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analysis were met by the introduction of QSAR-specific software [23]. The development
of QSAR approaches in CADD during the 1980s brought about a major shift. Software
began to incorporate QSAR principles at this time, which made it possible for research-
ers to establish correlations between biological activity and molecular structures. This
development represented a significant turning point in the use of computational meth-
ods to forecast and maximize the activity of possible therapeutic options [24]. This period
illustrates how software tools are always being improved and diversified to keep up
with the changing field of molecular research and medication development. Within the
field of molecular modeling computational tools on different platforms, specialized soft-
ware packages have arisen to meet the needs of workstations, minicomputers, super-
computers, and personal computers. UCSF’s Peter Kollman and colleagues developed
AMBER, a notable example of a workstation or supercomputer that might be used for
computer-assisted model development, e nergy minimization, MD, and free energy
perturbation calculations. Another tool used for molecular modeling was Midas Plus
from the UCSF Computer Graphics Laboratory. Molecular Simulations Inc.’s QUANTA/
CHARMM and Harvard’s Martin Karplus directed CHARMM (Chemistry at HARva rd
Macromolecular Mechanics), which concentrated on quantum chemistry, QSAR, and
drug and molecule creation. Structure construction and modification were aided by Tri-
pos, Inc.’s SYBYL, while X-ray and NMR data processing were made easier by Biosym’s
Insight/DISCOVER, which was later acquired by Accelrys Inc. CADD approaches are
mathematical tools that can be used in various programs to change and measure the
attributes of possible drug candidates [25]. They comprise a variety of freely and com-
mercially accessible software programs; the selection that follows provides examples
of essential CADD tools, with a focus on those that are frequently utilized in our lab.
Tools for MD simulation like CHARMM, AMBER, NAMD, GROMACS, and OpenMM are
frequently utilized. If X-ray crystallography or nuclear ma gnetic resonance (NMR)
techniques were used to determine the 3D structure of a protein, RNA, or other mac-
romolecule for SBDD, the structure can be found in the protein data bank (PDB).
Alternatively, using an online web server like SWISS-MODEL, or a program like MOD-
ELLER and homology modeling techniques, a 3D structure can be built. In situations
where target binding site information is unavailable, potential binding sites can be
found using a variety of CADD techniques. One such program is the binding response
program, which, considering both the geometrical match and the binding energy of a
variety of different drug-like compounds to the protein regions under investigation,
finds potential binding sites. Additional binding site identification programs are Con-
Cavity and FINDSITE [26]. Large in silico compound databases are typically screened
using virtual database screening (VS) approaches to find possible binders for a query
target [27]. Several popular freeware programs, like AutoDock Vina, DOCK, and Auto-
Dock are examples of docking software frequently used for this purpose. Pharmer is
an additional program that uses 3D pharmacophores for database screening. The in
silico drug-like compound database is a crucial part of the VS-based CADD ligand dis-
covery process. ZINC is a publicly available database of compounds for VS that now
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has over 90 million compounds. For specific VS requirements, internal databases can
also be created, and chemical suppliers like ChemBridge and ChemDiv offer their
chemical catalogues for download in sdf format. The database should contain all
physiologically accessible protonation and tautomeric states of the ligands; however
converting these into 3D structures can be difficult. CADD software packages that may
be purchased commercially are Discovery Studio, OpenEye, Schrödinger, and MOE
[28]. Some common software used in CADD are given in Table 1.1.
1.4.4 Challenges and limitations
1.4.4.1 Predictive model accuracy
Since theoretical models are the foundation for MD simulations, docking scores, and
machine learning predictions, one of the main challenges in CADD is guaranteeing the
accuracy of computational models. The complex details of biological syst ems might
not be represented well by these models. Understanding the unique characteristics of
Table 1.1: Commonly used software in CADD.
Software Purpose
GHECOM Uses mathematical morphology and ligand pocket detection to find
pockets on protein surfaces
fpocket Ligand pocket detection
LIGSITE
csc
Automatic identification of pockets using the degree of conservation
PASS Uses geometry to characterize regions of buried volume
Biovia discovery studio –
Macro molecule tool
Detects sites utilizing an algorithm for site search and flood filling
AMBER Computer-assisted model building, energy minimization, molecular
dynamics, and free energy perturbation calculations
CHARMM, NAMD, GROMACS Molecular dynamic simulation
AutoDock Vina, DOCK,
AutoDock
Docking
Schrödinger A comprehensive suite of software tools for various aspects of drug
discovery, including molecular modeling, virtual screening, and
structure-based drug design
MOE (molecular operating
environment)
A comprehensive software package for molecular modeling,
bioinformatics, and cheminformatics
PyRx Open-source software for virtual screening based on the AutoDock suite
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scoring systems is crucial for improving accuracy [29]. In the field of drug discovery,
scoring algorithms play a crucial role in forecasting the binding affinity of compounds
and their targets. It is essential to actively reduce the possibility of false positives and
negatives in order to guarantee accuracy. This includes careful scoring parameter cal-
ibration, including a variety of chemical descriptors, and ongoing validation against
experimental data. It is possible to improve forecast reliability, for example, by rigor-
ously validating docking results against known binding affinities. Researchers can in-
crease trust in scoring algorithms and lower the possibility of inaccurate drug discovery
predictions by maximizing the trade-off between sensitivity and specificity.
1.4.4.2 Data quantity and quality
CADD tools’ predictions are only as accurate as the training set of data they use. If the
underlying data are poor or of low quality, the projections are probably wrong. One
continuous problem in the field of drug discovery through machine learning is the
absence of well selected, high-quality datasets [30]. Molecular interaction datasets can
be improved by removing outliers and making sure that data formatting is consistent.
This will re duce errors and increase the dependability of computer models. Further
enhancing data quality in CADD and guaranteeing reliable and strong results is by
application of standardized experimental techniques, such as uniform assay condi-
tions and endpoint measurements.
1.4.4.3 Excessive dependence on computational forecasts
Although CADD is an effective tool, relying too much on its projections without further
experimental confirmation may result in unproductive efforts. A good drug development
process requires balancing experimental evidence with computational predictions.
1.4.4.4 Time and computational cost
Several sophisticated CADD methods demand a significant amount of computational
power, particularly when they incorporate large-scale MD simulations or complex
machine learning algorithms. For some research groups, the related costs both in
terms of time and infrastructure may be unaffordable [31].
16 Arshdeep Singh et al.
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1.4.4.5 Representation of molecular flexibility
Most biological molecules are extremely flexible, including target proteins and possi-
ble pharmacological substances. It can be difficult to accurately reflect this flexibility,
particularly in methods such as molecular docking, and this can have a big effect on
the outcomes of CADD examinations [32].
The potential advantages of CADD in drug discovery are enormous, notwithstand-
ing these difficulties. Through recognition of these constraints and persistent effo rts
to overcome them via creativity and investigation, CADD will continue to lead the
way in contemporary drug development, influencing the direction of therapeutics.
1.5 Commercial medicines that used CADD in their
discovery phase
The pharmaceutical industry has undergone a revolution with the introduction of
CADD. Drugs have been designed and their binding ability has been predicted using
CADD. This allowed researchers to know or predict the type of interaction that the
drug will show with the target and, consequently, determine its efficacy and safety.
Some medications made with the aid of CADD are listed below; while the use of CADD
to design drug molecules has gained momentum from the twentieth century onward,
the medications of the twenty-first century have been discussed in Table 1.2 [33].
Table 1.2: Commercial medicines developed by aid of CADD in the twenty-first century.
Inhibitor
name
Protein target Computational contribution to drug discovery Approval
Lopinavir HIV- protease SBDD (transition-state mimetic concept) [D modeling
and docking. Energy minimization using DISCOVER CVFF
force field]
Imatinib
mesylate
Abl tyrosine kinase SBDD
Valsartan
(Diovan)
Angiotensin II
receptor
Superimposition of energy-minimized conformation and
QSAR
Fosamprenavir HIV- protease Structure-based design
Atazanavir HIV- protease Computational mapping of protein binding sites and
docking of the ligands
Enfuvirtide
(Fuzeon)
HIV- protease Homology modeling
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Table 1.2 (continued)
Inhibitor
name
Protein target Computational contribution to drug discovery Approval
Erlotinib EGFR kinase SBVS (Structure based virtual screening)
Darunavir Nonpeptidic HIV-
protease
Combined SBDD and LBDD
Maraviroc CCR/gp SB molecular modeling
Raltegravir HIV- integrase Combining MD with flexible-ligand docking
Tomudex Thymidylate
synthase
SBDD
Rivaroxaban Factor Xa HTS, SBDD, and virtual SAR
Crizotinib
(Xalcori)
ALK and ROS SBDD and SAR
Telaprevir NS/ A protease Substrate-based inhibitor design and structure-based
inhibitor optimization
Saroglitazar
(Cevoglitazar)
PPAR Combined virtual screening of D databases, SBDD, and
pharmacophore Modeling
Rucaparib
(Zepatier)
Poly (ADP-ribose)
polymerase
(PARP-)
Ligand-based molecular modeling
Grazoprevir
(Zepatier)
NS/ A protease Molecular modeling and docking-derived approach
Brigatinib
(Alunbrig)
ALK Docking and homology modeling
Betrixaban Serine protease
Factor Xa (fXa)
Molecular docking
Copanlisib
Hydrochloride
Phosphoinositide
-kinase (PIK)
SBDD (X-ray crystallography and docking) and LBDD
(based on lead scaffold)
Vaborbactam
(Vabomere)
β-Lactamase Docking and MD
Abemaciclib Cyclin-dependent
kinase
Structure–activity relationship studies in conjunction
with structure-based design
Apalutamide Androgen
receptor inhibitor
SBDD and SAR
Dacomitinib Oral kinase Combined FBDD and SBDD
Glasdegib
Maleate
Hedgehog
pathway
SBDD (molecular docking, virtual screening) and LBDD
(lead optimization and SAR)
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1.6 Future prospects
Thanks to advances in technology, greater computing capacity, and a growing under-
standing of biological processes, CADD has a bright future ahead of it. The develop-
ment of personalized medicine is one of the key factors influencing the course of
CADD. CADD seeks to customize treatments to each patient’s specific needs by combin-
ing lifestyle data, genetic information, and particular disease symptoms. This custom-
ized strategy could revolutionize the way we approach healthcare by increasing
efficacy and decreasing side effects. In the field of CADD, artificial intelligence (AI)
and machine learning (ML) have the potential to revolutionize the field. Drug discov-
ery procedures should be revolutionized by the incorporation of AI and ML into
CADD, which will make them quicker, more accurate, and resource-efficient. Addi-
tionally, CADD is expec ted to have a major influence on HTS techniqu es. Computa-
tional model-guided VS methods can help prioritize compounds for experimental
testing, which can optimize the drug discovery process. Time and money can be saved
by using this strategic approach to significantly minimize the number of chemicals
that need to be synthesized and tested in the lab. A significant focus in the field of
complex disease research is on developing medications that can target several biologi-
cal system components at once. It is expected that CADD technologies would be essen-
tial in anticipating and optimizing drug interactions with various targets, providing
opportunities for the creation of more potent therapeutic approaches. Another area
where CADD is making progress is fragment-based drug design. CADD helps identify
and optimize these fragments by focusing on tiny, fragment-like molecules that inter-
Table 1.2 (continued)
Inhibitor
name
Protein target Computational contribution to drug discovery Approval
Ivosidenib Isocitrate
dehydrogenase-
(IDH)
LBDD coupled with broad SAR profiling and modification
Larotrectinib
Sulfate
Tropomyosin-
related kinase
LBDD with SAR and crystal-binding mode similarity
Lorlatinib Tyrosine kinase SBDD and physical property-based optimization
Talazoparib
Tosylate
Poly(ADP-ribose)
polymerase-PARP
SBDD, SAR, and lead optimization
Darolutamide Androgen
receptor
SBDD (docking and MD)
Entrectinib Tyrosine kinase
inhibitor
SBDD and SAR
1 Historical development of computer-aided drug design 19
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act with a target, which helps design new and effective medications. Furthermore,
there is a growing trend to integrate CADD with experimental methods including bio-
physical tests and structural biology. By working together, this collaborative strategy
fills the gap between computational predictions and experimental results, enabling a
more thorough knowledge of the complex structure–activity interactions between
medications and their targets. In the future, CADD’s integration with modern technol-
ogies like blockchain, 3D printing, and virtual/augmented reality could completely
change the drug discovery process. These technologies offer new opportunities to im-
prove medication delivery systems, secure data, and give researchers better user-
friendly tools for manipulating and visualizing molecules.
1.7 Conclusion
The history of CADD indicates a transformative narrative of innovation and precision
in pharmaceutical research. From its beginnings in molecular modeling to the sophis-
ticated methodologies of today, CADD reshaped paradigms in drug discovery. Methods
such as SBDD and LBDD offer a variety of approaches depending on whether struc-
tural data is available, and VS techniques speed up the identification of promising
drug candidates. CADD software and tools are always evolving, giving access to more
computational power for researchers. This journey is a testament to a constant inno-
vation, interdisciplinary collaboration, and a pursuit of precision.
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Gita Chawla
✶
and Tathagata Pradhan
2 Lead-hit-based methods for drug design
and ligand identification
Abstract: The field of drug discovery has undergone a transformative shift with the ad-
vent of lead-hit-based methods, revolutionizing the process of drug design and ligand
identification. This chapter provides a comprehensi ve over view of these innovative
methodologies and their pivotal role in modern drug development. Lead discovery en-
tails the identification of promising compounds, or “hits,” which serve as starting points
for drug design. High-throughput screening (HTS) techniques, virtual screening, frag-
ment-based drug design (FBDD), phenotypic screening, and natural product screening
are examined in depth, highlighting their distinct approaches and applications. HTS en-
ables rapid assessment of vast compound libraries, while virtual screening employs
computational algorithms to predict ligand-target interactions. Phenotypic screening ex-
plores complex cellular responses, while natural product screening delves into nature’s
chemical diversity. The chapter discusses the integration of these methods with cutting-
edge computational tools and artificial intelligence, enhancing the accuracy and effi-
ciency of hit identification. By elucidating the synergy between experimental and
computational approaches, this chapter underscores the transformative impact of lead-
hit-based methods in shaping the future of drug discovery.
Keywords: Drug design, lead discovery, hit identification, ligand screening, computa-
tional chemistry
2.1 Introduction
Drug design is a complex process that involves the discovery and development of new
medications to treat various diseases and medical conditions. It typically consists of
several stages, which can vary in duration and complexity depending on the specific
therapeutic area [1]. The general stages involved in the drug design process are as fol-
lows (as depicted in Figure 2.1):
✶
Corresponding author: Gita Chawla, Department of Pharmaceutical Chemistry, School of
Pharmaceutical Education and Research, Jamia Hamdard, Hamdard Nagar, New Delhi 110062, Delhi,
India, e-mail: gchawla@jamiahamdard.ac.in
Tathagata Pradhan, Department of Pharmaceutical Chemistry, School of Pharmaceutical Education
and Research, Jamia Hamdard, Hamdard Nagar, New Delhi 110062, Delhi, India
https://doi.org/10.1515/9783111207117-002
https://t.me/med1917
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