Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5440_Библиотеки_им_академика_М_И_Перельмана.pdf
203
9.3.1.2 Ligand-based Drug Design
1) Quantitative Structure–Activity Relationship (QSAR)
i) Construction: Establishing statistical relationships between chemical structure and
biological activity.
ii) Example: Predicting the activity of new antihypertensive compounds based on the quanti-
tative relationships derived from known structure–activity data.
2) Pharmacophore Modeling
i) Construction: Identifying key spatial arrangements of functional groups essential for
biological activity.
ii) Example: Designing kinase inhibitors by aligning molecules with a pharmacophore
representing the critical features for binding to the kinase active site.
9.3.1.3 De Novo Design Strategies
1) Receptor-based De Novo Design
i) Construction: Generating entirely new molecules that interact with a specific biological target.
ii) Example: Designing novel anticancer drugs by considering the structure of a unique
cancer-specific protein.
2) Ligand-based De Novo Design
i) Construction: Designing molecules based on the properties of known active ligands.
ii) Example: Designing analogs of an existing painkiller with modifications to improve efficacy
and reduce side effects.
9.3.1.4 Artificial Intelligence (AI) and Machine Learning-based Design
1) Generative Models
i) Construction: Using AI to generate novel molecular structures.
ii) Example: Employing a deep learning model to generate potential drug candidates with
desired properties.
2) Virtual Screening
i) Binding Pattern: Predicting the likelihood of compounds binding to a target.
ii) Example: Screening a large chemical database using machine learning algorithms to iden-
tify potential inhibitors for a specific enzyme.
9.3.1.5 Hybrid Approaches
1) Combining Structure and Ligand Information
i) Construction and Binding Pattern: Integrating structural and ligand-based considerations.
ii) Example: Designing new antibiotics by combining features of known antibiotic structures
with insights from the target’s 3D structure.
2) Experimental–Computational Interactions
i) Construction and Binding Pattern: Iteratively refining designs based on computational
predictions and experimental results.
ii) Example: Adjusting the chemical structure of a designed compound after assessing its
activity in experimental assays.
9.3.2 Basic Principle of De Novo Drug Design
De novo drug design is an approach that generates new chemical entities solely from data about a
biological target, such as a receptor or its known active binders, which are ligands with strong
binding or inhibitory activity against the receptor. The description of the receptor active site or
ligand pharmacophore modeling, molecule creation (sampling), and molecule evaluation are the
9.3 De Novo Drug Design
9 Scaffold Hopping and De Novo Drug Design204
main steps in the de novo drug design process. Two main de novo drug design strategies are acces-
sible, such as design based on ligands and structure. X-ray crystallography, nuclear magnetic reso-
nance, or electron microscopy are typically used to obtain the 3D structures of receptors (Figure 9.4).
When the receptor’s structure is unknown, homology modeling can be used to find a good struc-
ture for de novo medication design. On the other hand, sequence similarity and template structure
quality are what determine how good a homology model is. When one or more active binders are
known, but no structural data for the biological target are available, the ligand-based method is
typically employed.
9.3.3 Application of De Novo Drug Design
De novo drug design has numerous applications in the field of drug discovery and development.
Some of the key applications include:
1) Target-based drug discovery
De novo drug design is often used in conjunction with information about a specific molecular
target (such as a protein implicated in disease) to design molecules that interact selectively and
with high affinity.
2) Orphan disease
For rare or orphan diseases where existing drug treatments may not be available, de novo drug
design can be a valuable approach to discovering new compounds targeting specific molecular
pathways related to the disease.
3) Drug resistance
De novo drug design can be applied to address the challenges of drug resistance. By designing
novel compounds that interact with a target differently than existing drugs, it may be possible
to overcome resistance mechanisms.
De Novo drug design
Structure-based
Atom-based Fragment-based
Ligand-based
• Receptor 3D structure is known
• Generation of interaction sites for the
ligand
• A seed atom is required
• High number of generated structures
with questionable chemical
accessibility
• A seed fragment is required
• Lower number of generated structures
with good chemical accessibility
• Receptor 3D structure is unknown
• Generation of a pseudo-receptor or
direct similarity search
Figure 9.4 De novo drug-design approach schematic illustration.
205
4) Polypharmacology
Designing compounds that interact with multiple targets can be important in addressing com-
plex diseases with multiple contributing factors. De novo drug design allows for the creation of
multitarget drugs.
5) Fragment-based drug design
De novo approaches, particularly fragment-based design, are valuable for identifying small-
molecular fragments that can be assembled to form larger, more complex compounds. This
approach is especially useful for targets with large binding sites.
9.3.4 Historical Overview of Scaffold Hoping and De Novo Drug Design
The pharmaceutical industry began in the 1880s–1930s when chemical corporations set up
research facilities to create novel medications and separate active ingredients from natural ingre-
dients, and their biological activity was examined [16]. Schneider et al. presented the idea of scaf-
fold hopping in 1999 as a method to find isofunctional molecular structures with noticeably
distinct molecular backbones. In the brief definition, the new compounds’ different core struc-
tures and similar biological activity compared to the parent compounds were highlighted as two
important aspects of scaffold hopping [17].
The concise definition highlights two essential elements of scaffold hopping: the new compounds’
comparable biological activity to those of their parent compounds and their distinct core structures.
The similarity property concept states that substances with comparable chemical structures typically
have similar physicochemical qualities and biological activities [18]. These two requirements seem to
contradict this idea. The possibility of structurally distinct molecules binding to the same target is not
ruled out by this concept. The concept of the similarity property is widely recognized because, in
order for the ligands to fit in the same pocket, they should have some structural similarities, such as a
similar shape and electropotential surface, even though they may belong to different chemotypes. The
Lennard–Jones potential’s repulsive penalty is highly sensitive to interatomic distances, so the activity
landscape is not linear. For example, adding or removing a small methyl group can cause significant
changes in biological activity [19], and similarity metrics are insufficient to capture this nonlinearity.
Furthermore, the flexibility of small molecules and proteins further complicates the relationship
between similarity and activity. Scaffold hopping is not an exception to the similarity property princi-
ple, which is still the cornerstone of contemporary drug discovery techniques like SARs.
The Latin word “De novo” means “from the beginning,” “afresh,” or “anew.” The term “De novo”
refers to the process of designing therapeutic molecules from scratch using CADD technology.
Better chemical space exploration and the discovery of novel chemical structures for medication
development are made possible by it; these structures are not available in any known chemical
database, whether they are discovered in synthetic chemical libraries or natural compounds. As a
result, the chemicals or medications that are found will be special [12, 13]. In 1991, the first de novo
drug design tool, called LEGEND, was published. As of right moment, there are more than 20 of
these tools available. Structure-based drug design approaches are used by the tools LigMerge [19],
LEGEND [20], LUDI [21, 22], SPROUT [23], BREED [24], and PRO LIGAND [25] to generate
novel compounds. LEGEND is one of these instruments that construct the molecule or medicine
atom by atom, while the other technologies employ fragment-by-fragment construction. To gener-
ate novel compounds, the ligand-based drug design technique is used by the tools NEWLEAD [26]
and PhDD [27]. Apart from these tools, the simulated annealing technique has also been applied
to tools like AMOSA [28], CONCERTS [29], SkelGen [30], and MOLig [31]. LigBuilder [32],
LEA [33], ADAPT [34], PEP [35], SYNOPSIS [36], LEA3D [37], GANDI [38], TOPAS [39], Flux [1, 40],
9.3 De Novo Drug Design
9 Scaffold Hopping and De Novo Drug Design206
FLUX [41], MEGA [42], and EvoMD [43] are a few examples of instruments. The process of find-
ing new drugs is heavily dependent on the continuous supply of fresh lead compounds that bind to
macromolecular receptor molecules that are relevant to the disease and display the appropriate
pharmacological profile. There are several approaches to identifying these leads, the majority of
which rely on high-throughput screening (HTS) campaigns that involve the experimental testing
of physically accessible molecules [44, 45]. By screening virtual molecules, computer-assisted de
novo design offers a supplementary method that significantly increases the number of compounds
that are easily accessible for HTS (usually 1–3 million compounds). In computer-aided de novo
design, intelligent algorithms must simulate the steps involved in compound manufacturing and
testing in order for the program to suggest chemically plausible, drug-like compounds. It has been
demonstrated that fragment-based virtual synthesis techniques are very useful for this task [46,
47]. De novo ligand design was introduced in 1991 as a purely structure-based method to suggest
ligands for synthesis and was later augmented by ligand-based approaches. Pharmacophores and
shape restrictions are identified by both structure-based and ligand-based approaches, which then
match them with complimentary features included in small-molecule topologies. De novo ligand
design has received a lot of interest lately when combined with biophysical fragment screening
and X-ray structural clarification. From a specialized application of de novo design, scaffold hop-
ping has developed into a rapidly growing software toolkit with a wide range of applications in the
pharmaceutical sector. While both De Novo design and scaffold hopping-based approaches are cru-
cial in drug design, they exhibit distinct differences as illustrated in Figure 9.5.
9.3.5 Methodological Approaches in De Novo Drug Design
On the basis of the information regarding the biological target or the known active target site,
ligand pharmacophore modeling, and construction of the sample, de novo drug design creates
1. Creation of entirely new molecules from scratch
1. Modication of an existing molecular scaffold
to generate novel compounds
2. Less complexity and iterative
3. Leverage known structure-activity relationships
(SAR) and reduce the risk associated with
designing entirely novel molecules
4. Employed while lead compounds are optimized
or structure-activity relationships need to be
explored within a chemical series to improve
protency, selectivity, or pharmacokinetic properties
2. Complex and innovative
3. Require advanced computational methods and
synthetic chemistry expertise
4. Useful when there no suitable lead compounds are
available or when entirely novel chemical classes for a
specic target are searched for
From the scratch
De Novo design Scafflold hopping
Novel compound
Existing scaffold
Novel compound
Figure 9.5 The basic difference between de novo drug design and scaffold hopping.
207
novel chemical compounds. Ligands are found to have good binding or inhibitory activity
against the target site [48]. In the structure-based design, the structure of the target molecule is
unknown; therefore, homology modeling is introduced. When the structure of the target molecule
is available, but here, one or more active binders are also known, then the ligand-based approach
is followed.
9.3.5.1 Structure-based De Novo Drug Design
The active site’s physical–chemical properties and molecular shape are analyzed to determine the
shape resistance and the noncovalent interactions to a specific binding of a ligand [13]. Distinction
of the receptor’s active site is described by receptor-based de novo drug design. During the interac-
tion of the ligand with an active site containing a receptor, there may be noncovalent interactions
like hydrogen bonds, electrostatic, and hydrophobic. Based on the interaction on the active site, it
will increase the structure selectivity [48] (Figure 9.6).
Several methods are used to describe the interaction site for the receptor’s active site. A few
examples are HSITE [49], LUDI and PRO_LIGAND [49, 50], and HIPPO [50], respectively, con-
sider hydrogen-bond donors and acceptors, hydrophobic interaction sites and interactivity with
covalent and metal bonds.
9.3.5.2 Ligand-based De Novo Drug Design
The quality of the pharmacophore model holds a considerable role in ligand-based de novo drug
design. For the evaluation of the pharmacophore model, a quantitative SAR model is used paral-
lelly. In the absence of 3D structure of a biological target, the known active binders give an
Molecular
modeling
Experimental
evaluation
Ligand modeling
Cl
N
N
O
O
O
O
O
1
O
O
O
O
O
P
N
N
Cl
S
10
S
P
Molecular target
Figure 9.6 Structure-based de novo drug design.
9.3 De Novo Drug Design
9 Scaffold Hopping and De Novo Drug Design208
alternative strategy for de novo design [49]. By SAR studies or screening, these data are accessible
from the literature [50]. ChEMBL is a database where we can find active binders that contain
bioactive molecules with drug-like properties [51]. When finding a crystal structure is challenging,
this method is applied to finding biological targets. For one or more active binders, a ligand phar-
macophore model is applied and used to generate novel structures [52]. Specifically, the ligand
pharmacophore model can be employed for generating a pseudo-receptor or for directly executing
similarity design [53]. There are a few tools that are used for ligand-based drug design are –
DOGS [54], TOPAS [54], and SYNOPSIS [54].
9.3.5.3 Generation of Drug-Like Molecular Fragments
For the computerized molecular design algorithms are used. These algorithms are based on the
concepts of reproduction, evaluation, mutation, crossover, and selection. This concept or algo-
rithms are applied to the already existing molecules to create novel molecules. During this opera-
tion, mutation and crossover play a pivotal role in generating new molecules [55]. On the basis of
the Kawai et al. study the atom-based method applied for the mutation purpose. This method
usually resulted in a lot of unusual structures that contained invalid hetero–hetero atom bonds [55].
This strategy aims to explore possible options that show structural similarities to the reference
molecule but introduce differences in both their scaffolds and peripheral chains. The technique
moves across a large chemical space by using a known active molecule of interest as a guide. The
fragment library should ideally be put together using a set of well-known compounds that are
connected to the reference molecule’s target. Every fragment in the collection can be used for
mutation, depending on fragmentation. Three categories – side chains, linkers, and rings – are
used to group these fragments. By employing the fragment library and crossover, new entities are
created through fragment-based mutation. Computational experiments conducted with our pro-
prietary fragment library, derived from GPCR SARfari, have validated the feasibility of our
approach to drug discovery [55].
9.3.5.4 Similarity Searching
When considering any compounds to be added to an existing library by purchasing, it is very
important as well as hard to find whether structurally similar molecules have the same biological
activity shown or not. Within chemical and pharmaceutical research, similarity searching stands
as a traditional and extensively employed method to identify compounds with specific properties
from databases. Various techniques have been devised for small-molecule similarity searching [56].
Regardless of the method employed, a similarity search typically begins with one or more known
active compounds serving as reference molecules. It fundamentally comprises three key compo-
nents: a selected molecular representation, the search algorithm, and a metric to measure the simi-
larity among these represented molecules [56].
9.3.5.5 Selection of Target Reference Structure
● Angiotensin-converting enzyme inhibitors
Krueger and colleagues and their team conducted a database search to explore the mechanism
of action associated with the keyword “ACE-Inhibitor” in the Derwent World Drug Index (ver-
sion July 2006). The software used for this search was ISIS/base version 2.5 developed by MDL
Information Systems. Lisinopril (1), a nonprodrug ACE inhibitor, was chosen as the template
structure for evolutionary de novo design, along with the smaller Captopril (2), which, to the
best of knowledge, is the only other nonprodrug ACE inhibitor available on the market [57]. In
a second de novo approach, the team utilized a set of focused reference structures with a similar
209
chemotype. A similarity search in the reference actives for Lisinopril was conducted using the
FCFP-4 descriptor and Tanimoto index. The resulting set included the four most similar sub-
stances (3–6) along with Lisinopril (1), forming a set of five “focused” reference structures
(Figure 9.7).
● Angiotensin-II receptor antagonists
To assemble a collection of active compounds as antagonists for the angiotensin-II receptor
(AT2), the team conducted a database search in the WDI using the mechanism of action key-
word “Angiotensin-2-inhibitor.” This was followed by a filtering step employing a custom drug-
like filter. The team opted for the “first-in-class” drug Losartan (7) as the reference structure for
optimization, employing a single-reference approach. Additionally, for parallel-focused optimi-
zation with five similar reference structures, four substances (8–11) from the compiled actives
were identified to be most similar to Losartan using the FCFP-4 descriptor and Tanimoto
index [57] (Figure 9.8).
● Aldose reductase inhibitors
A collection of active compounds for a second target class was found by running a WDI database
search with the mechanism of action keyword “Aldose-reductase inhibitor” (ARI). After that, this
collection was filtered using our drug-likeness standards. A single template structure, Epalrestat
(12), was chosen as the template for de novo design. Four related compounds (13–16) were found
using a similarity search (FCFP-4, Tanimoto) for Epalrestat within the active chemicals list. These
compounds were then employed as templates for focused de novo design [57] (Figure 9.9).
9.3.5.6 Similarity Analysis of De Novo-generated Compounds
A target class-specific “best-split” similarity value can be computed and used as a similarity radius
or threshold around each known active from the previously built collection to build a preliminary
screening. A de novo compound may be classified as “potentially active” according to the “similar
property principle” or the “neighborhood behavior principle” if its similarity radius is less than the
threshold determined for any of the known actives. Once identified as possible actives, compounds
can be subjected to additional testing. De novo compounds with a high probability of inactivity may
be rejected if they do not show at least the threshold degree of resemblance to any recognized
active [40].
HO
HO
OH
OH
OH
OH
HS
N
N
N
N
N
N
N
OH
1. Lisinopril
4. Enalaprilmaleate 5. Ramipril 6. Cilazapril
2. Captopril 3. Enalapril
OH
O
O
O O
O
O
O
O
O
O
O
OO
O
O
O
O
O
O
O
NH
NH
NH
NH
NH
NH
2
Figure 9.7 Reference structures for ACE inhibitors.
9.3 De Novo Drug Design
9 Scaffold Hopping and De Novo Drug Design210
9.3.5.7 Evaluation of Scaffold Diversity
The research team, along with Krueger et al., computed Murcko scaffolds for every de novo com-
pound and every member of the active set in order to evaluate the diversity of the recently formed
de novo structures. A customized MOE script (Molecular Operating Environment, version 08.2006,
Chemical Computing Group) was used to do this. Only the cyclic substructures of a molecule and
their shortest linkers are captured by Murcko scaffolds; these indicate the shortest bond path
between two rings. Two types of Murcko scaffolds were created: atom-based scaffolds that kept
information about hetero atoms in rings and linkers, and graph-based scaffolds that removed all
information about hetero items in rings and linkers. For each scaffold, both MACCS keys and
FCFP-4 fingerprints were computed and subsequently clustered based on maximum dissimilarity,
employing the “complete linkage” method [57].
O
O
HO
16. AY-31358
14. ONO-3
13. ONO-1
15. Alrestatin
12. Epalrestat
O
N
S
HO
O
HO
S
S
N
O
HO
O
N
S
S
O
HO
N
O O
O
HO
O
S
O
OH
Figure 9.9 Reference structures for Aldose reductase inhibitors.
HO
HO
O
O
O
Cl
Cl
O O
Cl
O
NH
HN
HN
N
N
N
N N
N
N
N
N
N
N
N
N
NH
HN
N
N
7. Losartan 8. Elisartan
10. Pratosartan 11. KT-3-866
9. CP161418
HN
N
N
N
O
OH
N
N
N
O
N
HN
N
N
N
Figure 9.8 Reference structures for angiotensin-II receptor antagonists.
9.4 Results and Discussion 211
9.4 Results and Discussion
9.4.1 Generation of Drug-Like Molecular Fragments
After “cleaning” the Word Drug Index, Krueger et al. and his team identified 80,499 structures. By
subjecting them to various filtering criteria (including REOS, Hann, and drug-like properties), the
selection was narrowed down to 24,301 compounds. These compounds served as the foundation
for our pool of drug-like molecules. Utilizing their implementation of RECAP within the Retroflux
software, they dissected these compounds, resulting in 10,497 unique fragments with at least one
cleaved (unvalenced) bond. These fragments were then employed as the building blocks for gener-
ating new molecules from scratch. Additionally, we gathered reference sets comprising 73 ACE
inhibitors, 80 aldose reductase inhibitors, and 59 AT2 antagonists, all possessing known activity
against their respective targets [57].
9.4.2 De Novo Design with a Single Reference Structure
● ACE inhibitors: Using Lisinopril (1) as a template structure, 259,829 distinct de novo construc-
tions were produced in the de novo design studies. Although the most promising de novo-created
construct showed a Manhattan distance of 8.8 compared to Lisinopril (Manhattan distance com-
puted using CATS2D Fingerprints), Lisinopril itself was not reconstructed during the evolution-
ary optimization process. Subsequently, a similarity search was conducted across all compounds
within the ACE actives set, resulting in the identification of 12 known actives (e.g., 17, 18) within
the de novo constructs. Some of these compounds exhibited significant dissimilarity to the refer-
ence Lisinopril, as indicated by their Tanimoto scores [57] (Figure 9.10).
● AT
2
antagonists: In the de novo design process with Losartan (7) as the reference structure, a
total of 972,495 unique virtual constructs were generated. Once again, the evolutionary algo-
rithm failed to replicate the template structure. The top-performing de novo constructs
approached Losartan with a Manhattan distance of up to 3.4 (according to CATS2D). Of these
constructs, 5792 virtual entities achieved a similarity score of ≥0.48, meeting the target class-
specific similarity threshold (calculated using FCFP-4 Tanimoto similarity) compared to any of
the compounds within the known actives set. Consequently, these constructs were categorized
as potential actives [57].
● Aldose reductase inhibitors: In the de novo design process with Epalrestat (12) as the sole
template, a total of 250,753 virtual constructs were generated. The highest-ranking de novo
NH
NH
2
NH
N
OH
18. Cilazapril17. Libenzapril
HO
HO
O
O
O
O
OO
O
N
N
Figure 9.10 Reference structures for ACE inhibitors.
9 Scaffold Hopping and De Novo Drug Design212
construct closely resembled Epalrestat, achieving a Manhattan distance of 3.7 (measured using
CATS2D descriptors). During this process, two known actives, labeled as (19, 20) in the WDI,
were successfully reconstructed. Furthermore, among the generated constructs, 1030 virtual
entities exhibited a similarity score of ≥0.59, surpassing the target class-specific similarity
threshold (determined using Tanimoto similarity with FCFP-4 fingerprints) when compared to
any compound within the actives set. As a result, these constructs were identified as potentially
active [57] (Figure 9.11).
9.4.3 De Novo Design with a Focused Set of Five Similar Templates
● ACE inhibitor: The Flux algorithm identified 50 more structures that performed well. These
structures were close to the two molecules mentioned (44 and 45) [57], suggesting that the algo-
rithm focused on a particular area of activity. Even though none of the suggested template
designs were discovered from scratch. While looking closely at the newly created structures, it is
noticed that a specific pattern often appeared in the highest-rated ones. This pattern is the same
as the one found in Enalapril, which is the most common part shared by the five template com-
pounds (1) and (3–6) [57] (Figure 9.12).
● Angiotensin-II receptor antagonists: 650,616 different virtual things were constructed. The
best de novo structure had a Manhattan distance of D = 6.5 (CATS). It was found that 3980 con-
structs with a score of ≥0.58 were kind of similar to an already known active compound [57].
However, the given templates and any known active compounds are not reconstructed.
9.4.4 De Novo Design with a Diverse Set of Five Templates
● ACE inhibitors: 333,299 unique virtual designs were generated using 5 very diverse sets of
starting templates (1, 2, 7–9) [57]. Each design was different, and the best one scored 7.8 and it
fit well (using certain measures). This score is a bit better than starting from scratch with just one
template (which scored 8.8), but not as good as using a set of similar templates (which scored
5.2) [57]. Ten known compounds were recreated. A number of 4999 de novo structures with a
score of ≥0.52 were somewhat similar to existing active compounds, but not as many as when it
started from just one template (Figure 9.13).
● Aldose reductase inhibitors: 376,455 unique de novo were constructed in this approach. Out
of these, 1915 designs were found with a similarity score of ≥0.59 [57].
20. DN-108
19. Xantoxylin-Carboxylate
Ethylester
O
OH O
O
OO
NH
S
NH
O
O
Figure 9.11 Reference structures for aldose reductase inhibitors.