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Aman Thakur, Vineet Mehta, Priyanka Nagu and Kiran Goutam
Computer-Aided Drug Design
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Aman Thakur, Vineet Mehta, Priyanka Nagu
and Kiran Goutam
Computer-Aided
Drug Design
QSAR, Molecular Docking, Virtual Screening,
Homology and Pharmacophore Modeling
Authors
Aman Thakur
Adarsh Vijendra Institute of Pharmaceutical
Sciences
Shobhit University
Gangoh, Saharanpur 247341
Uttar Pradesh
India
amanthakur5052@gmail.com
Dr. Vineet Mehta
Department of Pharmacology
Government College of Pharmacy
Rohru 171207
Shimla, Himachal Pradesh
India
vineet.mehta20@gmail.com
Dr. Priyanka Nagu
Department of Pharmaceutics
Government College of Pharmacy
Rohru 171207
Shimla, Himachal Pradesh
India
priyanagupharma@gmail.com
Kiran Goutam
Shri Haridatt College of Pharmacy
Sewar, Bharatpur 321001, Rajasthan
India
gautam.kiran0701@gmail.com
ISBN 978-3-11-143474-2
e-ISBN (PDF) 978-3-11-143485-8
e-ISBN (EPUB) 978-3-11-143487-2
Library of Congress Control Number: 2024935223
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: koto_feja/E+/Getty Images
Typesetting: Integra Software Services Pvt. Ltd.
Printing and binding: CPI books GmbH, Leck
www.degruyter.com
Acknowledgments
We wish to express our profound gratitude to the institutions and individuals whose
support and guidance have been pivotal in the completion of this book, “Computer-
Aided Drug Design: QSAR, Molecular Docking, Virtual Screening, Homology, and
Pharmacophore Modeling.”
We extend our deepest appreciation to the Government College of Pharmacy,
Rohru, District Shimla, Himachal Pradesh. The institution’s unwavering commit-
ment to fostering a rigorous academic environment has been instrumental. We are
particularly grateful for the access to cutting-edge facilities and the intellectual sup-
port provided by the esteemed Director/ Principal Prof. (Dr.) Vivek Kumar Sharma
and other faculty members of the institute, which have significantly contributed to
the depth and quality of this work.
Our sincere gratitude to the Directorate of Technical Education, Vo cational
and Industrial Training, Sundernagar, District Mandi, Himachal Pradesh. The Di-
rectorate’s dedication to promoting excellence in technical education and vocational
training has been a cornerstone of the academic and professional development of the
students and faculty members of the Government College of Pharmacy, Rohru. Their
support has been crucial in enabling the research and compilation of this comprehen-
sive text.
We are profoundly grateful to Himachal Pradesh Technical University, Ha-
mirpur, Himachal Pradesh.Theuniversity’semphasisonresearchinnovationand
academic excellence has provided a fertile ground for our scholarly pursuits. The
collabo rative environment and the universi ty’s commitment to fostering interdisci-
plinary research have greatly enriched our work.
Wewouldalsoliketoacknowledgetheinvaluablecontributionsofourcol-
leagues, mentors, and students. Their critical insights, constructive feedback, and on-
going support have been indispensable throughout the research and writing process.
Their engagement and intellectual curiosity have greatly enhanced the scientific rigor
of this book.
Finally, we extend our heartfelt thanks to the family and friends for their unwa-
vering support and understanding. Their encouragement has been a constant source
of strength, allowing us to dedicate ourselves fully to this endeavor.
Thank you all for your indispensable contributions and support.
Aman Thakur
Dr. Vineet Mehta
Dr. Priyanka Nagu
Kiran Goutam
https://doi.org/10.1515/9783111434858-202
Contents
Acknowledgments V
Chapter 1
Introduction to computer-aided drug design (CADD) 1
1.1 Introduction 1
1.2 Drug discovery process 2
1.2.1 Discovery and development phase 2
1.2.2 Preclinical studies 2
1.2.3 Clinical trials 4
1.3 Computer-aided drug design (CADD) 6
1.3.1 Structure-based drug designing (SBDD) 6
1.3.2 Ligand-based drug designing (LBDD) 6
1.4 Historical perspective of CADD 8
1.5 Applications of CADD in the drug discovery process 10
1.5.1 Drawing molecular structures and energy minimization 10
1.5.2 Performing molecular docking for studying the ligand–protein
interaction 10
1.5.3 2D-QSAR modeling for the development of leads 11
1.5.4 3D-QSAR modeling 12
1.5.5 Homology modeling 12
1.5.6 Pharmacophore modeling and virtual screening 12
1.5.7 Molecular dynamics simulation 12
1.5.8 Prediction of absorption, distribution, metabolism, excretion, and
toxicity (ADMET) parameters 13
References 14
Chapter 2
Quantitative structure–activity relationship 17
2.1 Quantitative structure–activity relationship (QSAR) 17
2.2 Classification of QSAR methodologies 19
2.2.1 Based on the dimensions of descriptors used in the QSAR model 19
2.2.2 Based on the chemometric techniques employed for QSAR model
generation 24
2.2.3 Based on the activity to be correlated in the QSAR model 26
2.3 History and development of QSAR 26
References 31
Chapter 3
Physicochemical parameters and QSAR 34
3.1 Physicochemical parameters and QSAR 34
3.1.1 Lipophilicity parameters 35
3.1.2 Electronic parameters 40
3.1.3 Steric parameters 45
References 50
Chapter 4
Quantitative models in QSAR 53
4.1 Introduction 53
4.2 Hansch analysis 54
4.2.1 Importance of Hansch analysis in drug discovery 58
4.3 Craig plot 59
4.3.1 Importance of Craig plot in drug discovery 60
4.4 Topliss scheme 61
4.4.1 Topliss scheme I 62
4.4.2 Topliss scheme II 65
4.5 Free-Wilson analysis 68
4.5.1 The classical Free-Wilson model 68
4.5.2 The Fujita-Ban model (modified classical Free-Wilson model) 71
4.5.3 The relationship between Free-Wilson Analysis (FWA) and Hansch
analysis (mixed approach) 72
4.6 Development of a 2D-QSAR model 74
4.6.1 Topological descriptors 74
4.7 Deriving a 2D-QSAR model 76
References 78
Chapter 5
3D-QSAR in drug designing 80
5.1 Introduction 80
5.2 Basic assumptions in 3D-QSAR modeling 81
5.3 Classification of 3D-QSAR methodology 82
5.3.1 Based on the chemometric technique used in the development of the
QSAR equation 82
5.3.2 Based on the criterion of alignment 83
5.3.3 Based on the molecular modeling technique 83
5.4 Comparative molecular field analysis (CoMFA) 83
5.4.1 Methodology of CoMFA 84
5.5 Advantages of the CoMFA model 94
5.6 Limitations of the CoMFA model 95
5.7 Comparative molecular similar indices analysis (CoMSIA) 96
VIII Contents
5.8 Advantages of the CoMSIA model 97
5.9 Limitations of the CoMSIA model 98
5.10 Other 3D-QSAR approaches 99
References 103
Chapter 6
Statistical approaches used in the QSAR 105
6.1 Introduction 105
6.2 Linear regression analysis 106
6.2.1 Simple linear regression (SLR) 106
6.2.2 Multiple linear regression (MLR) 112
6.3 Multivariate data analysis (MVDA) 118
6.3.1 Principal component analysis (PCA) 118
6.3.2 Partial least square (PLS) analysis 122
6.3.3 Genetic function approximation (GFA) 127
6.3.4 Pattern recognition or multivariate data classification procedures or
cluster analysis 128
6.4 Statistical parameters for the validation of the QSAR models 133
6.4.1 Internal validation parameters for the QSAR model 133
6.4.2 External validation of the QSAR model 135
References 136
Chapter 7
Molecular and quantum mechanics in drug designing 139
7.1 Introduction 139
7.2 Molecular mechanics (MM) in drug design 140
7.2.1 Stretching and bending energy 141
7.2.2 Torsion energy 142
7.2.3 Nonbonding energy terms 143
7.2.4 Cross-bonding energy terms 145
7.2.5 Concept of force field in molecular mechanics (MM) 145
7.3 Quantum mechanics (QM) in drug designing 147
7.3.1 Ab initio methods in the CADD 148
7.3.2 Density functional theory (DFT) in CADD 151
7.3.3 Semiempirical methods in the CADD 153
References 157
Chapter 8
Energy minimization techniques in molecular modeling 160
8.1 Introduction 160
8.2 Concept of the local and global minima 161
8.3 Derivative energy minimization methods 163
Contents IX
8.3.1 First-order energy minimization methods 166
8.3.2 Second-order energy minimization methods 169
8.4 Nonderivative energy minimization methods (NDEMM) 173
8.4.1 Downhill simplex method of energy minimization 173
8.4.2 The sequential univariate method for energy minimization 176
8.5 Concept of global minima and bioactive conformer 177
8.5.1 Global minima 178
8.5.2 Bioactive conformers 179
8.5.3 Molecular docking 179
8.5.4 Molecular dynamics simulations 179
References 180
Chapter 9
Molecular docking and drug–receptor interactions 182
9.1 Introduction 182
9.2 Theory of molecular docking 183
9.2.1 Conformational search (search algorithms of the molecular
docking) 184
9.2.2 Scoring of the ligand–receptor binding 192
9.3 Classification of molecular docking techniques 198
9.3.1 Classification based on the flexibility of the molecules 199
9.3.2 Classification based on types of molecules used for molecular
docking 203
9.4 Steps involved in the molecular docking (protein–ligand docking) 204
9.4.1 Obtaining and optimizing the ligand structure 205
9.4.2 Obtaining the structure of the target receptor 206
9.4.3 Preparation of the protein 206
9.4.4 Performing the molecular docking 206
9.4.5 Analysis of the molecular docking results 207
9.5 Some examples of molecular docking 207
9.5.1 Agents acting on dihydrofolate reductase (DHFR) enzyme 207
9.5.2 Agents acting on the HMG-CoA reductase 209
9.5.3 Agents acting on HIV-protease 212
9.5.4 Agents acting on acetylcholinesterase (AChE) 214
References 217
Chapter 10
ADMET analysis in CADD 221
10.1 Introduction 221
10.2 Importance of ADME studies in CADD 223
10.3 Absorption parameters of the drug molecule 223
10.3.1 Partition coefficient/log P 224
X Contents
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