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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5728_Библиотеки_им_академика_М_И_Перельмана
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CADD in the 1990s: The first high-resolution protein structure of bacteriorhodopsin
using Cryoelectron microscopy was intr oduced in 1990 by Henderson and his group
[30]. In 1994, another variant of CoMFA, that is, comparative molecular similarity
analysis indices (CoMSIA) was given by Klebe et al. [31]. This decade also marked the
emergence of HIV protease inhibitors such as saquinavir, indinavir, ritonavir, etc.,
which were all developed by employing CADD approaches. HIV protease inhibitors,
developed through CADD, are considered a major breakthrough in the treatment
of HIV.
CADD in the 2000s: As the twenty-first century began, CADD has seen lots of develop-
ment in all of its approaches. The distinguishing development in the first decade of
the twenty-first century was the development of many databases, which proved piv-
otal in CADD. Major databases developed in this era are given in Tab. 1.1. These data-
bases have laid a strong foundation for both ligand-based and structure-based virtual
screening methods, which is another highlight of this decade.
CADD in the 2010s and the current trend: This decade marks its presence in the
field of CADD by bagging the Nobel Prize in Chemistry in the year 2013. It was
awarded to Martin Karplus, Michael Levitt, and Arieh Warshel for their work in
computational chemistry [38]. Their work has brought a revolution in the field of mo-
lecular dynamics in SBDD. With the evolvement of high-performance computing sys-
tems, the focus has shifted toward AI-based drug discovery, which can be very
efficient and can prove highly time- and cost-saving.
Tab. 1.1: Major databases developed in the first decade of twenty-first century that played a crucial role in
CADD.
S. no. Database Year Purpose of the database
. ZINC Free database of commercially available compounds for virtual screening []
. PubChem Database of small molecules, with their biological activities []
. DrugBank Database containing the structure of drugs along with their pharmacological
and pharmaceutical properties []
. ChemSpider Free chemical structure database providing fast text and structure search []
. ChEMBL A manually curated database of bioactive molecules with drug-like properties
[]
. SIDER Database of reported adverse drug reactions of marketed drugs []
1.4 Historical perspective of CADD 9
1.5 Applications of CADD in the drug discovery process
The CADD approach is applied in the drug discovery process to reduce the time and
cost of the drug discovery process, besides enhancing accuracy. Numerous CADD ap-
proaches find their applications in the process of drug discovery and are discussed
below:
1.5.1 Drawing molecular structures and energy minimization
This is the most basic and initial step of any CADD approach. Drawing of molecular
structures and their energy minimization is a prerequisite for all the CADD techniques,
whether it is molecular docking, 2D-QSAR, 3D-QSAR, molecular dynamics, Pharmaco-
phore based virtual screening, or any other technique. The most common software
available till now for serving the purpose is given in Tab. 1.2.
1.5.2 Performing molecular docking for studying the ligand–protein interaction
This is the most widely used technique of CADD to date. Molecular docking has come
a long way since starting the first docking program “DOCK” in 1982. Molecular docking
is a technique that comes under SBDD approach, which works on the lock-and-key
model. In molecular docking, the ligand to be investigated acts as a key and the active
cavity of the receptor acts as a lock. When a ligand is placed inside the cavity of the
receptor, the Gibb’s free energy of this ligand–receptor system is calculated (ex-
Tab. 1.2: Name of the popular molecular modeling software available for drawing and energy
minimization of the chemical structures.
S. no. Name of the molecular
modeling software
Available at
ACD/ChemSketch https://www.acdlabs.com/resources/free-chemistry-soft
ware-apps/chemsketch-freeware/
ChemDraw https://revvitysignals.com/products/research/chemdraw
MarvinSketch https://download.chemaxon.com/marvin
Avogadro https://avogadro.cc/
ChemDoodle https://www.chemdoodle.com/
Spartan Software https://store.wavefun.com/Spartan_Software_s/.htm
Biovia Draw https://www.ds.com/products/biovia/draw
10 Chapter 1 Introduction to computer-aided drug design (CADD)
pressed as dock score). This dock score of the ligand is than compared with the dock
score of the standard drug. Currently, there are numerous software available for
docking, and are included in Tab. 1.3.
1.5.3 2D-QSAR modeling for the development of leads
The journey of modern 2D-QSAR started in the early 1960s and has now become an indis-
pensable part of the drug discovery process. In this technique, initially, a set of ligands
whose biological activities are reported in the literature are selected. The biological activ-
ity of these ligands is then related to their 2D structural properties (Descriptors) in the
form of a mathematical equation, also known as the 2D-QSAR model. This mathematical
equation is then used for the calculation of the biological activity of the proposed new
ligands. The list of the 2D-QSAR software available is given in Tab. 1.4.
Tab. 1.3: Some common molecular docking programs available in CADD.
S. no. Docking
program
Availability Algorithm
AutoDock Free academic version Based on Lamarkian genetic algorithm
Gold Commercial Based on genetic algorithm
GLIDE Commercial Systematic search and descriptor matching algorithm
UCSF DOCK Free academic version Based on geometric algorithm
FlexX Commercial Incremental construction algorithm
MDOCK Free academic version Based on sphere–ligand matching algorithm
Surflex-Dock Commercial Based on molecular similarity algorithm
HDOCK Free online server Based on ab initio and template-based modeling algorithm
Tab. 1.4: Available software for the development of the 2–D QSAR model.
S. no. Program
name
Availability Available at
DTC-QSAR Free open
source
http://teqip.jdvu.ac.in/QSAR_Tools/
QSAR-Co Free open
source
https://sites.google.com/view/qsar-co
QSARINS Free academic https://dunant.dista.uninsubria.it/qsar/?page_id=
QSARpro Commercial https://www.vlifesciences.com/products/QSARPro/Product_
QSARpro.php
AutoQSAR Commercial https://www.schrodinger.com/products/autoqsar
1.5 Applications of CADD in the drug discovery process 11
1.5.4 3D-QSAR modeling
3D-QSAR is considered a natural extension of the traditional 2D-QSAR studies. In this
approach, the 3D properties of the molecules are correlated with the biological activ-
ity for the generation of the model. CoMFA and CoMSIA are the two most exploited
techniques based on the ligand-based 3D-QSAR.
1.5.5 Homology modeling
Homology modeling is the approach of obtaining a 3D structure of the target protein re-
ceptor, which is not available in the protein data bank of the Research Collaboratory for
Structural Bioinformatics (RCSB). The 3D structure generated through homology modeling
can be further employed, either in molecular docking or in molecular dynamics. This is
based on the principle that similar proteins fold in the same manner; hence, there will be
similarity in their 3D structure. Modeller, Swiss-model, and I-Tasser are among the most
common software available for performing homology modeling currently.
1.5.6 Pharmacophore modeling and virtual screening
Credit for giving the concept of pharmacophore modeli ng is given to Paul Ehrlich;
however, the term Pharmacophore was given by Lemont Kier in 1971. The Interna-
tional Union of Pure and Applied Chemistry (IUPAC) defines pharmacoph ore as “an
ensemble of steric and electronic features that is necessary to ensure the optimal su-
pramolecular interactions with a specific biological target and to trigger (or block) its
biological response.” Pharmacophore modeling is employed in both the SBDD and
LBDD, in which a pharmacophore model is generated and then a database of com-
pounds is screened against the model to identify the probable hits against the same.
LigandScout, PharmaGist, Pharmer, Catalyst, and LiSiCA are the popular software for
pharmacophore modeling and virtual screening that are currently available.
1.5.7 Molecular dynamics simulation
Molecular dynamics has gained immense popularity in the twenty-first century. All
calculations in molecular dynamics are based on Newton’s laws of motion. The main
purpose of molecular dynamics is to understand the real-time behavior of a biological
system, especially of ligand–protein, when put in biological environmental conditions.
GROMACS and NAMD are among the freely available software for performing dynam-
ics simulations, whereas Schrodinger’s Desmond module is available commercially
for the same purpose.
12 Chapter 1 Introduction to computer-aided drug design (CADD)
1.5.8 Prediction of absorption, distribution, metabolism, excretion, and toxicity
(ADMET) parameters
Having good biological activity alone is not sufficient for any hit/lead for becoming a
probable drug candidate. ADMET parameters are also of utmost importance to any
drug for successfully exerting its desired pharmacological response. Therefore the in
silico approaches for predicting the ADMET parameters of any designed molecule
have become indispensable in the drug discovery process. SWISS-ADME, ADMET-
predictor, admetSAR, cypreact, druLiTo, etc. are some of the reliable software cur-
rently available for the prediction of ADMET parameters.
The use of any approach is vague if it does not have any success story behind it. In
the same context, CADD has also given us many therapeutically active drug molecules
that are used clinically today. Table 1.5 provides the list of the successful drug candi-
dates obtained for clinical applications by employing various CADD-based approaches.
Tab. 1.5: Some of the successful stories of CADD.
S. no. Class of drugs Major approaches
used in discovery
Examples
Anti-HIV (protease
inhibitors)
SBDD approach Squinavir (, Roche), Indinavir (, Merck),
Ritonavir (, Abbott), Lopinavir (, Abbott)
LBDD approach Nelfinavir (, Agouron Pharmaceuticals, Inc.)
Anti-influenza SBDD approach Zanamivir (, Glaxo Smith Kline), Oselatamivir
(, Gilead Sciences)
Carbonic anhydrase
inhibitors
Fragment-based and
SBDD approach
Dorzolamide (, Merck & Co.)
Antihypertensive
(ACE inhibitors)
Both SBDD and LBDD
approaches
Captopril (, Squibb and Sons
Pharmaceuticals)
Anti-HIV (integrase
inhibitors)
SBDD approach
(molecular docking and
dynamics)
Raltegravir (, Merck & Co.)
Fibrinogen
antagonist
LBDD approach Tirofiban (, Merck & Co.)
Antihypertensive
(renin inhibitors)
SBDD approach Aliskiren (, Novartis and Speedel)
Anti-hepatitis C virus SBDD approach Boceprevir (, Schering-Plough)
1.5 Applications of CADD in the drug discovery process 13
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Tab. 1.5 (continued)
S. no. Class of drugs Major approaches
used in discovery
Examples
Anti-cancer drugs
(kinase inhibitors)
SBDD approach Imatinib (, Novartis), Gefitinib (,
AstraZeneca), Erlotinib (, Genentech, Inc.),
Acalabrutinib (, AstraZeneca), Lorlatinib
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approach
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Antihypertensive
(AT receptor
antagonist)
SBDD approach Eprosartan (, SmithKline Beecham)
LBDD approach Losartan (, Merck & Co.), Valsartan (,
Novartis)
Antibiotic (DNA
gyrase inhibitor)
Both SBDD and LBDD
approaches
Norfloxacin (, Merck & Co.)
14 Chapter 1 Introduction to computer-aided drug design (CADD)
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References 15
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16 Chapter 1 Introduction to computer-aided drug design (CADD)
Chapter 2
Quantitative structure–activity relationship
Molecular desc riptors are the alphabet of QSAR, and statistical models are the language that
translates it into meaningful predictions. – Corwin Hansch
2.1 Quantitative structure–activity relationship (QSAR)
Quantitative structure–activity relationship (QSAR) is a powerful and widely used
computational approach in the field of chemistry and drug design [1]. It plays a cru-
cial role in predicting and understanding the biological activity of molecules based on
their structural features [2]. The concept of QSAR has revolutionized the way re-
searchers approach drug discovery and d esign, leading to the development of more
efficient and targeted pharmaceutical compounds [3]. QSAR is based on the idea that
the biological activity of a molecule can be correlated with its physicochemical prop-
erties and structural characteristics [4, 5]. By utilizing statistical and computational
techniques, QSAR models establish quantitative relationships between the chemical
structure of a molecule and its biological activity, typically against a specific target or
endpoint, such as receptor binding, enzyme inhibition, or toxicity [6]. This informa-
tion is invaluable for guiding the design of new molecules with improved potency and
reduced adverse effects.
The roots of QSAR can be traced back to the late nineteenth and early twentieth
centuries when scientists began to notice the connection between the chemical struc-
ture of compounds and their biological effects. How ever, it was not until the 1960s
that the term “quantitative structure–activity relationship” was formally introduced
by Corwin Hansch and his colleagues [7]. They were among the first to propose the
application of mathematical equations to describe the relationship between chemical
structure and biological activity. Their pioneering work laid the foundation for the
development of QSAR as a discipline [8].
The success of QSAR heavily relies on the availability of high-quality and diverse
datasets containing both chemical structures and corresponding biological activities [9].
In recent years, the accessibility of large databases and advancements in computational
power has significantly bolstered the development of QSAR models [10]. Additionally,
the emergence of new algorithms and machine learning techniques has further en-
hanced the predictive capabilities of QSAR models, making them more accurate and re-
liable [11]. One of the significant advantages of QSAR is its ability to prioritize molecules
for experimental testing, thereby reducing the cost and time associated with drug dis-
covery [12, 13]. QSAR models can effectively screen vast chemical libraries, highlighting
compounds with the highest probability of biological activity, thus streamlining the
drug development process. Furthermore, QSAR models can shed light on the underlying
https://doi.org/10.1515/9783111434858-002
Fig. 2.1: A general workflow for the development of any QSAR model in computer-aided drug designing.
18 Chapter 2 Quantitative structure–activity relationship
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