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artemisinin, are secondary metabolites of plants [51]. Bisbenzylisoquinoline from Stephania erecta
can restrict the growth of chloroquinone-resistant strains of Plasmodium falciparum. Quassinoids
from Brucea javanica, Simaba guianensis, and Quassia indica are effective against Plasmodium
falciparum. Certain flavonoid compounds like lanaroflavona, volkensiflavone, aetonin B, and
cyclohetrophyllin are effective against malaria. Xanthones like allanxanthone, torophyllin A, and
symphonin have in vitro class antiplasmodial activity against sensitive and resistant strains [52].
Asterric acid and preussiafuran found in Preussia sp. are fungal bioactive compounds, which are
known to be effective against malaria. Some of the other antimalarial compounds found in fungi
are cytochalasin, acremonisol, fumoquinone, and pseurotin [53, 54].
2.3.5 Marine Bioactive Products
In terms of the discovery of bioactive chemicals, marine resources have been the least utilized. The
pharmaceutical, cosmetic, and food industries could all benefit from the substances it contains.
These biomolecules found in the ocean have a stellar reputation for fighting cancer, inflammation,
and free radicals. The phycobilin pigments present in cyanobacteria and red algae have hepatopro-
tective anti-inflammatory properties. Marine algae have great antioxidative properties due to the
presence of compounds like phlorotannin, beta carotene, and astaxanthin [55]. Red and brown
algae and sea cucumber (Ludoigothurea grisea) are rich in sulfated polysaccharides that have
antithrombotic and anticoagulant activities. The anticoagulant activity is due to the presence of
fucoidins. These algae also inhibit replication of enveloped viruses. Nostocmuscorum and
Oscillatoria have antitumor activity against hepatocellular cancer cell lines. HeLa cancer cells were
inhibited by compounds present in Calothrix. Marine cyanobacterial depsipeptide gallinamide A
was found to inhibit cathepsin L, which is used by coronaviruses to cause infection inside the
cells [56]. Plitidepsin obtained from Aplidium albicans reduces the proliferation of cancer and
reduces lung inflammation and viral load in COVID-19 infections. It has also been proved that
plitidepsin has greater therapeutic potential in treating COVID-19 in comparison to remdesi-
vir [57]. Marine bioactive molecules can be used as drug targets and as dietary supplements with
proper research to rule out the possible chances of toxicity.
2.4 Role of Density Functional Theory (DFT) Studies in Bioactive
Small-Molecule Discovery
2.4.1 Importance of DFT in Small-Molecule Drug Discovery
If medicinal chemists want to improve their drug discovery and design procedures, they need bet-
ter computational methods. If we want to learn important things about the electrical characteris-
tics of small-molecule pharmaceuticals and biological systems in a short amount of time, we need
to use the correct quantum mechanical (QM) methods [13]. In QM calculations, conventional
Hartree–Fock (HF) methods may not be able to accurately describe certain properties owing to a
lack of electron correlation, leading to the development of post-HF methods such as the Moller–
Plesset perturbation theory (MPn) and configuration interaction (CI), which consider electron cor-
relation [58]. However, these methods are also computationally expensive. As a result, DFT has
gained popularity as it offers a high level of accuracy with reduced computational time, making it
a cost-effective option compared to other methods. In the drug design and discovery process, DFT
is used to study isolated drug molecules to provide information related to chemical reactivity and
34
global reactivity parameters. DFT geometry optimization is the key method for small molecules, as
it directly determines the electronic probability density and the geometry of molecule is optimized
to provide low energy conformation which is useful for the ligand of greater possible
conformations [59].
Extensive DFT studies of diverse molecular properties have demonstrated the accuracy of
DFT. Moreover, a comprehensive study comparing DFT calculations with experimental data has
confirmed its suitability. The B3LYP functional has been widely utilized in this field of study. Drug
design often focuses on the energetic properties of drug molecules, including their ionization ener-
gies, relative energies, and electron affinities. While DFT can effectively study these properties, it
does not provide reliable information on atomization energies [60]. The process of designing new
drugs requires an understanding of the different types of interactions that occur between drug
molecules and their target sites. DFT calculations accurately predict the strength of hydrogen
bonds and reveal the strength of ionic and covalent interactions between drug-like compounds and
their targets. However, dispersion interactions that affect hydrogen bonds to hydrophobic interac-
tions are not predictable by DFT calculations [59]. DFT calculations cannot provide a reliable
description of systems with low-energy forces, such as those between 1 and 5 kcal/mol [61].
Therefore, when analyzing the interactions between a drug and its target, it is imperative not to
depend exclusively on DFT. The transition states of small pharmacological compounds can be
accurately determined using DFT calculations. Calculations employing B3LYP functionals offer
insights into the drug–target interaction process.
DFT is additionally used with molecular docking and MD simulations to predict drug–receptor
interactions more precisely. In molecular docking, the drug candidate is considered flexible,
whereas the macromolecule (active site) is considered rigid. So, in order to mimic an actual bio-
logical system in which the protein is flexible, the ligand–macromolecule complex is simulated
through MD simulations, which are based on classical Newtonian physics, to address the unrealis-
tic rigidity of a system [61].
The docking and MD simulations reveal drug–target binding mechanisms and potential nonco-
valent interactions between small-molecule drugs and their targets. However, these methods are
expensive. To overcome these disadvantages, integrated QM/molecular mechanics (MM) hybrid
modeling has been utilized. DFT part in QM/MM method studies the enzyme active site where
different possible bonding interactions take place while MM method takes care of the rest of mac-
romolecules. Because of this, force field-based MM methods can effectively simulate the complex
macromolecular portion, which consists of hundreds of atoms, while QM methods calculate elec-
tronic properties like charge transfer at protein target [62]. Some selected DFT functionals used in
molecular modeling are listed in Table 2.5.
2.5 Application of DFT to Bioactive Small Molecules
2.5.1 HOMO–LUMO Calculation
Based on the predictions made from the highest occupied molecular orbital (HOMO) and the low-
est unoccupied molecular orbital (LUMO), DFT calculates the Frontier molecular orbital (FMO) to
find out how many electrons a molecule may take or donate. A large HOMO–LUMO gap suggests
that the pharmacological molecule is relatively unresponsive or inactive. The HOMO–LUMO
energy gap (EHOMO–ELUMO) is inversely proportional to the compounds’ stabilizing interac-
tions in protein–ligand interactions. A number of reactivity metrics are computed for drug
35
molecules, including chemical hardness (ɐ), chemical potential (μ), chemical softness (σ), ionization
potential (I), electron affinity (A), electrophilicity index (ϋ), electronegativity (χ), and dipole
moment (Debye). Extensive calculations are performed in accordance with the theory in order to
assess the stability and reactivity of drugs [63].
2.5.1.1 Molecular Electrostatic Potential (MEP) Map
A valuable electronic property in molecular is the MEPs, which has been studied alongside other
theories such as FMO analysis, chemical reactivity, and local descriptors. MEP surface analysis
reveals the molecular charge distribution and related properties of small-molecule drugs [64]. The
MEP map is a three-dimensional picture that can be used to understand the electronic nature of
reactive sites, such as electron-deficient (most positive) and electron-rich (most negative) regions.
Table 2.5 Summary of selected DFT functionals used in molecular modeling studies of small-molecule
drug design and discovery.
Functional Type χ Basis set
Correlation
functional Description
wB97X-D GGA
RSH
100
22
6-31+G(d,p)
def2-TZVP
Becke97 The most suitable DFT
functional for accurately
representing hydrogen
bonding interactions
B3LYP Meta GGA
Hybrid
GGA
20 def2-TZVP
6-311G*
6-311G(d,p)
6-31G*
6-31G
Lee-Yang-Parr
(LYP)
Computationally less
expensive and gives accurate
results for a vast number of
drug molecules. It is suitable
for small organic molecules
and it poorly performs with
transition meta complexes
and very large organic
molecules
CAM-B3LYP RSH 85 def2-TZVP
6-311G*
6-311G(d,p)
6-31G*
6-31G
Lee-Yang-Parr
(LYP)
When compared to B3LYP,
CAM-B3LYP does better for
transferring charge
excitations in a dipeptide
model; it also beats B3LYP
when it comes to
nucleophilic substitutions
and subsets of barrier height
LC-BLYP RSH 100 def2-TZVP
6-311G*
6-311G(d,p)
6-31G*
6-31G
Lee-Yang-Parr
(LYP)
LC-BLYP is resistant to
spurious oscillations in
molecular properties
B3PW91 Hybrid
GGA
20
6-311++G** Perdew-
Wang91 PBE
It is suitable for the systems
with uniform density
PBE0-D3 Semi-empirical
GGA
25 6-31G* PBE It is used to study flexible
drug–target complex
χ, the percentage of HF exchange functional.
Abbreviations: GGA, generalized gradient approximation; RSH, range separation hybrid.
36
The MEP domains can be shown using various colors; for instance, blue can be used to represent
the most positive regions, red to represent the most negative, and yellow or green to represent the
intermediate regions. This picture helps understand possible covalent connections upon binding
to macromolecules and other interactions such as hydrogen bonding or weak London forces [65].
2.5.1.2 The Two Main Methods Used in Population Statistics Are the Mulliken and Natural
Population Analyses
According to MEP, the type of charge on each atom in the drug molecule can be revealed by using
natural population analysis (NPA) and Mulliken population analysis (MPA). A number of possible
interactions between the drug molecule and the enzyme active site can be anticipated if we sup-
pose that the molecule contains a nucleophilic and electrophilic core.
2.5.1.3 Natural Bond Orbital (NBO) Analysis
DFT uses a chemical bond analysis technique called NBO analysis (DFT). It investigates all possible
combinations of “empty” (acceptor) non-Lewis NBOs and “full” (donor) Lewis-type NBOs. Second-
order perturbation theory is then applied to estimate the energy of these interactions. Hydrogen bonds
between molecules, intramolecular hydrogen bonds, and hyperconjugative interactions can all be
identified by NBO analysis. The donor–acceptor stabilization energy (E2) is linked to the i/j delocali-
zation between the acceptor NBO (j) and donor NBO (i). The stabilizing energy (E2) is greater, allow-
ing for a more thorough conjugation over the entire molecular system. Furthermore, the medicinal
molecule’s electronic spectra’s π–π* and n–π* transitions are validated using the results of NBO [66].
Researchers can also learn new things about the catalytic mechanism of active site enzymes
thanks to the QM/MM technique, which is important for mechanism-based, logical drug design:
For the past 20 years, QM clusters have been in use for enzyme active site reaction mechanism
modeling. This approach, which uses simple QM as opposed to QM/MM, provides high-level
chemical precision, especially at the catalytic regions of enzymes, allowing for a thorough exami-
nation of protein–ligand interactions and reaction processes. The enormous computational cost of
this approach is a drawback [67].
In summary, electron density, electronic charge, and other reactivity parameters can be studied
for any isolated drug molecule using DFT as a standalone method; DFT is also utilized to study the
type of interactions at the target site of enzyme by MM method. Finally, QM/MM method is com-
binedly used to evaluate the mechanisms of inhibition reactions by a small-molecule drug [68].
2.5.1.4 Implementations and Tools
Thoms and Fermi started the work on the DFT in the 1920s but the broad usage of DFT was estab-
lished by Hohenberg, Kohn, and Sham in the 1960s. Later, the field booms after the development
of popular B3LYP functionals in the early 1990s that the application of DFT in molecular modeling
came into reality. Some of the most widely used quantum chemical programs are listed in Table 2.6,
including Gaussian, GAMESS, Jaguar, and ORCA [69, 70].
2.6 Factors Affecting the Choice of Bioactive Molecules
in Drug Discovery
Drug discovery is a complex, multifaceted process that involves identifying, designing, and devel-
oping compounds that exhibit therapeutic properties. The selection of bioactive molecules plays a
pivotal role in this process, and several factors influence this choice. Bioactive molecules are
37
compounds capable of interacting with biological systems, often exhibiting specific biological
activity that can be harnessed for therapeutic purposes. These molecules serve as the foundation
for potential drugs, affecting target specificity, pharmacokinetics, and safety profiles. The choice of
bioactive molecules profoundly influences the success of drug discovery due to their direct impact
on efficacy, safety, and feasibility in pharmaceutical development. This factor plays a crucial role in
determining a molecule’s suitability for therapeutic development. Table 2.7 gives the summary of
common factors that typically affect the choice of bioactive molecules in drug discovery [1].
Here are some key factors (Table 2.8) and the corresponding reference articles that discuss these
aspects.
2.6.1 Target Identification and Validation
The starting point in drug discovery is the identification and validation of a specific molecular target
associated with a disease. This target could be a protein, enzyme, receptor, or nucleic acid. The choice
of bioactive molecules revolves around their ability to interact with and modulate these targets
Table 2.6 Widely used quantum chemical programs.
Quantum chemical
programs License URL
Gaussian Commercial https://gaussian.com/
Jaguar Commercial https://newsite.schrodinger.com/platform/products/jaguar/
GAMESS Academic free https://www.ameslab.gov/gamess-open-source-quantum-
chemistry-software
ORCA Academic free https://orcaforum.kofo.mpg.de/app.php/portal
Quantum Espresso Open source https://www.quantum-espresso.org/Doc/user_guide/node8.html
MOLPRO Commercial https://www.molpro.net/
Psi4 Academic free https://psicode.org/
Table 2.7 Common factors that typically affect the choice of bioactive molecules in drug discovery.
Factor Description
Target specificity The degree of selectivity of a bioactive molecule toward the intended drug target
Safety profile Evaluation of the molecule’s safety, encompassing toxicity and potential adverse
effects
Pharmacokinetics Study of how the molecule is absorbed, distributed, metabolized, and excreted
in the body
Structural
characteristics
Analysis of the molecular structure and its relevance to interactions with the
target
Bioavailability Measurement of the amount of the administered dose that reaches the intended
target site
Potency Assessment of the strength or efficacy of the molecule in inducing the desired
biological effect
38
Table 2.8 A concise overview of the factors influencing the selection of bioactive molecules
in drug discovery.
Factor Description References
Target
identification and
validation
Target identification methods, encompassing affinity-based, genetic,
computational, and chemical proteomics approaches, delineating
their pros, cons, and applications, and categorizing methodologies by
execution steps to aid strategic selection in drug discovery
Comparison of chemogenomic compound evaluations, affinity-based
chemoproteomics, activity-based protein profiling, label-free
techniques, and computational approaches, emphasizing their
components, advantages, limitations, and applications to assist
researchers in effective target identification for drug discovery
[71]
Target specificity Discusses the importance of molecules interacting selectively with
intended biological targets while minimizing off-target interactions
[72]
Bioavailability and
pharmacokinetics
Barriers (e.g., enzymatic degradation and low permeability) affecting oral
peptide and protein delivery, along with strategies (e.g., formulation
techniques and encapsulation methods) categorized by efficacy in
overcoming specific challenges, providing valuable insights for researchers
developing optimized oral delivery systems for protein therapeutics
Explores the evolution and relevance of the Rule of Five in drug
discovery, how the Rule of Five impacts protein targets and drug
binding sites, emphasizing distinctive features and providing
examples of exceptions predominantly found in natural products
(NPs) and synthetic compounds, ultimately contributing to a
comprehensive understanding of structural influences on bioactive
molecule selection in drug discovery for strategic decision-making
[72]
Chemical structure
and drug-likeness
A structured overview of essential physiochemical properties
influencing drug development; categorizing methods for screening,
diagnosis, and analysis; and offering a comprehensive guide for
scientists and students in medicinal chemistry to assess and optimize
drug-like properties throughout drug discovery and development
[73]
Safety and toxicity Models incorporate gene disruption, structural information (MOLD2),
toxicity data (TOX21), and adverse event reporting (FAERS) to predict
drug-induced liver injury (DILI). Models display various performances
in this regard. The article showcases the use of ensemble voting
methods to improve predictive capacities by highlighting accuracies
and Matthews correlation coefficients (MCCs) for different clinical
DILI subtypes. Highlighting the intricacy of predictive modeling for
DILI, this all-inclusive resource provides insights into model
performance for safety evaluation during drug development
[74]
Toxicity and side
effects
Discusses strategies to filter out problematic compounds early in the
drug discovery process to minimize toxicity and adverse effects
[74]
Cost-effectiveness,
synthetic feasibility,
and scalability
Explores economic considerations in pharmaceutical production and
emphasizes cost-effective synthesis methods. Focuses on the
importance of synthetic accessibility on a large scale for clinical
production and scalability for practical manufacturing processes
[75]
Structural diversity
and novelty
Diversity-oriented synthesis (DOS) aims to generate diverse pure
compounds for drug lead discovery, blending strategies from nature
and synthetic chemistry. It tackles limitations of relying solely on
natural sources, employing molecular descriptor analysis and a freely
available tool to assess structural diversity in synthesis
[76]
Patentability and
intellectual
property
Considerations in protecting pharmaceutical innovation [77]
39
effectively. The process of identifying and validating potential drug targets is pivotal in drug discovery,
particularly concerning the selection of bioactive molecules [71]. Several aspects affecting target
identification and validation are critical to the selection of bioactive compounds. Important decisions
regarding which bioactive compounds to pursue in further stages of drug development are made dur-
ing target identification and validation. Several strategies have been suggested for identifying targets,
including computational methods, genetic methods, phenotype-based methods, and affinity-based
methods. Thermal shift assays, drug affinity responsive target stability (DARTS), and photoaffinity
labeling (PAL) are all affinity-based methods that directly identify targets for small molecules. PAL is
one example; it employs a photoreactive moiety on a chemical probe that, when triggered by light of
a certain wavelength, forms a covalent bond with its target [78]. DARTS, on the other hand, identifies
interactions without prior modification of the drug, relying on protease digestion and subsequent
analysis of stabilized proteins. Thermal shift assays, like cellular thermal shift assay (CETSA), inves-
tigate protein stabilization upon ligand binding. These methods offer advantages in sensitivity and
specificity but may face challenges in terms of scalability and resource requirements. Additionally,
phenotype-based assays, exemplified by MorphoBase, utilize high-content image analysis to classify
drug effects based on cell morphology. Genetic approaches, particularly CRISPR-Cas9, revolutionize
drug target discovery by enabling precise genome editing, as demonstrated in the identification of
novel therapeutic targets in diseases like acute myeloid leukemia. Computational methods, involving
analyses of genomics, transcriptomics, proteomics, and metabolomics data, contribute to a compre-
hensive understanding of drug interactions. While these diverse approaches offer valuable insights
into target identification, each comes with its own set of advantages and disadvantages, emphasizing
the importance of a multifaceted approach in drug discovery [79].
2.6.2 Target Specificity
Molecules must interact selectively with the intended biological target (e.g., protein, enzyme, and
receptor) to exert the desired therapeutic effect while minimizing off-target effects. Target specific-
ity stands as a pivotal factor in the selection of bioactive molecules during the intricate process of
drug discovery. This ensures efficacy and safety. It is believed that stress ligand efficiency is a key
metric for lead selection, emphasizing the importance of molecule effectiveness in binding to
intended targets for drug specificity. Specificity in targeting biomolecules or receptors significantly
impacts a molecule’s efficacy and safety profile. Balancing potency and selectivity poses a funda-
mental challenge in drug development, highlighting the need for researchers to carefully consider
target specificity in the intricate process of selecting bioactive molecules [80].
2.6.3 Bioavailability and Pharmacokinetics
Bioavailability and pharmacokinetic properties determine the molecule’s efficacy and safety in
drug discovery. The strategies to improve bioavailability, particularly for biologics, proteins, and
peptidesare essential considerations in drug discovery. Bioavailability and pharmacokinetics are
critical factors influencing the choice of bioactive molecules in drug discovery, particularly when
considering the oral delivery of complex proteins. The introduction of proteins as therapeutic
agents brings forth advantages in terms of high bioactivity and specificity, but challenges arise in
ensuring their effective delivery through the gastrointestinal (GI) tract. The GI environment pre-
sents hurdles such as enzymatic degradation, low permeability, and weak absorption, impacting
the bioavailability of orally administered proteins and affecting clinical outcomes. Overcoming
these barriers is crucial for improving patient compliance and reducing production costs. Current
oral delivery strategies, including formulation approaches and the utilization of delivery systems,
aim to enhance the stability and absorption of proteins (Peng et al. 2023). However, translating
40
these strategies from in vitro and in vivo animal studies to human applications requires careful
consideration of safety and limitations. The shortage of clinical data poses a challenge, emphasiz-
ing the need for innovative design strategies, novel model systems, and a deeper understanding of
bioavailability and pharmacokinetics to advance oral protein drug delivery in the field of drug
discovery [81].
Selecting bioactive compounds with good absorption, distribution, metabolism, and excretion
rates is essential. The ADME pathway allows molecules to reach their target location at effective
concentrations; molecules should have appropriate characteristics for this pathway. The chemical
space is enormous, yet biologically active molecules tend to congregate in tiny areas. The number
of protein folds and pockets also limits the options for drug design. The biophysics of cavity crea-
tion in protein ligand-binding sites is in good agreement with the physicochemical characteristics
of pharmaceuticals, as stated by the Rule of Five. Experimental solubility is greatly affected by the
parameter lipophilicity, which has nothing to do with binding site biology. Lipinski [82] discusses
the evolution of the “Rule of Five” as a guideline for drug discovery, emphasizing its alignment
with structural constraints in protein targets and ligands. Ro5, derived from physicochemical pro-
files of phase II drugs, guides ligand chemistry and design philosophy, influencing drug-likeness
concerning bioavailability and pharmacokinetics. Lipinski in 2016 explores two extremes in ligand
construction: synthetically derived ligands and NP metabolites shaped by evolutionary selection.
Cyclosporine A, a chemical chameleon and an outlier of the NP Ro5 population, is a good example
of how NPs can serve outliers owing to their peculiar chemistry and the effects of natural selection.
The chameleon-like nature of NPs, caused by intramolecular hydrogen bonding, is demonstrated
by exceptions to Ro5, particularly among NPs like cyclosporine A. Navitoclax and other non-NP
Ro5 outliers were found by a team of very competent and coordinated individuals, with special
emphasis on the complex interaction between medicinal chemists and in vivo bioassay specialists.
Discovering Navitoclax – a non-NP Ro5 outlier – required a great deal of skill, resources, and
unconventional thinking. In spite of confirmed accomplishments, the cost-effectiveness of finding
oral medications that do not originate from NPs outside the Ro5 space is still up in the air. This
highlights the persistent difficulties in effectively locating such compounds during the drug devel-
opment process [83].
2.6.4 Chemical Structure and Drug-likeness
Chemical structure and drug-likeness play a crucial role in determining the success of drug develop-
ment. Drug-likeness describes the chemical and physical properties of a molecule that make it suit-
able for use as a drug. Factors like molecular weight, lipophilicity, solubility, and structural complexity
influence a molecule’s drug-like properties. It underscores the critical role of selection of drug candi-
dates in drug discovery, emphasizing the importance of compounds with favorable absorption, distri-
bution, metabolism, elimination (ADME), and toxicity profiles [84]. It navigates physiochemical
properties, offering in-depth exploration of screening, diagnosis, and modification strategies, and
serves as a valuable guide for scientists and students involved in understanding, discovering, and
developing clinically optimal candidates in drug development. In drug discovery, only a fraction of
newly invented compounds with therapeutic target binding capabilities possess sufficient ADME
properties and acceptable toxicology profiles to progress through human Phase I clinical trials. Kerns
and Di’s book provides scientists and students with the background and tools to comprehend, dis-
cover, and develop optimal clinical candidates [85]. The resource explores physiochemical proper-
ties, including solubility and permeability, and covers a wide variety of current methods for screening,
diagnosis, and in-depth analysis of drug properties. Through case studies, structure–property
41
relationship descriptions, and modification strategies, it equips scientists with tools and methods for
all aspects of drug research, discovery, design, development, and optimization [5].
2.6.5 Safety and Toxicity
Understanding a molecule’s safety profile is crucial to avoid adverse effects. Assessing potential
toxicities and side effects early in the drug discovery process helps in selecting molecules with bet-
ter safety profiles. Safety and toxicity assessments are critical in drug development to mitigate
potential side effects. The study conducted by Adeluwa et al. [74] sheds light on the challenges and
complexities in predicting DILI, a pivotal concern in regulatory drug clearance. One major issue in
regulatory clearance is the prediction of DILI, which is the subject of the study by Adeluwa
et al. [74]. In order to create prediction models for various DILI classes, the study used a wide vari-
ety of datasets, including gene expression data, structural information, toxicity profiles, and reports
of adverse events [86]. The findings indicate varying accuracies and MCCs among models based on
expression signatures, FAERS, MOLD2, and TOX21 data for predicting clinical DILI subtypes.
Integrating these data types through ensemble voting methods enhanced predictive capabilities,
achieving balanced accuracies up to 0.60 for clinically relevant DILI subtypes. The challenges in
employing traditional machine learning for optimal classification with the current dataset empha-
size the complexity of safety prediction in drug development [87]. The limitations of traditional
machine learning in handling imbalanced datasets prompt the use of resampling techniques. The
findings underscore the ongoing need for improvement in both model construction and the devel-
opment of robust predictive data to comprehensively capture the complex landscape of DILI. As
drug discovery continues to evolve, the study advocates for a broader focus on predicting biomark-
ers specific to DILI using advanced omics data, such as single-cell and metabolomics signatures, to
enhance the safety assessment of bioactive molecules. Understanding a molecule’s safety profile
remains paramount in mitigating adverse effects and advancing the overall success of drug devel-
opment efforts [88].
2.6.6 Toxicity and Side Effects
It is of utmost importance to ensure that a molecule is safe, with minimum toxicity and side effects
in medication candidates. Research by Baell and Holloway [89] explains how to identify poten-
tially harmful substances at an early stage of drug development and eliminate them or at least
reduce their impact. In biochemical high-throughput screens, it draws attention to substructural
properties that are critical for finding frequent hitters (promiscuous chemicals), which helps in
identifying and excluding pan-assay interference compound (PAINS) from screening libraries and
bioassays [90]. These compounds, despite evading detection by common filters for reactive com-
pounds, exhibit activity across various assays, raising concerns about their prevalence as starting
points in drug discovery. The accompanying table likely details specific problematic substructural
features, their prevalence in diverse assay systems, and potential toxicity and side effects. This
comprehensive guide aids researchers in stringent filtering and exclusion criteria, enhancing the
reliability of screening libraries and bioassays in drug discovery [89].
2.6.7 Cost-Effectiveness, Synthetic Feasibility, and Scalability
Cost-effectiveness, synthetic feasibility, and scalability are critical considerations in the selection
of bioactive molecules for drug discovery. The cost of synthesizing and developing bioactive
42
compounds significantly influences their practical application in drug development. The synthesis
processes must be economically feasible to enhance the viability of drug production. Moreover, the
scalability of synthesis processes is vital to ensure that bioactive molecules can be produced on a
large scale for clinical applications [91]. Factors such as the complexity of molecules, the availabil-
ity of raw materials, and the chosen synthetic routes play a pivotal role in determining both the
cost and scalability of production. These considerations underscore the importance of balancing
cost-effectiveness and scalability when exploring bioactive molecules for drug discovery. Achieving
economically viable synthesis processes is essential for advancing drug development and making
these molecules accessible for large-scale production in clinical settings [92].
2.6.8 Structural Diversity and Novelty
Structural diversity enhances the discovery of novel bioactive compounds in drug development.
Diversity-oriented synthesis aims to efficiently create a wide array of pure compounds suitable for
lead generation. Structural diversity and novelty are pivotal factors influencing the choice of bioac-
tive molecules in drug discovery, as highlighted by the focus on diversity-oriented synthesis (DOS)
discussed by Bender et al. [76]. While nature inherently produces a vast array of structurally
diverse small molecules, relying solely on NP sources for drug discovery has its limitations. The
goal of DOS is to chemically synthesize small compounds with diverse and complicated structures
as efficiently as possible so that phenotypic, high-throughput screening methods can provide
leads [93]. It compares and contrasts the methods used by synthetic chemists with those used by
nature, highlighting the significance of learning from the former. Examining molecular character-
istics for chemical classification and the subjective nature of comparing DOS approaches are also
covered in detail [94]. To address this, the article introduces freely available software for evaluating
structural diversity (www.cheminformatics.org/diversity) in combinatorial synthesis, emphasiz-
ing the need for a systematic and objective approach to enhance the selection of diverse and novel
bioactive molecules in drug discovery programs. It underscores the importance of molecular
descriptor analysis and provides a tool for assessing structural diversity in combinatorial synthesis
(www.cheminformatics.org/diversity).
2.6.9 Patentability and Intellectual Property
The considerations of patentability and intellectual property (IP) play a crucial role in determining
the selection of bioactive molecules in drug discovery. The potential for obtaining patent protec-
tion becomes a significant factor, especially when dealing with unique chemical structures or
novel applications. This aspect is pivotal in safeguarding the substantial investments made in
research and development [95]. The works of Yu (2016) underscore the importance of IP in drug
discovery, emphasizing the need for a comprehensive understanding of the interface between
competition policy and IP rights, particularly in collaborative pharmaceutical research [96]. Yu
(2016) delves into the intricate relationship between these factors and their impact on the drug
discovery process, assessing the compliance of collaboration agreements with Article 101.
Evaluation of patentability and IP rights in such collaborations is fundamental for ensuring adher-
ence to legal frameworks and optimizing the protection of innovative bioactive molecules in drug
development [96].
In summary, the intricate process of selecting bioactive molecules in drug discovery involves
careful consideration of diverse factors, encompassing target identification, pharmacokinetics,
chemical structure, safety, cost, and IP considerations. Achieving a delicate balance among these