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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5435_Библиотеки_им_академика_М_И_Перельмана
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safety, effectiveness, and possible adverse effects of the medication. This stage in-
cludes both human and animal studies, which are significant benchmarks in assessing
whether a medicine is viable for general usage. A newly discovered medicine must
undergo more research, product development, and regulatory approvals before re-
ceiving FDA clearance and being released onto the market. Post-commercialization re-
search is also conducted during this phase to improve the medication’ssafetyand
efficacy. Drug discovery takes a long time and costs a lot of money; it typically takes
10–15 years and costs more than US $1.2 billion. About 250 of the 5,000–10,000 com-
pounds that are originally screened in this thorough process go through preclinical
research. After thorough analysis, only one out of every five chosen compounds are
eventually authorized by the FDA, and only one of the five authorized compounds
proceeds to clinical trials. These figures highlight how difficult drug discovery is and
how careful and thorough the process is from the time a concept is developed to the
time it is put on the market [3]. The process of finding new drugs has been revolution-
ized by CADD, which has changed how scientists find possible therapeutic candidates.
CADD uses computational tools to quickly screen and evaluate large libraries of chem-
icals, in contrast to earlier methods that mostly depended on laborious experimental
methodologies. This method greatly reduces the lead discovery process by speeding up
the identification of promising compounds. By predicting a molecule’sinteractionswith
a target receptor or enzyme using virtual simulations, CADD simplifies the process of
designing, testing, and optimizing molecules. Due to its predictive powers, scientists
may concentrate their efforts on the most promising possibilities, which eliminate the
need for several compounds to undergo long experimental testing. This increases the
effectiveness, economy, and precision of the drug discovery process. In the end, CADD
provides a more practical and economical substitute for the drug discovery [4]. Applica-
tion of CADD in various stages of drug discovery is shown in Figure 1.2.
Disease related
genomics
– Bioinformatics
– Reverse Docking
– Protein Structure
prediction
– In silico ADMET
Prediction
– Physiologically
based
pharmacokinetic
(PBPK) simulation
– QSAR
– 3D-QSAR
– Structure-based
optimization
– Target druggability
– Tool compound
design
– Library design
– Docking scoring
– De novo design
– Pharmacophore
– Target flexibility
CADD
Target
Identification
Target
Validation
Lead
Discovery
Lead
Optimization
Preclinical
Tests
Clinical
Trials
Figure 1.2: Application of CADD in various stages of drug discovery.
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1.4 The birth of computer-aided drug design (CADD)
Preclinical and clinical research, hit design/discovery, hit-to-lead optimization, and tar-
get identification comprise the drug design and discovery process. With a success prob-
ability of only 10%, this voyage requires a significant expenditure of US$ 2.558 billion
and takes 10–17 years. Innovative methods are desperately needed to improve success
rates, cut expenses, and speed up the procedure overall, considering these difficulties.
Drug development was previously based on empirical pharmacology, which involved
evaluating natural extracts to find active chemicals [5]. Although this strategy produced
several medications, it had drawbacks, especially when the biological target was un-
clear, making it difficult to optimize the medication for tolerance, effectiveness, and
drug–drug synergy. The idea of “one target, one drug,” which concentrated on well-
known drug targets, was first presented during the 1970s trend towards rational design.
It became standard procedure to investigate novel analogues of well-known substances,
such as me-too-drugs produced by bioisosteric alterations. Medicinal chemistry knowl-
edge among researchers was crucial in directing drug development towards more tar-
geted pharmacotherapies. Target-based drug discovery emerged in the 1990s and was a
major revolution, especially when Viracept, an HIV protease inhibitor, was discovered.
Viracept was produced entirely under computer guidance. This breakthrough brought
in the era of CADD, when virtual strategies were used to lower costs and improve chan-
ces of success. With the development of computational power, CADD methods which in-
volve a variety of in silico approaches became indispensable. The primary benefit is the
logical screening of drugs using a target or ligand dataset as a guide. As a result, fewer
analogues are tested, which makes it easier for computer programs to mimic and find
potentially beneficial medications. When the 3D structure of the biological target is under-
stood, structure-based drug design, or SBDD, is used. This structure is obtained by meth-
ods such as NMR or X-ray crystallography, which direct virtual screening (VS) via ligand
similarity at the binding site. Two essential tools in SBDD campaigns are pharmacophore
modeling and molecular docking. When a target lacks a 3D structure, ligand-based drug
design(LBDD),isemployed[6].Thisapproach makes use of pharmacophore groups, li-
gand similarity analysis, or QSAR models on a dataset of ligands with known activity out-
comes. These methods aid in the productive design and discovery of new drugs.
1.4.1 Evolution of computer-aided drug design: milestones
through the decades
1.4.1.1 Inception of molecular modeling (1960s)
CADD began in the 1960s when researchers realized how much molecular modeling
could be accomplished with computers. Researchers aimed to go beyond conventional
two-dimensional (2D) descriptions of molecules and initiated investigations into the
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feasibility of generating 3D models. This fundamental step made it possible to compre-
hend the spatial arrangement of atoms and bonds inside molecular structures on a
greater level.
1.4.1.2 DENDRAL and BIOSTER systems (early 1970s)
DENDRAL and BIOSTER, two groundbreaking initiatives, were essential in determin-
ing the direction that CADD would take in the early 1970s. Dendritic algorithm, or
DENDRAL, aimed to clarify molecular structures by using algorithms to deduce molec-
ular formulas from mass spectrometry data. The biological structure elucidation by
systematic theoretical enumeration and ranking group, or BIOSTER, on the other
hand, invented systematic methods to help scientists understand complex molecular
structures. These initiatives marked an important move in molecular analysis to-
wards systematic computational methods [7].
1.4.1.3 Quantitative structure–activity relationship (QSAR) (1980s)
With the introduction of QSAR techniques, CADD underwent a revolutionary phase in
the 1980s. Researchers were able to establish quantifiable correlations between the
chemical structures of molecules and their biological activity by the incorporation of
QSAR concepts into CADD software. During this time, software tools like C log P (cal-
culations of log P) and COMPACT (computer optimization of molecular parameters by
ascending continuum transformations) were remarkable because they made major
contributions to the prediction of physicochemical parameters and bi oavailability.
These developments open the way for more accurate approaches to drugs design [8].
1.4.1.4 Molecular dynamics and docking (1990s)
The development of MD simulations in the 1990s signaled a turning point. Over time,
scientists were able to observe how molecules behaved dynamically, which shed light
on their relationships and fl exibility. The development of docking software, demon-
strated by applications such as AutoDock, also made it easier to anticipate the binding
patterns of small compounds to target proteins. This breakthrough completely changed
the field by making it feasible to identify possible drug candidates based on their bind-
ing affinities and conformations and to conduct VS.
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1.4.1.5 Integration of cheminformatics and high-throughput screening (2000s)
Moving into the early 2000s, CADD embraced the integration of cheminformatics
tools. These tools allowed for the systematic organization and analysis of chemical
data, contributing to a more comprehensive understanding of molecular properties.
Moreover, CADD software increasingly supported HTS, enabling the rapid screening
of large compound libraries to identify potential drug candidates efficiently. This era
witnessed a conv ergence of computational and experimental approaches, streamlin-
ing the drug discovery process [9].
1.4.2 Advancements in CADD methodologies
The integration of modern computational tools and procedures has enabled develop-
ments in CADD methodology to significantly change the drug discovery landscape.
These developments include a number of aspects as shown in Figure 1.3.
1.4.2.1 Molecular modeling
Molecular modeling, a versatile computational tool, is integral across scientific fields,
elucidating molecular behaviors, interactions and properties. Widely applied in drug
discovery, materials science, computational biology, and chemistry, it intricately repre-
sents molecules at the atomic scale, enabling diverse simulations predicting structures,
Figure 1.3: Classification of CADD.
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energies, and dynamics. This multidimensional approach encompasses quantum me-
chanics-based methods, employing intricate equations for precise calculations and
molecular mechanics (MM), simplifying molecular behavior using classical physics
principles. Scientists rely on molecular modeling to predict structures, analyze inter-
actions, design new molecules, understand biological processes, and virtually simulate
experimental conditions. In order to interpret atomic-level insights using a variety of
approaches, techniques include structure building, 3D database analysis, protein model-
ing, diversity analysis, ligand docking, and continuum methods. In quantum mechanics
approach involves positioning nuclei in space while the corresponding electrons are
distributed throughout the system as a continuous electronic density, computed via
solving the Schrödinger equation. For biomolecules, this method can be executed under
the Born–Oppenheimer approximation, where the Hartree–Fock self-consistent field is
commonly used to compute the electronic density and system energy. When simulating
chemical reactions is not necessary, classical mechanics was adequate to describe the
behavior of biomolecular systems. This mathematical framework, known as MM, calcu-
lates the energy of systems, including large assemblies of atoms found in molecules or
complex biochemical systems [10]. Unlike quantum mechanics, MM overlooks electrons
and computes system energy solely based on nuclear positions. Through parameterizing
the potential energy function, the electronic aspect of the system is implicitly considered.
The collective equations and parameters defining a molecule’s potential surface are re-
ferred to as the force field. MM is a useful tool for studying large molecular systems by
calculating their structures and relative energies. It simplifies the system by treating
each atom, including its nucleus and associated electrons, as a single particle, without
explicitly considering the electrons. This simplification follows the Born–Oppenheimer
approximation, which separates electronic and nuclear movements for separate consid-
eration. MM is like a ball-and-spring model, where atoms and molecules are represented
by classical forces between them. These forces are described by potential energy func-
tions that account for structural aspects like bond lengths, angles, and torsional angles.
These functions are set up with specific parameters aimed at replicating experimental
properties. In this, the total potential energy of a molecule is the sum of various energies
such as bond-stretching energy (E
str
), bond angle-bending energy (E
bend
), torsion energy
(E
tor
), and the energy resulting from interactions among unbound atoms (E
nb
). This
breakdown allows for a detailed understanding of the different energy components
within the system [11].
1.4.2.2 Molecular dynamics
The origins of MD simulation trace back to Alder and Wainwright’s early introduction,
further developed by the Karplus group in 1977, laying the groundwork for its eventual
Nobel Prize recognition in 2013 for its role in multiscale models for complex chemical
systems. Today, MD simulation stands as a critical bridge between experimental lab
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work and theoretical concepts, offering profound understanding of evolving single and
intricate systems over time through theoretical models. Within the modern scientific
landscape, MD simulation serves as a theoretical powerhouse, offering a unique avenue
to validate hypotheses and predictions through experimental means [12]. The concurrent
efforts of global scientists have propelled MD simulation algorithms into parallel func-
tionality, amplifying their computational process. Integrating MD simulation data with
wet-lab experiments amplifies its significance, providing atomic-level dynamics and ki-
netic insights unattainable through conventional experimental methods. Advancements
in computational capabilities have elevated MD simulation to an indispensable tool
across diverse research fields, leveraging fundamental principles and approximations to
unravel complex problems. MD simulations, historically centered on biomacromolecules
like proteins and nucleic acids, have recently extended their reach to cellular scales.
They enable simulations of entire cells, unravelling foundational molecular principles
underpinning life itself. This transformative capability contributes significantly to the
scientific understanding of cellular and MD, empowering researchers with unparalleled
insights into life’s fundamental mechanisms. Within the field of drug development and
pharmaceutical research, MD simulation is a powerful instrument with a wide range of
uses. Finding and confirming biologically relevant targets is the first stage in modern
drug research. Drug substances could alter these targets, which are usually proteins
such as enzymes or receptors or nucleic acid molecules like DNA or RNA, to treat dis-
eases or reduce symptoms. But proteins are dynamic, and designing ligands is difficult
because of their different conformations. The degree to which a ligand fits precisely into
aprotein’s binding site is affected by even minute conformational changes, such as side-
chain shifts in residues. Here, MD simulations are essential. MD simulations provide im-
portant insights into the various conformational states that influence ligand-binding site
interaction by examining the dynamic nature of these targets. This knowledge is crucial
for developing medicines that work well and complement the target’sdynamicbehavior.
These predictive insights it provides into molecular interactions demonstrate its key role
in CADD [13]. Scientists can obtain an in-depth understanding of the atomic-level interac-
tions between possible therapeutic molecules and biological targets because of MD simu-
lations. The dynamic behavior, stability, and binding mechanisms between medicines
and target proteins or biomolecules are all covered in detail by these simulations. By
using MD simulations, scientists can more effectively design and optimize drug candi-
dates, improving their safety, efficacy, and specificity. Moreover, MD simulations en-
hance the understanding of intricate protein dynamics, which directs the development
of medications that specifically target protein states linked to specific illnesses. Addition-
ally, this method makes VS possible, which in turn makes it possible to identify potential
therapeutic candidates quickly and effectively from enormous compound libraries. In
the end, MD simulation is an invaluable tool in accelerating the drug development pro-
cess by providing predictive insights that improve the identification and development of
possible therapeutic agents [14].
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1.4.2.3 Structure-based drug design (SBDD)
SBDD revolves around dissecting and analyzing the 3D con figuratio ns of biological
molecules. The core principle driving this approach is that a molecule’s ability to in-
teract with a specific protein and yield a desired biological impact depends on its
adeptness at engaging with a specific binding site on that protein. Molecules that
share these beneficial interactions are likely to exhibit similar biological effects.
Hence, exploring a protein’s binding site becomes a gateway to discovering fresh com-
pounds. Obtaining structural insights into the target protein, a practice dating back to
the early 1980s, is essential to SBDD. Advancements in genomics and proteomics have
widened the scope of SBDD by uncovering numerous potential drug targets. Biophysi-
cal techniques such as X-ray crystallography and NMR spectroscopy have significantly
contributed to unveiling 3D structures of both human and pathogenic proteins. For
example, databases like PDB house over 81,000 protein structures, while resources
like PDBBIND and protein ligand databases contain thousands of ligand-protein coc-
rystal structures. These resources have hastened drug discovery, expediting the devel-
opment of several clinical drugs. Rapid identification of potential binding compounds
for a biologically relevant target is pivotal in drug discovery [15]. Computational meth-
odologies streamline this process by swiftly screening extensive compound libraries
and identifying potential binders through modeling, simulation, and visualization
techniques. The foundation of SBDD is the ability to obtain the 3D structure of biologi-
cal targets, which can often be achieved via methods such as nuclear magnetic reso-
nance (NMR) spectroscopy and X-ray crystallography. When a target’s experimental
structure is unavailable, homology modeling takes over and builds a precise atomic
model of the target using the experimental structure of a related protein. SBDD uses
the structure of the biological target to design novel medications with high specificity
and affinity for the target. Medicinal chemists’ knowledge and dynamic visuals are
involved in this procedure. To suggest novel drug candidates, it also combines a num-
ber of computational techniques. Techniques used in SBDD include de novo design,
which creates new ligands within the constraints of the binding pocket, VS, which
searches large databases for ligands that fit the binding pocket of the receptor and
optimization of known ligands through evaluation of suggested analogs inside the
binding cavity. In SBDD, identifying the protein’s binding site is essential. Concave
surfaces that retain drug-sized molecules in place and have significant “hot spots” like
hydrophobic surfaces or hydrogen bonding sites that are necessary for ligand binding
are the focus of this process. Two essential components of SBDD are docking and scor-
ing. By placing the tiny molecule inside the binding site to maxi mize interactions,
docking seeks to determine the optimal chemical match between a ligand and a recep-
tor. By separating empirically observed modes from other modes and calculating
binding affinity, scoring assesses these interactions. Before and during docking stages,
representing the receptor binding site and ligand, sampling the ligand-receptor com-
plex’s configuration space, and evaluating ligand-receptor interactions during docking
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and scoring are all parts of the docking process [16]. Together, these elements make
up the core of SBDD in CADD.
1.4.2.4 Homology modeling
A key method in SBDD is homology modeling, which is necessary to acquire a target
molecule’s 3D structure, which is a requirement for effective drug development. X-ray
crystallography, NMR spectroscopy, and single-particle cryo-electron microscopy are
examples of conventional experimental techniques that provide conclusive structural
information. Nevertheless, it can be difficult to obtain the structure of a therapeutic
protein due to the complexity involved in protein production and purification, which
frequently inhibits their use. When experimental structures are lacking, in silico tech-
niques such as homology modelin g, threading, or ab initio modeling fill the gap by
simulating the target’s 3D structure. When a structurally comparable protein (>40%
sequence similarity), sometimes referred to as a template structure, is present in the
protein structure database, homology modeling stands out as the most trustworthy of
these computational techniques for predicting the 3D structure of a target protein. Uti-
lizing the idea that very identical protein sequences have comparable structural char-
acteristics, homology modeling makes use of this idea. However, threading or fold
recognition becomes the preferred method when the target sequence has the same
protein fold as known structures but no closely matched template structure in the da-
tabase. These techniques aim to find and derive structural similarities to predict the
target protein’s 3D structure, which is essential for later drug design projects [17].
1.4.2.5 Ligand-based drug design (LBDD)
LBDD is a viable alternative for SBDD in the field of CADD when structural data on a
therapeutic target is still difficult to get by. It is not necessary to know the mechanisms
of action previously for LBDD, unlike SBDD; instead, it depends on the availability of
structural data and bioactivity data for small molecules. The underlying idea of LBDD is
that molecules with identical structural characteristics typically display similar fea-
tures. The retrieval and buildup of small molecule libraries is essential to LBDD. Chemi-
cal structures are typically created, handled, and used as molecular graphs with nodes
and edges standing in for atoms and bonds, respectively. Connection tables and linear
notations are the two main ways molecular graphs are communicated. Connection ta-
bles, which come in a variety of formats such as mol2, sdf, and pdb, have extensive sec-
tions that list coordinates for each type of atom and connection. On the other hand,
alphanumeric characters in linear notations, including simplified molecular input line
entry specification (SMILES) and Wiswesser line notation, provide compact representa-
tions that are perfect for managing large libraries of tiny molecules during storage or
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transfer. LBDD encompasses a range of impactful methodologies crucial in computa-
tional drug discovery. Among these techniques, molecular similarity-based search
stands out, employing molecular fingerprints or descriptors to pinpoint compounds
sharing structural likeness with known bioactive molecules. This approach utilizes simi-
larity metrics to gauge molecular resemblance, aiding in the identification of promising
drug candidates by virtue of their structural similarity to established bioactive com-
pounds [18]. QSAR models hold significant importance within LBDD. These models es-
tablish quantitative correlations between a compound’s chemical structure and its
biological activity. By leveraging molecular descriptors, QSAR models enable the extrap-
olation of a molecule’s activity, providing insights into its potential bioactivity. Pharma-
cophore modeling represents another cornerstone in LBDD. Pharmacophores serve as
abstract representations of crucial molecular features involved in interactions with a bi-
ological target. Pharmacophore models effectively map these essential features, allowing
the identification of molecules that share similar key interaction patterns, enhancing the
quest for potential drug candidates. LBDD methodologies significantly contribute to drug
discovery by enabling the exploration and identification of potential drug candidates,
even in scenarios where explicit structural information about the target is unavailable.
These techniques leverage structural resemblances and predictive models, allowing for
the discovery of compounds with potential bioactivity despite the absence of detailed
structural insights [19].
1.4.2.6 Virtual screening (VS)
VS techniques in CADD encompass a range of computational methodologies aimed at
sifting through vast libraries of compounds to identify potential drug candidates. These
methods include similarity searching, 2D/3D fingerprints, pharmacophore mapping, ma-
chine learning for SAR modeling, and protein-ligand docking. These methods operate on
diverse principles, leveraging sophisticated algorithms to expedite the identification of
molecules with desired biological activities. One fundamental technique within VS is
similarity searching, where molecular structures or functional traits are compared
against known active compounds, allowing the discovery of molecules with analogous
bioactivities or structural features. 2D/3D fingerprints represent another pivotal facet of
VS [20]. These fingerprints serve as quantitative representations of molecular structures,
capturing crucial information about shared molecular fragments or spatial arrange-
ments. They facilitate comprehensive structural analyses, aiding in the identification of
compounds that exhibit substantial structural similarity or possess key features vital for
interaction with target biomolecules. Pharmacophore mapping, another integral method,
plays a pivotal role in elucidating the essential molecular features imperative for effec-
tive interaction with biological targets. This approach involves defining and mapping
these essential features, enabling the identification of molecules with analogous inter-
action patterns, thus guiding the design of compounds with optimized binding charac-
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teristics. Machine learning techniques, specifically employed for structure–activity
relationship (SAR) modeling, harness vast datasets of known active and inactive com-
pounds to develop predictive models. These models establish quantitative relation-
ships between a compound’s chemical structure and its biological activity, enabling the
estimation of a molecule’s potential bioactivity. Such insights are invaluable in prioritiz-
ing compounds for further experimental evaluation. Finally, protein-ligand docking
emerges as a pivotal aspect of VS. This computational technique simulates and predicts
the binding interactions between a molecule (ligand) and a target protein, forecasting
their spatial arrangement and interaction strengths within a binding site. By virtually ex-
ploring these interactions, researchers can predict and prioritize potentially active com-
pounds, thereby expediting the drug discovery process. VS speeds up the process of
identifying and prioritization of promising compounds by coordinating these advanced
computational techniques, greatly simplifying the early phases of the drug discovery pro-
cess. In helping researchers reduce large compound libraries to a feasible subset for fur-
ther experimental validation and optimization, these methods are significant tools [21].
1.4.3 Software and tools for CADD
The beginnings of CADD go back to the 1960s, when scientists began to develop methods
for molecular modeling. It was at this time that molecular structures were first visual-
ized and simulated using computers. This was a significant advancement in the applica-
tion of computational tools for understanding the complexities of molecular interactions
and set the groundwork for further uses in drug creation [22]. Computational techniques
have evolved in response to significant turning points in the history of drug develop-
ment. Fundamental ideas were first presented in the early 1900s by Emil Fischer (1894)
and Paul Ehrlich (1909), who developed the lock-and-key and receptor theories. Although
limited to 2D data and retrospective analysis, QSAR was first introduced in the 1970s.
When molecular biology, X-ray crystallography, and multidimensional NMR were com-
bined, CADD was born in the 1980s. Complex simulations were made possible by the
adoption of computer graphics and molecular modeling during this era. Key advance-
ments occurred in the 1990s, including as the mapping of the human genome, the
creation of combinatorial chemistry, the development of bioinformatics, and the intro-
duction of HTS methods. A variety of tools have been developed in the field of molecular
modeling software to address different facets of drug discovery. Multifunctional fea-
tures, dynamics, and MM were all included in general-purpose programs that made
modeling of large and tiny molecules easier. For tiny molecules, quantum mechanical
or molecular orbital calculations were introduced in quantum chemistry. Efficient
storage and access of molecular data has become dependent on software designed for
creating and maintaining databases of molecular structures. The ability of molecular
graphics software to display both large and small molecules was essential for the visu-
alization of molecular structures. Furth ermore, the requirements of small molecule
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