiny molecules. These
methods are especially effective for investigating the intricacy of biological systems and
revealing correlations that contribute to the identification of drugs [26]. Various essential
network-based methodologies are employed in the investigation of bioactive small mole-
cules (Table 2.3).
Protein–protein interactions (PPIs) are illustrated in PPI networks, providing a comprehensive
view of biological activity at a systems level. These networks are useful in drug development as
they help identify core proteins or hubs that could serve as therapeutic targets or play significant
roles in disease pathways. Drug–target interaction networks refer to networks that illustrate the
connections between drugs and the proteins they specifically operate upon. Through the explora-
tion of drug–target interaction networks, researchers can get insights into the polypharmacology
of medications, enabling them to comprehend the mechanisms of therapeutic action by detect-
ing potential off-target effects. Chemical similarity networks were created to facilitate the identi-
fication of compounds that share similar structures. These networks were built using the
chemical similarity of small molecules. Chemical similarity networks facilitate the discovery of
novel drug concepts and the identification of alternative applications for existing ones. A net-
work that displays the interconnectedness among different biological processes is referred to as a
Table 2.3 Network-based methods for bioactive small molecules.
Network-based methods Description
Protein–protein
interaction (PPI)
networks
Represent interactions between proteins in a network format. Analyzing PPI
networks helps identify key proteins or hubs that play crucial roles in cellular
processes, aiding in the identification of potential drug targets
Drug–target
interaction networks
Model the interactions between drugs and their target proteins. Analyzing these
networks provides insights into the polypharmacology of drugs, helping
understand their multiple interactions and potential side effects
Chemical similarity
networks
Constructed based on the similarity of chemical structures between small
molecules. These networks assist in identifying structurally related compounds,
guiding the exploration of chemical space for drug discovery
Biological pathway
networks
Depict the relationships between biological pathways, illustrating how different
pathways interact. Analyzing these networks aids in understanding the broader
context of a target’s involvement in cellular processes
30
biological pathway network. Analyzing these networks allows researchers to get a more compre-
hensive foundation for drug discovery projects by better understanding the potential involve-
ment of a single target in several pathways. Hence, considering the many interconnections
among biological systems, network-based techniques offer a comprehensive methodology for
identifying bioactive small compounds. These methods provide insights into chemical similari-
ties, biological pathways, PPIs, drug–target links, and drug–target linkages, enabling a better
understanding of potential drug candidates and their effects on cellular processes [27–30].
2.3 Natural Products in Bioactive Small-Molecule Discovery
Since ancient times, natural products (NPs) have been the main source of treatment. Traditional medi-
cines like Ayurveda, Chinese medicine, Siddha, and Unani all use NPs as a source of medicine. The key
component of these NPs is bioactive small molecules. Bioactive small molecules are soluble molecules
that can interact and moderate the activity of a cell. Many of these bioactive molecules are derived from
primary and secondary metabolites of plants. They are also obtained from marine sources. These bioac-
tive molecules are mainly used as anticancer, antiviral, and antimalarial agents [31].
2.3.1 Plant Primary and Secondary Molecules as Bioactive Molecules
Chemicals that are fundamental for activities such as respiration, photosynthesis, and transloca-
tion are known as primary metabolites. Although they are not directly engaged, plants may need
secondary metabolites for growth and development [32].
Primary metabolites include amino acids, nucleosides, and carbohydrates. Branched chain
amino acids like valine, leucine, and isoleucine have been found to enhance muscle health. The
consumption of glycine improved the quality of sleep compared to chemical substances like ben-
zodiazepines. Supplementation of cysteine and theanine improved the overall immunity by
antigen-specific antibody production after encountering an antigen. Plants also contain a wide
range of vitamins, which are essential for metabolism. Several vitamins originating from plants,
including pro-vitamins A, B complex, C, E, and K1, have antioxidant properties. Supplements con-
taining these vitamins are available [33].
Secondary metabolites include phenolics, terpenes, steroids, alkaloids, and flavonoids. Phenolics
have antioxidant and anti-inflammatory properties that can moderate inflammatory signaling path-
ways and can serve as bioactive small molecules for anti-inflammatory drugs used in the treatment
of various diseases like cancer. Phenolics also serves as antimicrobial and anticancer compounds.
Flavonoids have antioxidative, anti-inflammatory, antitumoral, and cardioprotective activities.
These are mainly found in plants like cabbage, cauliflower, berries onion, apples, and pears [34].
Terpenes contain many bioactive small molecules like eugenol (from Syzygium aromaticum),
Germacrone (from Eryngium maritimum), and Santalol (from Santalum album), which acts as antivi-
ral drugs against influenza infections. They also serve as anti-inflammatory compounds by inducing
the release of pro-inflammatory cytokines and leukotrienes [35]. Steroids have a great affinity toward
nuclear receptors. This led to the development of drugs derived from steroids for receptor-mediated
diseases. Several anticancer molecules and antihormonal drugs have been synthesized from steroids.
Alkaloids are a major part of Chinese herbal medicine. Alkaloids are majorly found in higher plants,
especially in dicots. Berberine obtained from Coptidis rhizoma acts as an antibacterial and anticancer
drug. Matrine from Sophora flavescens acts as an anti-inflammatory, anti-arrhythmic, and antifibrotic
31
drug. Piperine from Piper nigrum has antioxidant, anti-asthmatic, anticarcinogenic, anti-inflammatory,
anti-ulcer, and anti-amoebic properties. Rhyncophylline obtained from Uncaria rhynchophylla can be
used as a substitute for amphetamine to treat psychological disorders [36] (Table 2.4).
2.3.2 Anticancer Agents as Bioactive Molecules
Developing less toxic anticancer medications is crucial because cancer is one of the top causes of
mortality in the modern era. Bioactive compounds with anticancer properties regulate metabolic
and signaling pathways, thereby limiting and preventing the growth and division of aberrant
Table 2.4 Pharmacological effects of bioactive compounds.
Bioactive
compound Source Effect Reference
Preussiafuran
Cissetin
Asterric acid
Preussia sp. Antimalarial [37]
Piliformic acid Xylaria sp. Antimalarial [37]
Fumoquinone
Pseurotin
Aspergillus sp. Antimalarial [37]
Bostrycin
Nigrosporin B
Fusarubin
Fusarium sp. Antimalarial [37]
Gallic acid Vitis vinifera and
Camellia sinensis
Antioxidant, antimicrobial, anti-inflammatory,
and antitumoral
[38]
Luteolin Brassica oleracea and
Apium graveolens
Antioxidant, anti-inflammatory, and
antitumoral
[38]
Anthocyanins Vitis vinifera and Daucus
carota
Antimicrobial, anti-inflammatory, antioxidant,
and anti-proliferative
[38]
Curcumin Curcuma longa Anti-inflammatory [39]
Paclitaxel Taxus brevifolia Antimitotic agent – various cancers [39]
Artemisinin Artemisia annua Malignant cerebral malaria
Tomatine Solanum lycopersicum Anticancer, immune effects, and antifungal [39]
Oblongolide Phomopsis sp. Antiviral [39]
Illudin S Omphalotus illudens Antiviral [39]
Fucoidans Fucus vesiculosus and
Lonicera japonica
Antioxidative [40]
Fucoidans Fucus vesiculosus Antithrombin, anti-inflammatory [41]
Nisin A Lactococcus lactis Cancers of the head and neck, breast, liver,
and blood cells (acute T cell leukemia)
[41]
Pediocin Pediococcus acidilactici Colon adenocarcinoma [42]
N-butanol fraction Acacia arabica Anti-HIV activity [43]
Methanolic
extract
Ocimum basilicum Inhibition of viral replication [43]
Aqueous extra Olea europaea Inhibition of cell-to-cell transmission of HIV [43]
32
cancer cells. Untailored natural materials or their synthetic versions constituted 47.1% of the 155
anticancer medications that were clinically authorized between 1961 and 2006. Majority of the
bioactive compounds with anticancer properties originate from microbes and plants. The presence
of bioactive molecules like anthraquinones, polysaccharides, and chromones in Aloe vera makes it
an anticancer and antiproliferative drug [44]. Luteolin, a flavonoid acts as an anticancer agent by
inhibiting the histone acetyltransferase (HAT) pathway of cancer cells. HAT is an enzyme that
causes acetylation of chromatin to isomers of histone. In most cases of cancer, elevated levels of
HAT are observed. Vinblastine, Vincristine (from Catharanthus roseus), and Colchicine (from
Colchium autumnale) are alkaloids that inhibit cancer cell migration and metastasis by binding to
microtubulins. Microtubules are cytoskeletal structures that are involved in various processes like
cell migration, maintaining the cell shape, and cell division [45]. Podophyllotoxin is a lignan (sec-
ondary metabolite) derived from Podophyllum peltatum L. It prevents the growth of tumor cells by
preventing polymerization of tubulin thereby arresting mitosis. Etoposide, teniposide, and
etopophos are semisynthetic derivatives of Podophyllotoxin [46].
Certain antibiotics like Actinomycin D, Bleomycin, Doxorubicin, and Mitomycin C are used to
treat cancers like sarcomas, leukemia, and carcinomas. They are derived from Actinomycetes such
as Streptomyces antibioticus, Streptomyces verticillus, Streptomyces peucetius, and Streptomyces
caespitosus, respectively. Actinomycin D treats tumors by inducing p53-independent apoptosis.
Bleomycin cleaves the DNA and causes DNA damage. Doxorubicin blocks replication and tran-
scription process and causes oxidative damage to the cell components. Mitomycin C crosslinks the
DNA strands [42, 47].
2.3.3 Antiviral Agents as Bioactive Molecules
Antiviral drugs are important for treating clinically significant viruses like HIV (human immuno-
deficiency virus), HSV (herpes simplex virus) influenza, and hepatitis C. Most of the existing viral
drugs like zidovudine, acyclovir, and ribavirin have serious adverse effects on the body. Therefore,
development of antiviral drugs with the least side effects is necessary for which natural bioactive
molecules are a great choice. Methanol extracts from various plants like Alchornea laxiflora, Panax
notoginseng, and Senecio scandens have inhibitory effects on HIV integrase. Ethanol extracts from
Justicia gendarussa and Bauhinia variegata and hexane extracts from Aerva lanata, Acorus cala-
mus, and Rhinacanthus nasutus had inhibitory effects on HIV reverse transcriptase [48]. Anti-HSV
activity was shown by ethanol, methanol, and chloroform extracted from plants like Aglaia odor-
ata, Pistacia vera, and Quercus brantii. They halted viral growth by inhibiting viral replication, viral
DNA synthesis, and virus entry. Dichloromethane and methanol obtained from plants like
Mussaenda elmeri and Baccaurea angulata in 1:1 ratio inhibited hemagglutination due to influ-
enza infection [20]. An anti-influenza drug, oseltamivir synthesized from shikimic acid and qui-
nine acid was found to decrease the mortality rate in COVID-19-infected patients [49].
Several bioactive compounds from fungi are also known to have antiviral properties. Ganodeeric
acid in Ganoderma lucidum and adenosine in Cordyceps militaris show inhibitory effects on HIV
protease. Beta glucan protein of Agaricus subrufescens limits the replication of HSV [50].
2.3.4 Antimalarial Agents as Bioactive Molecules
Antimalarial bioactive molecules help combat the malarial infection caused by various Plasmodium
species. Malaria caused by Plasmodium falciparum is considered the most fatal one due to its
nature of causing cerebral infections. The most successful antimalarial drugs, quinine and