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References 193
91 Goldmann, D., Pakfeifer, P., Hering, S., and Ecker, G.F. (2015). Novel scaffolds for modulation of
TRPV1 identified with pharmacophore modeling and virtual screening. Future Medicinal
Chemistry 7 (3): 243–256.
92 Medina-Franco, J.L. (2013). Advances in computational approaches for drug discovery based on
natural products. Revista Latinoamericana de Quimica 41 (2): 95–110.
93 Koutsoukas, A., Simms, B., Kirchmair, J. etal. (2011). From in silico target prediction to multi
target drug design: current databases, methods and applications. Journal of Proteomics 74 (12):
2554–2574.
94 Rognan, D. (2010). Structure-based approaches to target fishing and ligand profiling. Molecular
Informatics 29 (3): 176–187.
95 Rella, M., Rushworth, C.A., Guy, J.L. et al. (2006). Structure-based pharmacophore design and
virtual screening for novel angiotensin converting enzyme 2 inhibitors. Journal of Chemical
Information and Modeling 46 (2): 708–716.
96 Paliwal, S., Mittal, A., Sharma, M. et al. (2015). Pharmacophore and molecular docking based
identification of novel structurally diverse PDE-5 inhibitors. Medicinal Chemistry Research 24:
576–587.
97 Peach, M.L. and Nicklaus, M.C. (2009). Combining docking with pharmacophore filtering for
improved virtual screening. Journal of Cheminformatics 1 (1): 1–15.
98 Hindle, S.A., Rarey, M., Buning, C., and Lengauer, T. (2002). Flexible docking under
pharmacophore type constraints. Journal of Computer-Aided Molecular Design 16: 129–149.
99 Mobley, D.L., Liu, S., Lim, N.M. et al. (2014). Blind prediction of HIV integrase binding from the
SAMPL4 challenge. Journal of Computer-Aided Molecular Design 28: 327–345.
100 Lyne, P.D., Kenny, P.W., Cosgrove, D.A. et al. (2004). Identification of compounds with
nanomolar binding affinity for checkpoint kinase-1 using knowledge-based virtual screening.
Journal of Medicinal Chemistry 47 (8): 1962–1968.
101 Wang, M., Hou, S., Wei, Y. et al. (2021). Discovery of novel dual adenosine A1/A2A receptor
antagonists using deep learning, pharmacophore Modeling and molecular docking. PLoS
Computational Biology 17 (3): e1008821.
102 Alavijeh, M.S. and Palmer, A.M. (2004). The pivotal role of drug metabolism and
pharmacokinetics in the discovery and development of new medicines. Current Opinion in
Investigational Drugs 5: 755–763.
103 Mohan, C.G. (2019). Structural Bioinformatics: Applications in Preclinical Drug Discovery Process,
vol. 27. Springer.
104 de Groot, M.J. and Ekins, S. (2002). Pharmacophore modeling of cytochromes P450. Advanced
Drug Delivery Reviews 54 (3): 367–383.
105 Miners, J.O., Smith, P.A., McKinnon, R.A., and Sorich, M.J. (2004). Multiple pharmacophores for
the investigation of human UDP-glucuronosyltransferase isoform substrate selectivity. Molecular
Pharmacology 65 (2): 301–308.
106 Murgueitio, M.S., Rakers, C., Frank, A., and Wolber, G. (2017). Balancing inflammation:
computational design of small-molecule Toll-like receptor modulators. Trends in Pharmacological
Sciences 38 (2): 155–168.
107 Sato, T., Honma, T., and Yokoyama, S. (2010). Combining machine learning and pharmacophore
based interaction fingerprint for in silico screening. Journal of Chemical Information and
Modeling 50 (1): 170–185.
108 Barillari, C., Marcou, G., and Rognan, D. (2008). Hot-spots-guided receptor-based
pharmacophores (HS-pharm): a knowledge-based approach to identify ligand-anchoring atoms
194
in protein cavities and prioritize structure-based pharmacophores. Journal of Chemical
Information and Modeling 48 (7): 1396–1410.
109 Schuetz, D.A., Seidel, T., Garon, A. et al. (2018). GRAIL: grids of pharmacophore interaction
fields. Journal of Chemical Theory and Computation 14 (9): 4958–4970.
110 Perricone, U., Wieder, M., Seidel, T. et al. (2017). A molecular dynamics-shared pharmacophore
approach to boost early-enrichment virtual screening: a case study on peroxisome proliferator-
activated receptor α. ChemMedChem 12 (16): 1399–1407.
111 Lee, J.Y., Krieger, J.M., Li, H., and Bahar, I. (2020). Pharmmaker: pharmacophore modeling and
hit identification based on druggability simulations. Protein Science 29 (1): 76–86.
112 Opo, F.A.D.M., Rahman, M.M., Ahammad, F. et al. (2021). Structure based pharmacophore
Modeling, virtual screening, molecular docking and ADMET approaches for identification of
natural anti-cancer agents targeting XIAP protein. Scientific Reports 11 (1): 4049.
113 Sangande, F., Julianti, E., and Tjahjono, D.H. (2020). Ligand-based pharmacophore modeling,
molecular docking, and molecular dynamic studies of dual tyrosine kinase inhibitor of EGFR and
VEGFR2. International Journal of Molecular Sciences 21 (20): 7779.
114 Hassan Baig, M., Ahmad, K., Roy, S. et al. (2016). Computer aided drug design: success and
limitations. Current Pharmaceutical Design 22 (5): 572–581.
115 Hamza, A., Wei, N.-N., and Zhan, C.-G. (2012). Ligand-based virtual screening approach using a
new scoring function. Journal of Chemical Information and Modeling 52 (4): 963–974.
116 Scior, T., Bender, A., Tresadern, G. et al. (2012). Recognizing pitfalls in virtual screening: a critical
review. Journal of Chemical Information and Modeling 52 (4): 867–881.
117 Gimeno, A., Ojeda-Montes, M.J., Tomás-Hernández, S. et al. (2019). The light and dark sides of
virtual screening: what is there to know? International Journal of Molecular Sciences 20 (6): 1375.
118 Kirchmair, J., Wolber, G., Laggner, C., and Langer, T. (2006). Comparative performance
assessment of the conformational model generators omega and catalyst: a large-scale survey on
the retrieval of protein-bound ligand conformations. Journal of Chemical Information and
Modeling 46 (4): 1848–1861.
119 Drwal, M.N. and Griffith, R. (2013). Combination of ligand- and structure-based methods in
virtual screening. Drug Discovery Today: Technologies 10 (3): e395–e401.
120 Vancraenenbroeck, R., De Raeymaecker, J., Lobbestael, E. et al. (2014). In silico, in vitro and
cellular analysis with a kinome-wide inhibitor panel correlates cellular LRRK2
dephosphorylation to inhibitor activity on LRRK2. Frontiers in Molecular Neuroscience 7: 51.
195
9.1 Introduction
Drug discovery is a process where a novel lead compound is present, which binds to a specific
macromolecular receptor and exhibits pharmacological activity. The aim is to find novel lead com-
pounds with desirable pharmacological properties. To find this compound de novo design is applied
in various cases. A supportive approach is provided by computer-assisted de novo design is the
screening of virtual molecules. For this, the process of compound synthesis and testing must be
emulated by smart algorithms [1], so that software can suggest chemically feasible substances
which exhibit drug-like properties.
Computer-assisted de novo drug design has an immense advantage in the field of compound
diversity which is theoretically unlimited and innovative also [2]. By de novo drug design, we can
resolve two main problems: first is how to construct chemically and synthetically feasible drug-like
molecules (the assembly problem) and second one is how to assess the quality of the designed
compounds (the scoring problem) [1]. For the solution of the first problem, fragment-based meth-
ods are a good approach. Fragment-based drug discovery is an important method for finding
potential drugs and improving their effectiveness. With the vast possibilities in the world of poten-
tial drugs, computers help us explore small building blocks of chemicals and find good starting
points for designing effective drugs [3].
In the early stage de novo drug design was basically based on receptor-based scoring schemes.
There are two main ways to deal with the scoring problem. One way is to make better scoring func-
tions for designing based on the receptor (called “in situ” design). The other way is to not focus on
the receptor’s structure and instead rely on ligand-based scoring. In the first approach, you need a
model of the receptor’s binding cavity. In the second approach, you need at least one known refer-
ence ligand to use as a guide for designing compounds [1]. Due to significant improvement in the
area of de novo design methods, it will defiantly help in drug discovery more precisely in the
near future.
9
Scaffold Hopping and De Novo Drug Design
Shrimanti Chakraborty, Soumi Chakraborty, Biprajit Sarkar, Rahul Ghosh,
Sharanya Roy, Nisha Kumari Singh, and Gourav Rakshit
Department of Pharmaceutical Sciences and Technology, Birla Institute of Technology, Ranchi, Jharkhand, India
9 Scaffold Hopping and De Novo Drug Design196
9.2 Scaffold Hopping
Scaffold hopping (Figure 9.1) refers to a procedure focused on discovering molecular structures
with different molecular backbones while maintaining iso functionality [4]. Also known as lead
hopping, leapfrogging, chemotype switching, and scaffold searching [5]. Scaffold-hopping
approaches typically initiate with recognized active compounds and conclude by altering the central
core structure of the molecule to generate a novel chemotype [6]. At times, adjusting side chains is
enough to mitigate the undesirable properties linked to the parent molecule, while in other cases, it
becomes necessary to modify the core structure or scaffold of the parent molecule [6].
There exist various motives for pursuing a diverse array of molecular frameworks: diverse chem-
otypes provide options for chemical accessibility and opportunities for lead optimization. Having
multiple lead structures reduces the risk of drug development setbacks in the presence of undesir-
able ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties. Scaffold-
hopping can additionally be employed to transition from natural substrates to more drug-like
agents or enhance receptor-subtype selectivity [4]. This method necessitates having a template,
which is a chemical structure showcasing the desired biological activity. It operates on the assump-
tion that other compounds, structurally different from the template but retaining certain essential
features, can exhibit the same biological activity [7]. Attaining this involves modifying factors such
as mode of action, selectivity, potency, and other relevant aspects of activity, administration, avail-
ability at the target site, or other metabolic properties. This process encompasses not only bioisos-
teric replacement but also substantial changes and substitutions to the foundational structure,
aiming to introduce both novelty and the desired response [5]. From a chemist’s viewpoint, two
scaffolds may differ if they are constructed through distinct synthetic routes. Numerous cases
Shape matching
Pharmacophore
searching
Fragment
replacement
Figure 9.1 Illustration of scaffold hopping.
9.2 Scaffold Hopping 197
illustrate that minute alterations can lead to dramatic changes in molecular properties. Therefore,
a pharmacologist may differentiate between an agonist and an antagonist based on the compound’s
function, even if the compounds vary by just one minor substituent [7].
A prevalent strategy in scaffold hopping involves seeking replacements for a fragment within an
active compound rather than entire compounds. An early example of a 3D database searching
program, CAVEAT, was specifically designed to address this challenge [7]. Several other programs,
employing variations of the methodology used by CAVEAT or targeting the common objective of
substituting a specified scaffold in a 3D structure, include Brood, sparkV10, Core Hopping, Scaffold
Replacement, SHOP, ParaFrag, and ReCore. These programs take as input a 3D structure of an
active compound or a scaffold portion, with designated bonds marked for use as attachment vec-
tors linking the scaffold to substituents (R-groups). All these programs conduct searches within
predefined fragment databases and provide matching 3D structures that can replace the original
scaffold. The proposed new scaffolds are then organized based on criteria such as size, predicted
physicochemical properties, pharmacophoric features, common structural framework, or other
attributes that assist users in reviewing the results and evaluating the potential of the suggested
structural replacements [8].
9.2.1 Classification of Scaffold Hopping
Boehm et al. established a classification criterion for distinguishing two scaffolds: they deemed
them different if synthesized through distinct synthetic routes, irrespective of the magnitude of
the alteration. This assertion has found support in numerous instances where closely related
chemical structures, exhibiting sufficient disparity to warrant separate patent claims or US Food
and Drug Administration (US FDA) approvals for new drug applications, coexist. A notable illus-
tration involves the phosphodiesterase enzyme type 5 (PDE5) inhibitors sildenafil and vardenafil,
where a mere exchange of a carbon atom and a nitrogen atom in the 5–6 fused ring constitutes the
primary structural difference, yet it suffices for distinct patent coverage. Similarly, the cyclooxyge-
nase 2 (COX-2) inhibitors Rofecoxib (Vioxx™) and valdecoxib (Bextra™) exhibit minimal dissimi-
larity, limited to the five-member hetero rings connecting the two phenyl rings, but were marketed
separately by Merck and Pharmacia/Pfizer. The rationale behind the concept of scaffold hopping
hinges on evaluating the extent of change relative to the original parent molecule [6].
9.2.1.1 1° Hop: Heterocycle Replacement
Minor alterations, such as substituting or interchanging carbon and heteroatoms within a backbone
ring, fall under the classification of a 1° hop. Heterocycles, serving as the cores of drug molecules,
typically present multiple vectors projecting in various directions. Introducing novel scaffolds can
be achieved by replacing carbon (C), nitrogen (N), oxygen (O), and sulfur (S) atoms in a heterocy-
cle while preserving the outward vectors. Enhanced binding affinity is often attainable when the
heterocycle directly engages in interactions with the target protein [6].
Illustratively, nonsteroidal anti-inflammatory drugs (NSAIDs) operate by inhibiting the enzyme
cyclooxygenase (COX), which is responsible for catalyzing the biosynthesis of prostaglandins
(PGs) from arachidonic acid (AA). In humans, two enzymes, COX-1 and COX-2, catalyze the initial
step in PG biosynthesis. Despite sharing the same catalytic activity, COX-1 and COX-2 differ in
sequence (60% identity), tissue distribution, and physiological function. COX-1 plays a role in gas-
troprotection and vascular homeostasis, while COX-2 is primarily associated with inflammatory
processes. Selectively inhibiting the COX-2 isozyme can mitigate the ulcerogenic effects linked to
traditional NSAIDs like aspirin and ibuprofen. Despite a 60% sequence homology between COX-1
9 Scaffold Hopping and De Novo Drug Design198
and COX-2, their protein backbones, particularly the ligand-binding sites, closely resemble each
other. Nevertheless, subtle structural distinctions at the ligand-binding sites prove sufficient to
generate COX-2 selective inhibitors [6].
9.2.1.2 2° Hop: Ring Opening and Closure: Pseudo Ring Structures
Manipulating a molecule’s flexibility can be achieved through ring opening and closure, exerting
control over the total number of free rotatable bonds. Intramolecular hydrogen bonds (HBs) often
guide where to initiate ring closure, as exemplified in the following instances. The GlaxoSmithKline
group, intrigued by the potential intramolecular HB between the o-alkoxy group and biaryl NH,
synthesized a series of indole compounds as PG EP1 receptor antagonists. The strategic design of
ring closure effectively constrained the molecule into a bioactive conformation. One of the result-
ing indole compounds, featuring iso-butyl as R, exhibited noteworthy activities in binding and
functional EP1 antagonist assays at low nM and sub-nM levels.
While ring closure positively impacts binding free energy, it can potentially have adverse effects
on solubility and other ADME properties. To mitigate these effects and enhance drug-likeness,
medicinal chemists may opt for ring opening. An illustrative example is the pyridopyrimidinone
moiety found in protein kinase inhibitors. PD166285, a broad-spectrum tyrosine kinase inhibitor
with a 6-aryl substituted pyridopyrimidinone structure, served as a template for designing novel
tyrosine kinase inhibitors. Furet et al. performed ring opening by relocating the nitrogen atom in
position 1 of the pyrimidine ring to position 5, forming a pseudo-six-member ring with the adja-
cent urea through intramolecular hydrogen bonding. Ab initio calculations and data mining con-
firmed the favorable pseudo cyclic conformation, supported by assay results showing submicromolar
inhibition against several tyrosine kinases, including c-Src, EGFR, and c-Abl, for the pyrimidinyl
urea compound [6].
9.2.1.3 3° Hop: Pseudopeptides and Peptidomimetics
The strategic design of small molecules to emulate the structural attributes of peptides, with active
peptide conformations serving as templates, has yielded promising outcomes for challenging tar-
gets. This approach has been expansively employed in targets associated with protein–protein
interactions, aiming to create small molecules that mimic the interacting components of proteins.
In peptide-based drug discovery, the primary objective is to diminish peptide characteristics to
bolster resistance against proteolysis, all the while preserving essential chemical features for
molecular recognition. A common method employed for the transition from peptides to small
molecules is scaffold hopping. Frequently observed secondary structures like α-helices, β-sheets,
and β/γ-turns at peptide-protein and protein–protein interfaces have prompted the design of syn-
thetic structures to replicate these secondary structures.
Tools such as Recore and CAVEAT prove invaluable in devising suitable scaffolds to substitute
specific segments of peptides. Additionally, pharmacophore modeling packages offered by
Chemical Computing Group, Accelrys, and Schrodinger find widespread application in the design
of peptidomimetics [6].
9.2.1.4 4° Hop: Topology/Shape-Based Scaffold Hopping
Instances of successful topology/shape-based scaffold hopping are infrequently documented in the
literature. One potential explanation is that numerous attempts have been made, but the majority
ended in failure and remained unpublished. Another scenario arises when the new chemotype
significantly diverges from its template; scientists may categorize the process as virtual screening
(VS) rather than scaffold hopping.
9.2 Scaffold Hopping 199
The Cambridge Structural Database (CSD) extends beyond being a mere repository of
small-molecule crystal structures; it boasts a robust search engine and tools for query construction
and structure mapping. The CSD package serves as an effective topology hopping tool, empower-
ing users to specify diverse topological requirements, encompassing dihedral angle, point-to-plane
distance, and plane-to-plane angle. Additionally, ROCS stands out as a potent topology-based
scaffold-hopping tool. The grid-based method SHOP has also been employed to identify novel
scaffolds featuring significantly distinct chemotypes from their source queries [6].
9.2.2 Advantages of Scaffold Hopping
● Diversity in chemical space: Scaffold hopping allows for the exploration of diverse chemical
structures, potentially leading to the discovery of novel compounds with unique biological activities.
● Improved selectivity: Modifying the scaffold can enhance the selectivity of a compound for a
specific biological target, reducing off-target effects and improving the overall safety profile.
● Overcoming limitations: If a scaffold has limitations such as poor pharmacokinetics or toxic-
ity issues, scaffold hopping can be used to address these challenges and improve the overall
drug-like properties.
● Structure–activity relationship (SAR) exploration: Scaffold hopping facilitates the explora-
tion of SAR, providing insights into how specific structural changes influence the biological
activity of a compound.
● Intellectual property opportunities: Developing new scaffolds may offer opportunities for
obtaining new patents, providing a competitive advantage and exclusivity in the market.
● Lead optimization: When a lead compound shows promising activity but has undesirable
properties, scaffold hopping can be employed to optimize the compound for better drug-like
characteristics.
● Avoidance of patent issues: Scaffold hopping can help navigate patent landscapes, allowing
researchers to design compounds with unique structures that do not infringe on existing intel-
lectual property.
● Leveraging previous knowledge: If there is existing knowledge about the biological activity
of a certain scaffold, scaffold hopping can leverage this information to design new compounds
with similar or improved properties.
9.2.3 Disadvantages of Scaffold Hopping
● Loss of activity: Modifying the scaffold may lead to a loss of biological activity, and there is a
risk that the newly designed compounds may not exhibit the desired therapeutic effects.
● Unforeseen toxicity: Introducing structural changes through scaffold hopping may result in
unexpected toxicity issues, and the safety profile of the new compounds must be thoroughly
evaluated.
● Synthetic challenges: Some scaffold modifications may pose synthetic challenges, making the
production of new compounds difficult or impractical.
● Limited predictability: The success of scaffold hopping is not always predictable, and the relation-
ship between structural changes and biological activity can be complex and difficult to anticipate.
● Time and resource intensive: Designing, synthesizing, and evaluating new compounds
through scaffold hopping can be time consuming and resource-intensive.
● Biological relevance: The newly designed scaffolds may not always be biologically relevant or
may not interact favorably with the target, leading to a lack of efficacy.
9 Scaffold Hopping and De Novo Drug Design200
9.2.4 Reasons for Scaffold Hopping
In recent days, the bridge between scaffolds, compounds, and biological activities has been looked
into everywhere. The development of new scaffolds and their guiding search calculations are very
useful in representing new potent compounds. For various reasons, scaffold hopping is applied in
medicinal chemistry as for example, one might be interested in circumventing an intellectual prop-
erty position by identifying novel chemical entities having a desired activity, replacing a chemically
complex natural product with a synthetically accessible molecule, or improving pharmacological
properties of known actives [9]. Some substances can be hard to absorb because of certain qualities
they have. For example, if a molecule does not dissolve easily or if it mostly exists in a charged form
like strong acids or bases, it might not be absorbed well because it cannot easily pass through cer-
tain types of membranes [10]. It is mostly used in the context of virtual or computational screen-
ing. The replacement of scaffold from a chemical perspective is by ingenious design or systematic
chemical modifications of core structures representing compound series [9].
In systemic scaffold hopping applications, computational approaches are important [9]. By this
method we can generalize the structures of known reference molecules and separate from them
to identify compounds having different scaffolds. This method explores the idea of molecular
similarity and dissimilarity in various ways. On the other hand, docking can also be utilized for
searching of novel active compounds. There is another reason for the necessity of scaffold hop-
ping is the metabolic transformation of the molecule. As for example, amide bonds are often
hydrolyzed rapidly by proteases, but peptides often occur as natural ligands of many targets. In
order to derive a lead structure from such a peptide, the scaffold often needs to be replaced by a
less peptidic one [10].
For the prediction of the affinity between targets and small molecules is another interest for
choosing scaffold hopping. In simple terms, when we do not know what a substance does, we can
predict its effects by looking at other substances that are similar to it chemically and already have
known effects [10]. Target profile prediction and prediction of new ligands for individual targets at
the early discovery stage.
9.2.5 Properties and Key Methods of Scaffold Hopping
● Pharmacophore searching: The concept of pharmacophore searching has been preferred for
a long time. A pharmacophore is a three-dimensional (3D) arrangement of molecular features or
chemical groups that are essential for a compound to interact with a biological target and exhibit
a specific biological activity. If the pharmacophores are transferred from the reference to the test
molecule, then the scaffolds will also be replaced [9]. Mostly, a pharmacophore query molecule
has a limited number of interaction properties like H-bond donors and acceptors, and lipophilic
or aromatic groups, which have a particular position and orientation in space [10]. There are
some specific examples: Catalyst and Unity.
● Fragment replacement: In the context of drug design, the fragment replacement technique is
equivalent to bioisosteric replacement, which defines a pair of compounds that differ only by a
single-site substructure change [8]. CAVEAT is a groundbreaking software that is mostly used in
fragment replacement. This program looks through specific databases for pieces and finds 3D
shapes that can be swapped with the original structure. The new suggestions are sorted based on
their size, expected properties, key features, shared structure, or other factors. This helps users
see and understand the potential of the suggested changes [8]. This method can be performed on
2D or 3D structures and has a high success rate [7].
2019.3 De Novo Drug Design
●
Similarity searching: When the information regarding 3D structure is not available, this
similarity searching is applied. Earlier, this is performed by using one or multiple query structures
and searching against a database of existing (or virtual) molecules [11]. These methods have a
downside. To search for a molecule in the database, it must already be clearly listed. This is usually
fine when looking through small compound collections or corporate libraries, which have around
10
5
–10
8
molecules. However, with large combinatorial libraries, like the virtual FE library men-
tioned earlier, there can be many more molecules, making the search more challenging [11].
9.3 De Novo Drug Design
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 computer-aided drug
design (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 natu-
ral compounds. As a result, the chemicals or medications that are found will be special [12, 13]. To
create an ideal solution, de novo drug design necessitates the simultaneous and independent
optimization of multiple goals. This renders the evolutionary algorithm – a soft computing
methodology – a problem-independent search technique that may be used for a variety of optimi-
zation problems, ranging from robot behavior to medication discovery and design [14].
De novo drug design involves building pharmacological compounds from scratch using either
atoms or molecular fragments [13, 15]. There is a vast amount of search space and many novel
chemical structures could be found. Nevertheless, the atom-based approach produces some com-
pounds that are not synthesizable in addition to taking longer to create these unique structures
(Figure 9.2).
Metabolic
stability
Bio-
availability
Solubility
Fragments
Safety
Permeability
Selectivity
Efficacy
Affinity
De novo
drug design
CH
2
CH
OH
H
H
H
H
O
O
N C C
+
HO
COO
–
NH
3
CN1C(=O)N(C)c2ncn(C)c2C1 = O
Smiles
Structure
3D geometry
pKi = 5.61
plC
50
= 5.14
logP = –0.07
R
T
= 9.8s
.............
Activity
Figure 9.2 Basic principle of de novo drug design.
9 Scaffold Hopping and De Novo Drug Design202
Assembling fragments is the favored drug synthesis strategy in many de novo drug design tools,
known as the fragment-based method. Better ADME (absorption, distribution, metabolism, and
excretion) qualities and significant diversity are found in the solutions that are found, and the
molecular search space is relatively smaller. The goal of de novo drug design is to create novel mol-
ecules with the desired biological activity while matching the biological target’s binding pattern.
There are two distinct de novo design approaches based on the binding pattern: ligand-based and
structure-based approaches [13, 15].
With structure-based de novo drug design, novel ligands are created without previous knowledge
of other ligands by utilizing data from the 3D structure of a protein target. Constructing the mol-
ecule directly in the target protein’s binding site and measuring the molecule’s interaction energy
with the protein are popular structure-based design techniques. Conversely, in ligand-based
de novo design, the structure of the protein target is unknown and the new molecule is built based
on its resemblance to an existing ligand molecule.
9.3.1 Classification of De Novo Drug Design
De novo drug design is a computational approach used in drug discovery to design novel molecules with
desired pharmacological properties. The classification of de novo drug design can be broadly catego-
rized into two main approaches. The classification has also been presented pictorially in Figure 9.3.
9.3.1.1 Structure-based Drug Design
1) Rational Drug Design
i) Construction: Designing molecules based on the 3D structure of the target.
ii) Example: Designing HIV protease inhibitors by understanding the structure of the viral
protease and creating molecules that fit its active site.
2) Fragment-based Drug Design
i) Construction: Identifying small, bioactive fragments and building them into larger
molecules.
ii) Example: Developing antibiotics by assembling fragments that individually interact with
essential bacterial enzymes.
De Novo drug design
Structure-based
drug design
Ligand-based
drug design
Rational
drug design
Fragment-based
drug design
Pharmacophore
modeling
Receptor-based
de novo design
Ligand-based
de novo design
Virtual
screening
Generative
models
Combining
structure and
ligand
information
Experimental-
computational
interactions
QSAR
De Novo design
strategies
AI and ML-based
design
Hybrid
approaches
Figure 9.3 Pictorial representation of overall classification of de novo drug design.