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Pooja A. Chawla, Dilpreet Singh, Kamal Dua, Muralikrishnan Dhanasekaran
and Viney Chawla (Eds.)
Computational Drug Discovery
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Computational
Drug Discovery
Molecular Simulation for Medicinal Chemistry
Volume 1
Edited by
Pooja A. Chawla, Dilpreet Singh, Kamal Dua,
Muralikrishnan Dhanasekaran and Viney Chawla
Editors
Prof. (Dr.) Pooja A Chawla
University Institute of Pharmaceutical Sciences and
Research
Baba Farid University of Health Sciences
Sadiq Road
Faridkot 151203, Punjab
India
pvchawla@gmail.com
Dr. Dilpreet Singh
University Institute of Pharma Sciences,
Chandigarh University,
Gharuan, Mohali, 140413, India
dilpreet.daman@gmail.com
Dr. Kamal Dua
Australian Research Centre in
Complementary and Integrative Medicine
Faculty of Health
University of Technology Sydney
235-253 Jones St
Ultimo 2007
Australia
Kamal.Dua@uts.edu.au
Prof. Dr. Muralikrishnan Dhanasekaran
Department of Drug Discovery and Development
Harrison College of Pharmacy
Auburn University
3306B Walker Building
Auburn AL 36849
United States of America
dhanamu@auburn.edu
Prof. Dr. Viney Chawla
University Institute of Pharmaceutical Sciences and
Research
Baba Farid University of Health Sciences
Sadiq Road
Faridkot 151203, Punjab
India
drvineychawla@gmail.com
ISBN 978-3-11-120669-1
e-ISBN (PDF) 978-3-11-120711-7
e-ISBN (EPUB) 978-3-11-120759-9
Library of Congress Control Number: 2024943364
Bibliographic information published by the Deutsche Nationalbibliothek
The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie;
detailed bibliographic data are available on the internet at http://dnb.dnb.de.
© 2024 Walter de Gruyter GmbH, Berlin/Boston
Cover image: AliseFox/iStock/Getty Images Plus
Typesetting: Integra Software Services Pvt. Ltd.
Printing and binding: CPI books GmbH, Leck
www.degruyter.com
https://t.me/med1917
Contents
Arshdeep Singh, Rabin Debnath, Viney Chawla, and Pooja A. Chawla
1 Historical development of computer-aided drug design 1
Gita Chawla and Tathagata Pradhan
2 Lead-hit-based methods for drug design and ligand identification 23
Gagandeep Kaur, Isha Rani, Prabodh Chander Sharma,
and Diksha Gulati
3 Virtual screening tools in ligand and receptor-based drug design 51
Pawan Kumar and Ajit Kumar
4 State-of-the-art modeling techniques in performing docking algorithms
and scoring 65
Yash Chauhan, Ajay Sharma, Arya Lakshmi Marisatti, Neha Singh, Sahil Kumar, and
Kalicharan Sharma
5 Design of computational chiral compounds for drug discovery and
development 81
Biswa Mohan Sahoo, Pooja Chawla, Subas Chandra Dinda, Narahari Narayan Palei,
Bhupendra Singh, and Bibhas Chandra Mohanta
6 Role of integrated bioinformatics in structure-based drug design 91
Vipul Kumar, Rakhi, Sahil Kumar, Kalicharan Sharma, and Rajesh K. Singh
7 Molecular recognizable tools in X-ray crystallography in computer-aided
drug design 133
Disha Tewari, Priyanka Sharma, Shalini Mathpal, Kalpana Rawat, Tushar Joshi,
Subhash Chandra, and Veena Pande
8 Design of target hit molecules using molecular dynamic simulations:
special key aspects of GROMACS or Role of molecular dynamic
simulations in designing a hit molecule for drug discovery 151
Anchal Sharma, Nitish Kumar, Jyoti, Aanchal Khanna,
and Preet Mohinder Singh Bedi
9 Computational prediction of drug-limited solubility and
CYP450-mediated biotransformation 175
https://t.me/med1917
Abhimannu Shome, Chahat, Keshav Taruneshwar Jha, Pooja A. Chawla,
and Muralikrishnan Dhanasekaran
10 Recent advancement in binding free-energy calculation 211
Anuradha Mehra, Vanktesh Kumar, Bhupinder Kapoor, Monica Gulati,
and Pankaj Wadhwa
11 Role of structural genomics in drug discovery 243
Mohammad Ovais Dar, Aamir Tariq Malla, Zahid Ahmad Paul, Roohi Mohi‑ud‑din,
Mubashir Hussain Masoodi, Pooja A Chawla, and Reyaz Hassan Mir
12 Unlocking therapeutic potential: computational approaches for enzyme
inhibition discovery 295
Bhupender Nehra, Manoj Kumar, Pooja A. Chawla, and Viney Chawla
13 Role of spectroscopy in drug discovery 319
Kannan Sadasivam, Venkata Surya Kumar Choutipalli, and Lalitha Gummidi
14 Computer-aided design of peptidomimetic therapeutics 351
Bhupender Nehra, Manoj Kumar, Pooja A. Chawla, Viney Chawla, Monika,
Honey Goel, and Imtiyaz Ahmed Najar
15 Developing safer therapeutic agents through toxicity prediction 379
Bhupender Nehra, Manoj Kumar, Pooja A. Chawla, Viney Chawla, and Sarita Pawar
16 Identifying prominent molecular targets in the fight against drug
resistance 403
Index 429
VI Contents
https://t.me/med1917
Arshdeep Singh, Rabin Debnath, Viney Chawla, and Pooja A. Chawla
✶
1 Historical development of computer-aided
drug design
Abstract: The 1970s witnessed the beginning of the historical development of com-
puter-aided drug design (CADD), which revolutionised drug discovery by utilising
computational techniques to increase accuracy and efficiency. Molecular modelling
and quantitative structure-activity relationship (QSAR) models, which connected
chemical structures with biological activities to forecast medication efficacy, were the
main focuses of early CADD initiatives. Developments in molecular docking and dy-
namics simulations were crucia l in the 1980s and 1990s. While molecular dynamics
simulations looked at the stability and interactions of drug compounds over time, mo-
lecular docking predicted how drug candidates would bind to target proteins. Drug
development was further expedited by the late 20th century combination of combina-
torial chemistry and high-throughput screening, which combined CADD with experi-
mental methods. With the introduction of artificial intelligence (AI) and machine
learning (ML) into CADD, a major advancement has occurred in the twenty-first cen-
tury. These tools aid in the search for new treatment candidates by quickly and accu-
rately analysing large datasets, spotting patterns, and making predictions about the
future. All things considered, CADD’s development is a reflection of ongoing advances
in computer technology, which makes it an essential part of contemporary pharma-
ceutical research. It keeps changing, taking advantage of fresh chances in medication
discovery and design as well as new obstacles.
1.1 Background
Within the broad field of pharmaceutical innovation, the development of computer-
aided drug design (CADD) is evidence of the collaboration between medical research
and computational technology. This chapter takes the reader on a historical tour, illus-
trating the complex growth and important turning points that shaped the framework of
CADD and permanently changed the face of drug development. The story of CADD
✶
Corresponding author: Pooja A. Chawla, University Institute of Pharmaceutical Sciences
and Research, Baba Farid University of Health Sciences, Faridkot 151203, Punjab, India,
e-mail: pvchawla@gmail.com
Arshdeep Singh, Rabin Debnath, Department of Pharmaceutical Chemistry, ISF College of Pharmacy,
Ghal Kalan, G.T Road, Moga 142001, Punjab, India
Viney Chawla, University Institute of Pharmaceutical Sciences and Research, Baba Farid University of
Health Sciences, Faridkot, Punjab, India
https://doi.org/10.1515/9783111207117-001
https://t.me/med1917
started in the middle of the twentieth century, a time of growing scientific curiosity and
rapid technological development. The combination of theoretical models and computer
algorithms transformed the way scientists understood molecular structures and interac-
tions and this is where CADD got its start. The foundation for comprehending the three-
dimensional (3D) structures of molecules was established by innovatory thinkers like
Linus Pauling and Dorothy Crowfoot Hodgkin, which ultimately led to the development
of CADD. Drug discovery techniques underwent a paradigm shift in the 1970s when
computational techniques began to be included into pharmaceutical research. During
this period, the first algorithms for predicting the molecular interactions were devel-
oped. The groundwork for the incorporation of computational techniques into drug de-
sign was laid by significant advancements, such as the release of significant publications
on molecular dynamics (MD) by innovators like Arieh Warshel and Martin Karplus. The
1980s and 1990s saw the rise of more complex algorithms and increases in computa-
tional power, which marked the beginning of CADD’s golden era. With the advent of
powerful technologies like quantum mechanics, molecular docking, pharmacophore
modeling, and QSAR (quantitative structure–activity relationship), scientists were able to
screen and optimize possible drug candidates much more quickly. Interactions among
biologists, pharmaceutical corporations, and computational chemists flourished, leading
to a period of multidisciplinary synergy. As the twenty-first century continued, CADD
emerged as a crucial component of modern drug research. The drug development pipe-
line has been greatly expedited by its capacity to expedite lead identification, anticipate
drug–target interaction, and optimize pharmacokinetic features. But along with the im-
mense potential came great challenges of computational complexity and predictive
model accuracy, and ethical issues became focus points that need ongoing innovation
and ethical considerations.
1.2 Traditional drug discovery
The roots of traditional drug discovery indeed sprouted from natural sources, chance
discoveries, and empirical testing rather than the structured and targeted approaches
used in modern times. It relied on trial-and-error testing of substances on cells or ani-
mals, often culminating in single-compound-based medicines derived from whole plant
extracts. While this approach yielded much valuable therapeutics, it also posed signifi-
cant challenges. These challenges included inconsistent medicinal performance, limited
targeting capabilities, and formulation constraints. The high costs and slow pace of
these methods rendered them impractical in the face of growing demands, prompting
the pharmaceutical industry to seek more efficient and streamlined approaches. The
evolution of pharmaceutical science and technology has revolutionized drug discovery
[1]. Modern methodologies leverage sophisticated techniques such as high-throughput
screening (HTS), computational modeling, and genomics. These advancements have ex-
2 Arshdeep Singh et al.
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pedited the drug discovery process, allowing for more targeted and efficient identifica-
tion of potential drug candidates. Despite these advancements, transitioning from the
initial hit stage to a viable lead drug remains a formidable challenge. Researchers and
the pharmaceutical industry are constantly exploring new methodologies, technologies,
and interdisciplinary approaches to accelerate this phase of drug development. Efforts
are underway to enhance the efficiency of lead identification, optimize compound prop-
erties, and improve the understanding of drug–target interactions. Continuous explora-
tion and integration of cutting-edge technologies from various research domains are
essential in the quest to overcome the challenges inherent in the drug discovery process.
The aim is to create a more agile, cost-effective, and robust framework for identifying
and developing promising drug candidates, ultimately translating scientific discoveries
into transformative therapies for various diseases and conditions. The traditional proce-
dure of drug discovery was illustrated in Figure 1.1 [2].
1.3 Drug discovery process
The methodical process of drug development aims to find substances that can effec-
tively control or manage the underlying causes of illnesses. First, a wide range of
chemical compounds are screened to identi fy the most potential targets for disease.
Understanding the configuration of the drug receptor becomes crucial for efficiently
designing drug molecules to the binding site. Target identification is the first phase in
the multistep process of finding new drugs. Next come lead identification, optimiza-
tion, and discovery. Preclinical and clinical trials then take center stage to verify the
10,000
compounds
Drug discovery
Preclinical
Phase
Clinical Phase
1–4
FDA Approved
250
compounds
10–14 years
>1 Billion dollars
5
compounds
1
Drugs
Figure 1.1: Traditional method of drug discovery.
1 Historical development of computer-aided drug design 3
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