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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5440_Библиотеки_им_академика_М_И_Перельмана.pdf
1.1 Introduction 3
1.1.2.9 Avogadro
Avogadro is a user-friendly, open-source tool for building and analyzing molecular structures. It is
a molecular editor and visualization tool [15].
1.1.2.10 Discovery Studio
Developed by BIOVIA (formerly Accelrys), Discovery Studio is a complete set of molecular mod-
eling and simulation tools used in materials science and drug development [16].
To conduct cutting-edge molecular modeling studies, advance our understanding of molecular
systems, and hasten discoveries in areas like drug development, materials science, and structural
biology, these software tools are indispensable aids for researchers in various scientific disciplines.
The unique study objectives, available computing power, and user skills all play a role in the soft-
ware selection process.
1.1.3 Molecular Mechanics
Due to the invaluable insights of molecular mechanics into the interactions between medications and
their biological targets, typically proteins, molecular mechanics plays a significant role in the develop-
ment of new drugs. This computational method helps researchers identify and optimize prospective
medication candidates by assisting in the study of the structural and energetic elements of molecular
interactions. Here is how molecular mechanics assists in the search for new medicines [17].
1.1.3.1 Prediction of Binding Affinity
Molecular mechanics can calculate the binding affinity between a potential medication and its
intended protein target. Researchers can forecast how tightly the medicine will bind to the target
by estimating the potential energy of the drug–receptor combination. When choosing or creating
molecules with higher affinity, which increases medicinal efficacy, this information is essential.
1.1.3.2 Conformational Analysis
Molecular mechanics that aids in the investigation of various conformations of a drug molecule
may take up in the binding site of its target. This is important because proteins are dynamic struc-
tures, and knowing how a medicine interacts with different conformations might help formulators
create more potent drugs.
1.1.3.3 Virtual Screening
To quickly assess the binding of hundreds of chemicals to a target protein, high-throughput virtual
screening uses molecular mechanics. To find possible drug candidates, virtual screening makes use
of molecular mechanics simulations, which optimize molecular interactions for effective drug dis-
covery. The prioritization of compounds with favorable binding energies speeds up the identifica-
tion of prospective therapeutic candidates [18].
1.1.3.4 Lead Discovery
Molecular mechanics helps refine lead compounds during drug development. It aids in the
decision-making process for scientists when deciding whether chemical alterations will improve a
drug’s binding affinity, selectivity, and overall pharmacological qualities [19].
1.1.3.5 Mechanism of Action
Computer simulations of molecular mechanics shed light on the ways in which medicines
affect their intended protein targets. By modeling molecular interactions, molecular mechanics
4
sheds light on drug processes and the finer points of a drug’s mode of action. Researchers can
use this information to create medications with more on-target effects and precise mechanisms
of action. Absorption, distribution, metabolism, and excretion (ADME) and possible toxicity of
drug candidates can also be predicted using molecular mechanics. Evaluation of a drug’s safety
and bioavailability is aided by knowledge of how it interacts with cellular and physiological
elements [20].
In drug development, molecular mechanics is a crucial tool for logically designing and perfect-
ing therapeutic medicines. Reducing the number of prospective candidates saves time and money
for researchers while increasing the likelihood that successful medications with focused effects
and few adverse effects will be developed.
1.2 Types of Molecular Models
Molecular models are critical in clarifying complicated structures at the molecular level, assisting
both scientists and students in picturing three-dimensional (3D) atom configurations. Three
important models stand out among the numerous types: the ball-and-spoke model, the space-
filling model, and the crystal lattice model (Figure 1.1).
The ball-and-spoke model represents atoms as spheres and bonds as sticks, simplifying chemical
structures. This basic method explains connectedness and geometry. In contrast, the space-filling
model depicts atoms as spheres that occupy available space, displaying relative sizes and molecular
packing. Crystal lattice models play an important role in crystallography because they depict the
recurring atomic configurations in crystals while stressing symmetry and periodicity. These mod-
els, each with its own set of strengths, contribute to a full understanding of molecular structures
by providing different viewpoints on the microscopic world [21, 22].
Types of molecular model
(a)
(c)
(b)
Figure 1.1 Different molecular models of carbon dioxide (CO
2
). (a) Ball and stick model, (b) space-filling
model, and (c) crystal lattice model.
5
1.2.1 Ball-and-Spoke Model
In molecular modeling, ball-and-spoke models are crucial visual representations that simplify
the complex 3D structures of molecules for analysis and comprehension. This paradigm,
which depicts atoms as spheres and bonds as sticks, has found widespread applicability in
various scientific disciplines. The ball-and-spoke model, often known as the stick model, is a
molecular depiction in which atoms are represented as spheres and the bonds that connect
them as sticks. This straightforward approach visualizes molecular geometry in a practical and
simple manner, assisting researchers in comprehending the spatial arrangement of atoms
within a molecule [23].
Applications of this model:
● Structural analysis: The ball-and-spoke model is extensively used in structural elucidation
because it allows researchers to easily determine bond angles, bond lengths, and overall molecu-
lar shapes.
● Teaching tool: The model is an effective teaching tool in educational settings, reducing com-
plex chemical structures for pupils and facilitating a better understanding of key chemistry
principles.
● Communication: The model is a universal language in scientific communication, commonly
used to transmit molecular structures to varied audiences in research publications, presenta-
tions, and textbooks.
Pros of this model:
●
Simplicity: The model’s simplicity makes it approachable to both academics and students,
promoting a clear understanding of molecular geometry.
● Intuition: The visual depiction aids in the rapid understanding of molecular structures, allowing
for instant insights into atom connectivity and arrangement.
● Communication effectiveness: The ball-and-spoke paradigm promotes clarity in research
publications and presentations, making it an effective means of communication in the scientific
community.
Cons of this model:
● Oversimplification: In some circumstances, the model may oversimplify molecular activity,
ignoring nuances that more advanced models may capture.
● Electron density representation is limited: The model does not directly express information
regarding electron density or orbital features, which can be critical for a thorough understanding
of molecular properties [23, 24].
1.2.1.1 Future Directions
As molecular modeling techniques advance, researchers are exploring ways to integrate the
strengths of the ball-and-spoke model with more advanced computational methods, aiming for a
more holistic representation of molecular structures.
In conclusion, the ball-and-spoke model remains a cornerstone in molecular modeling, offering
a balance between simplicity and effectiveness. While it has its limitations, its widespread applications
and continued use underscore its significance in the scientific community’s pursuit of understand-
ing molecular complexities.
6
1.2.2 Space-filling Models
Space-filling models, often known as CPK models (after the initials of the creators Corey, Pauling,
and Koltun), describe molecules by portraying atoms as spheres with radii proportional to the van
der Waals radii of the respective elements. These models try to provide a realistic representation of
molecular structures by demonstrating how atoms pack together efficiently in 3D space [25, 26].
Applications of this model:
● Steric considerations: Space-filling models are very useful for comprehending steric hindrance and
molecule packing. They explain how atoms inhabit space and impact the orientations of one another.
● Drug design: Space-filling models in pharmaceutical research help in drug design by visualiz-
ing how compounds can interact within active areas of biological macromolecules, allowing
researchers to optimize binding interactions.
● Molecular size visualization: The models excel in conveying the relative sizes of atoms within
a molecule, allowing for a better understanding of molecular dimensions and overall structure.
Pros of this model:
● Steric considerations: Space-filling models can help you understand steric hindrance and molec-
ular packing. They describe how atoms inhabit space and influence one another’s orientations.
● Drug design: In pharmaceutical research, space-filling models aid in drug design by visualizing
how chemicals interact within active areas of biological macromolecules, allowing researchers
to optimize binding interactions.
● Visualization of molecular size: The models excel in communicating the relative sizes of
atoms within a molecule, allowing for a better comprehension of molecular dimensions and
overall structure.
Cons of this model:
● Complexity for large molecules: Space-filling models for large and complicated molecules
may appear cluttered, making it difficult to detect individual characteristics.
● Inadequate bond information: Space-filling models, unlike ball-and-spoke models, do not
directly depict bond information, instead focus on atomic configurations [27].
1.2.2.1 Future Directions
Advances in processing power and visualization techniques provide opportunities to improve
space-filling models. The incorporation of interactive and dynamic aspects may alleviate some of
the difficulties involved with seeing complicated chemical structures.
Finally, space-filling models provide a detailed and realistic description of molecular structures,
revealing important information about molecular size, steric effects, and overall packing. Despite
significant limitations, their applications in various scientific disciplines highlight their impor-
tance as indispensable tools for both molecular modelers and researchers.
1.2.3 Crystal Lattice Models
In molecular modeling, crystal lattice models describe the repeated arrangement of atoms within
a crystal structure. These representations highlight crystals’ 3D periodicity, emphasizing the regu-
lar arrangement of atoms and molecules inside the lattice. These models help researchers grasp the
relationship between microscopic arrangements and macroscopic features by offering a macro-
scopic picture of the crystalline structure [28].
7
Applications of this model:
● Material science: Crystal lattice models are important in materials science because they guide
researchers in the discovery and design of novel materials. Predicting material qualities requires
an understanding of the crystal structure.
● Drug development: Crystallography aids in the elucidation of the crystal structures of
medicinal molecules in pharmaceutical research, providing critical information for optimizing
medication formulation and increasing bioavailability.
● Electronic properties: In order to understand the electronic properties of semiconductors and
other electronic materials, crystal lattice models are required [29, 30].
Pros of this model:
● Periodic insight: Crystal lattice models depict the periodic arrangement of atoms, allowing
researchers to identify patterns and symmetries within the crystal lattice.
● Macroscopic understanding: These models bridge the gap between microscopic and macro-
scopic scales, providing a comprehensive view of the crystal structure and assisting with experi-
mental data interpretation.
● Predictive capability: Understanding the crystal lattice is critical for forecasting material prop-
erties and driving the creation of novel materials with specific functions [31].
Cons of this model:
● Oversimplification: While crystal lattice models are excellent at depicting periodicity, they may
oversimplify certain aspects of molecular activity, ignoring dynamic and nonperiodic processes.
● Noncrystalline material complexity: Because crystal lattice models are inherently intended
for crystalline materials, their application for examining noncrystalline or amorphous structures
is limited [31].
1.2.3.1 Future Directions
Crystal lattice models are becoming more useful as computational tools and experimental proce-
dures advance. Integrating real-time simulations and dynamic elements could improve the accu-
racy and application of these models, allowing them to represent a wider range of materials.
Finally, crystal lattice models continue to be vital in molecular modeling, providing a powerful
tool for understanding the periodicity and structure of crystalline materials. Their applications in
materials science, drug development, and electronics highlight their value as valuable tools for
researchers attempting to unravel the mysteries of molecular configurations in the solid state.
1.3 Computational Methods in Drug Discovery
1.3.1 What Is Drug Discovery?
By combining computational, mathematical, experimental, translational, and clinical models, pos-
sible novel medicinal entities can be identified through the process of drug development. It is the
technique of locating and analyzing compounds that have the ability to govern illness in a secure
way, with the aim of developing medications that can prolong patients’ lives. Drug development is
still an arduous, expensive, time-consuming, and ineffective process with a high dropout rate of
novel therapeutic discovery, despite advancements in biotechnology and physiological system
8
understanding. Knowing a biological target while employing creativity to find novel drugs can be
referred to as drug designing. Creating molecules that complement the molecular target they inter-
act and bind to in terms of charge and shape is the fundamental aspect of drug design. In the big
data era, drug design typically, but not always, depends on computer modeling tools and bioinfor-
matics methodologies [32].
Apart from small molecules, biopharmaceuticals, particularly therapeutic antibodies, have
emerged as a significant class of medications [33]. Significant progress has also been made in com-
putational methods for enhancing the stability, selectivity, and affinity of these protein-based treat-
ments. Preclinical research on animal and cell models as well as human clinical trials are all part
of the process of developing and discovering new drugs. Afterward, the drug must receive regula-
tory approval before being put on the market. In contemporary drug discovery, screening hits are
identified, medicinal chemistry is applied, and those hits are optimized to improve the hit’s affin-
ity, specificity, efficacy, metabolic stability, and oral bioavailability. Before conducting clinical tri-
als, a molecule that satisfies all of these criteria will be found, and the drug development process
will start [34].
An illness or clinical condition for which there are not any appropriate pharmaceuticals on the
market leads to the start of a drug discovery program, and this unmet clinical need serves as the
project’s primary source of motivation. The preliminary study, which is frequently conducted in
academic settings, produces information to support a hypothesis that a protein’s or pathway’s acti-
vation or inhibition will have a beneficial impact in the context of a medical condition. The result
of this action is the identification of a target that, in order to support a drug development endeavor,
may need additional validation before moving forward into the lead discovery phase. It is a drawn-
out, resource-intensive procedure that calls for close collaboration among various disciplines. The
pharmaceutical industry is extremely interested in optimizing the drug discovery process since
identifying and selecting appropriate drug candidates promptly can have a significant impact on
the price and profitability of new medications [35, 36].
1.3.2 Computational Platforms for Drug Discovery
In the field of drug development, computational platforms and algorithms are invaluable resources
that have revolutionized the conventional and tedious approaches. These tools utilize artificial
intelligence (AI), chemical simulations, and advanced algorithms to accelerate multiple phases of
the drug development process. Computational models aid in the identification of pharmaceuticals
with the best pharmacokinetic profiles by forecasting important pharmacological aspects includ-
ing absorption, distribution, metabolism, excretion, and toxicity. By combining distinct datasets,
these technologies not only expedite decision-making processes but also promote collaboration.
This leads to the simplification of drug development pipelines and a notable increase in the effi-
ciency of generating innovative and effective medicines. Some most important computational
tools and platforms for drug development are enumerated below. These tools aid in the analysis of
enormous volumes of biological data, the prediction of drug–target interactions, and the optimiza-
tion of drug candidates.
1.3.2.1 NCBI
The National Center for Biotechnology Information or NCBI, a division of the National Library of
Medicine (NLM) of the National Institutes of Health (NIH), which was founded in 1988, is a com-
prehensive molecular biology information repository. It offers access to many biological databases,
including PubMed, BLAST, GenBank, NCBI Gene, Genome Resources, and NCBI Bookshelf [37].
9
1.3.2.2 Chemical Databases
Comprehensive chemical data and compound databases are available through platforms such as
PubChem and ChemSpider. Both databases provide a wealth of chemical data, but PubChem is
especially useful for biological data, such as bioassays, because it is part of the larger NCBI
resources. In contrast, ChemSpider places a strong emphasis on using a community-driven meth-
odology to create an extensive library of chemical structures. ChemSpider fosters a collaborative
atmosphere by encouraging user contributions and the curation of chemical data. Community
data entry is also permitted through PubChem. Chemical information can be accessed and
contributed to by academics, researchers, and professionals working in the field of chemistry
pharmacology and allied sectors using PubChem and ChemSpider. When used in tandem, they
can offer a more thorough comprehension of chemical molecules and their biological
functions [38, 39].
1.3.2.3 PDB
Protein Data Bank or PDB acts as the hub for 3D structural data of biological macromolecules and
is a cornerstone of structural biology. The PDB is a massive collection of structures that have been
determined through experimentation. It captures the complex architecture found in nucleic acids,
proteins, and complex molecular organizations. These structures are explored by techniques such
as crystallographic X-rays, NMR spectroscopy, and cryo-EM, providing crucial insights into the
spatial arrangement of atoms within those biomolecules. Researchers can study, examine, and
understand the structural nuances of biomolecules with the help of this publicly available resource.
The PDB continues to be an essential tool in expanding our knowledge of the connections between
structure and function, making a substantial contribution to areas like drug development, molecu-
lar biology, and bioinformatics. This is made possible by its cooperative efforts and interaction with
other databases [40].
1.3.2.4 AutoDock, AutoDock Vina, DOCK, PatchDock, HADDOCK, SwissDock, Glide, Gold,
FlexX, UCSF Chimera, and DockThor
These programs help uncover possible therapeutic candidates by predicting the binding modes of
small compounds with the target proteins [41].
1.3.2.5 UniProt
The Universal Protein Resource, or UniProt, is a comprehensive and open-access resource that
offers details on the functional annotations of proteins. UniProt is a centralized platform that uni-
fies data from many protein databases. It contains references to scholarly publications as well as
data on post-translational modifications, structural details, functional annotations, and protein
sequences. It is a priceless tool for scientists studying molecular biology, computational biology,
and related subjects. It provides a standardized, carefully curated repository that makes it easier to
find and examine data about proteins. In order to guarantee that the database continues to be an
extensive and trustworthy resource for the international scientific community, the UniProt
Consortium is dedicated to continuously updating and growing it [42].
1.3.2.6 QSAR
Quantitative structure–activity relationship or QSAR uses a compound’s chemical structure to pre-
dict its biological activity. The foundation of QSAR techniques is the statistical correlation between
target drug interactions and other molecular descriptors. The QSAR approach is based on the
observation that compounds with similar structures typically exhibit comparable biological
10
activities. These models provide a mathematical explanation of how the structural characteristics
of a ligand affect the activity responses of a target that binds it. The association between various
features of tiny ligand binders and biological activity acquired through experimentation is used to
calculate QSAR. It is possible to forecast the activity of novel drug molecule analogs using QSAR
relationships [43].
1.3.2.7 GROMACS, AMBER, NAMD, PLUMED, LAMMPS, CHARMM, GROMOS, OpenMM, Orac,
XMD, YASARA, Ms2, MacroModel, and Avizo
These software packages replicate the motions of atoms and molecules over a period of time,
providing an understanding of the dynamic behavior of biological systems [44].
1.3.2.8 Desmond
Specifically created for mimicking biomolecular systems. This cutting-edge MD modeling program
Desmond, created by DE Shaw Research, has been developed for examining the shifting dynamics
of biological systems in great detail down to the atomic level. Desmond distinguishes out for its
effectiveness and flexibility, making it especially appropriate for simulating large and complicated
biomolecular systems over longer scales. Desmond facilitates the investigation of the conforma-
tion dynamics, interactions, and the thermodynamic laws of macromolecules including nucleic
acids, proteins, and lipids by means of advanced algorithms and parallel processing capabilities.
Advanced capabilities in the software, such as parallel processing and flexible integration time
steps, enable faster and more accurate simulations. Desmond has contributed significantly to the
advancement of our knowledge of biological processes, aiding in the search for new drugs, and
offering insightful information on the structural dynamics underlying various physiological and
pathological occurrences [45].
1.3.2.9 OpenBabel
An open-source chemical package named OpenBabel has been developed for molecular structure
manipulation and chemical file format conversion. OpenBabel is a flexible and effective tool in the
field of cheminformatics, created by an interconnected group of computational chemists. Its exten-
sive chemical file format compatibility enables users to transform molecular representations
between various platforms and programs with ease. Activities including ligand docking, structure-
based virtual screening, and chemical database analysis are made easier by OpenBabel. Its exten-
sive feature set includes descriptor computations, chemical fingerprinting, and structural
perception. It is an important technology in computational chemistry that improves accessibility
and interoperability among various applications and tools utilized in the field of chemical
informatics [46].
1.3.2.10 DeepChem and Cheminformatics for Python (RDKit)
A machine learning package named DeepChem has been developed especially for cheminformat-
ics and drug discovery applications. It provides an extensive collection of tools and methods for the
creation and use of models for deep learning in biology and chemistry. DeepChem makes it easier
for researchers to use machine learning for drug development projects by facilitating processes like
compound screening, molecular featurization, and property prediction. Conversely, a popular
open-source cheminformatics toolbox for Python is called RDKit (Cheminformatics for Python).
RDKit provides a comprehensive variety of cheminformatics features, including molecular struc-
ture processing, biochemical informatics, and descriptor computation. It is a useful tool for
researchers in computational biology and medicinal chemistry since it is widely used in the
11
investigation and alteration of chemical data. DeepChem and RDKit work together to provide a
powerful blend of traditional cheminformatics tools with deep learning capabilities, enabling
scientists to investigate, evaluate, and optimize chemical structures for drug development
applications [47–49].
1.3.2.11 SBML
Systems biologists can represent and exchange computational models in a uniform format by using
the Systems Biology Markup Language (SBML). Researchers can describe mathematical models
representing processes in biology, such as pathways of metabolism, signaling pathways, and gene
regulatory networks, using SBML, an open, XML-based language. The power of SBML resides in
its capacity to enable model integration and interchange between different platforms and compu-
tational tools. In the ever-evolving field of systems biology, it ensures interoperability and collabo-
ration by offering a uniform framework for model representation. SBML is now a vital tool for
modelers, allowing them to share, replicate, and expand upon the work of one another, ultimately
leading to a more thorough knowledge of complex biological systems. It has gained widespread
acceptance with encouragement from the systems biology community [50].
1.3.2.12 Virtual Screening
A widely used computational method in drug development is virtual screening, which helps find
promising drug candidates rapidly from vast compound libraries. Using an in silico study of chem-
ical databases, this method predicts and ranks the compounds that have the best chance of binding
to a target, usually a protein linked to a specific disease. The two types of virtual screening tech-
niques are structure based and ligand based. The goal of ligand-based virtual screening is to find
compounds with comparable features by using the understanding of existing ligands and their
effects on biology. Conversely, structure-based virtual screening determines the affinity of ligand
binding by evaluating how well a target’s structure complements the chemical structures of puta-
tive ligands [51].
1.3.3 Applications of Computer-Based Methods in Steps of Drug Discovery
Throughout the drug development process, computer-based techniques are essential for increas-
ing productivity, cutting expenses, and finding new therapeutic targets [52]. In order to identify
and validate pharmacological targets, various disciplines including bioinformatics databases, sys-
tems biology, pathway analysis, network modeling, and computational biology work together [53].
By describing gene and protein functions, bioinformatics databases like NCBI and UniProt offer a
plethora of genomic and proteomic data that makes it easier to explore potential targets. These
databases function as biological information archives, providing researchers access to a wide range
of molecular information such as protein structures, genetic sequences, and functional annota-
tions. By enabling scientists to examine the function of certain genes and proteins in a range of
illnesses, these databases prove to be extremely beneficial in the identification of possible thera-
peutic targets [54]. Identification of important components and possible therapeutic targets is
facilitated by systems biology approaches, which provide an understanding of the intricate inter-
connections within biological systems. Systems biology techniques use experimental data integra-
tion to generate a comprehensive picture by simulating biological system dynamics using
computational models. The identification of important biological processes and possible targets
for medication intervention is made easier by this comprehensive understanding [55]. Pathway
analysis helps researchers identify critical nodes for therapeutic intervention by examining the
12
interlinked biochemical pathways linked to a disease using tools such as PathVisio and
Cytoscape [56]. This is furthered by network analysis, which pinpoints interactions between mol-
ecules and signaling cascades to discover possible targets inside the framework of cellular net-
works [57, 58]. Targeting certain proteins is evaluated as a viable option using computational
biology methods. Drug targets are validated in a substantial way by computational methods includ-
ing molecular docking, MD modeling, and structural bioinformatics. Molecular docking facilitates
the identification and refinement of putative therapeutic candidates by forecasting the orientation
and binding affinity of small compounds to target proteins. Protein structures are analyzed using
structural bioinformatics approaches in order to determine binding sites and comprehend their
activities. Furthermore, network pharmacology methodologies employ computational techniques
to investigate the interplay between pharmaceuticals and biological networks, offering perspec-
tives on the wider consequences of focusing on particular proteins [59–61]. By combining these
methods, a thorough framework for locating and confirming potential therapeutic targets is cre-
ated, which improves the efficacy and precision of the drug development steps.
1.4 Potential Use and Application of AI in Drug Designing
In terms of drug discovery and design, AI has emerged as a game-changer, providing previously
unheard-of chances to speed up the creation of innovative therapies. Here are a few prospective AI
applications and uses in medication development.
1.4.1 Target Identification and Validation
AI is able to find prospective therapeutic targets linked to diseases by analyzing enormous biologi-
cal information, such as genomes, proteomics, and clinical records. AI is essential to drug discov-
ery because it can quickly identify and evaluate therapeutic targets from a wide range of biological
data, which speeds up the process of selecting viable candidates for additional research and devel-
opment. Machine learning algorithms forecast the possibility that a given protein or metabolic
pathway would be an effective therapeutic target. AI can validate targets by identifying their
contribution to the development of diseases through network analysis [62].
1.4.2 Drug Screening and Lead Optimization
By modeling interactions between millions of chemicals and target proteins, AI speeds up high-
throughput virtual screening. With data-driven screening, AI revolutionizes drug discovery by
quickly finding promising molecules. In drug development, it helps in lead optimization by fore-
casting and improving molecular structures for increased safety and efficacy. It forecasts pharma-
cological and binding affinities, assisting researchers in finding potential lead compounds. To
improve therapeutic efficacy and safety, molecular structures are optimized using machine learn-
ing models [62] (Figure 1.2).
1.4.3 De Novo Drug Design
AI creates brand-new compounds with desired pharmacological characteristics. By creating new
compounds, tailoring structures, and forecasting chemical interactions, AI speeds up the de novo
drug discovery process. It offers specialized treatments for particular targets and disorders,