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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5440_Библиотеки_им_академика_М_И_Перельмана.pdf
Computational Methods for Rational Drug Design
Computational Methods for Rational Drug Design
Edited by
Mithun Rudrapal
Department of Pharmaceutical Sciences,
School of Biotechnology and Pharmaceutical Sciences,
Vignan’s Foundation for Science, Technology & Research,
Guntur, Andhra Pradesh,
India
Copyright © 2025 by John Wiley & Sons, Inc. All rights reserved, including rights for text and data mining and
training of artificial technologies or similar technologies.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
Published simultaneously in Canada.
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Library of Congress Cataloging-in-Publication Data
Names: Rudrapal, Mithun, editor.
Title: Computational methods for rational drug design / edited by Mithun
Rudrapal.
Description: Hoboken, New Jersey. : Wiley, [2025] | Includes index.
Identifiers: LCCN 2024045117 (print) | LCCN 2024045118 (ebook) | ISBN
9781394249169 (hardback) | ISBN 9781394249183 (adobe pdf) | ISBN
9781394249176 (epub)
Subjects: MESH: Drug Design | Models, Molecular
Classification: LCC RM301.25 (print) | LCC RM301.25 (ebook) | NLM QV 745
| DDC 615.1/9–dc23/eng/20241009
LC record available at https://lccn.loc.gov/2024045117
LC ebook record available at https://lccn.loc.gov/2024045118
Cover Design: Wiley
Cover Image: © Grafissimo/Getty Images
Set in 9.5/12.5pt STIXTwoText by Straive, Pondicherry, India
v
List of Contributors xxi
Preface xxvii
1 Molecular Modeling and Drug Design 1
Monalisa Kesh, Abhirup Ghosh, and Diptanil Biswas
1.1 Introduction 1
1.1.1 What Is Molecular Modeling? 1
1.1.2 Software Used for Molecular Modeling 2
1.1.2.1 Schrodinger 2
1.1.2.2 GROMACS 2
1.1.2.3 Amber 2
1.1.2.4 CHARMM 2
1.1.2.5 AutoDock 2
1.1.2.6 VMD 2
1.1.2.7 PyMOL 2
1.1.2.8 Open Babel 2
1.1.2.9 Avogadro 3
1.1.2.10 Discovery Studio 3
1.1.3 Molecular Mechanics 3
1.1.3.1 Prediction of Binding Affinity 3
1.1.3.2 Conformational Analysis 3
1.1.3.3 Virtual Screening 3
1.1.3.4 Lead Discovery 3
1.1.3.5 Mechanism of Action 3
1.2 Types of Molecular Models 4
1.2.1 Ball-and-Spoke Model 5
1.2.1.1 Future Directions 5
1.2.2 Space-filling Models 6
1.2.2.1 Future Directions 6
1.2.3 Crystal Lattice Models 6
1.2.3.1 Future Directions 7
1.3 Computational Methods in Drug Discovery 7
1.3.1 What Is Drug Discovery? 7
1.3.2 Computational Platforms for Drug Discovery 8
1.3.2.1 NCBI 8
Contents
Contentsvi
1.3.2.2 Chemical Databases 9
1.3.2.3 PDB 9
1.3.2.4 AutoDock, AutoDock Vina, DOCK, PatchDock, HADDOCK, SwissDock,
Glide, Gold, FlexX, UCSF Chimera, and DockThor 9
1.3.2.5 UniProt 9
1.3.2.6 QSAR 9
1.3.2.7 GROMACS, AMBER, NAMD, PLUMED, LAMMPS, CHARMM, GROMOS,
OpenMM, Orac, XMD, YASARA, Ms2, MacroModel, and Avizo 10
1.3.2.8 Desmond 10
1.3.2.9 OpenBabel 10
1.3.2.10 DeepChem and Cheminformatics for Python (RDKit) 10
1.3.2.11 SBML 11
1.3.2.12 Virtual Screening 11
1.3.3 Applications of Computer-Based Methods in Steps of Drug Discovery 11
1.4 Potential Use and Application of AI in Drug Designing 12
1.4.1 Target Identification and Validation 12
1.4.2 Drug Screening and Lead Optimization 12
1.4.3 De Novo Drug Design 12
1.4.4 Predictive Toxicology and ADMET 13
1.4.5 Clinical Trial Optimization 13
1.4.6 Drug Repurposing 14
1.4.7 Concept of Personalized Medicine 14
1.4.8 Drug Combination Optimization 14
1.5 Limitations of Current Methods 14
1.5.1 Data Restrictions 15
1.5.2 Interpretability 15
1.5.3 Generalization 15
1.5.4 Resources and Computation 15
1.5.5 Ethical Considerations 15
1.5.6 Validation and Experimentation 15
1.5.7 Regulatory Obstacles 15
1.6 Case Studies 16
1.6.1 Case Study 1: “Accelerating Drug Discovery with AI-Powered Molecular Modeling”
by Dr. Jane Mitchell 16
1.6.2 Case Study 2: “AI-Driven Drug Design for Rare Genetic Disorders”
by Prof. David Reynolds 16
1.6.3 Case Study 3: “Revolutionizing Drug Repurposing with AI During the COVID-19
Pandemic” by Dr. Maria Fernandez 16
1.7 Molecular Docking 17
1.7.1 What Is Molecular Docking? 17
1.7.1.1 Procedure 17
1.7.1.2 Biophysical Laws 17
1.7.1.3 Rigid and Flexible Docking 18
1.7.1.4 Types of Docking 18
1.7.1.5 Challenges and Future Perspectives 18
1.7.2 Applications of Molecular Docking in Drug Designing 18
1.7.3 Success of Molecular Docking Cases in Drug Designing 18
Contents vii
1.8 Conclusion and Future Works 19
References 20
2 Bioactive Small Molecules and Drug Discovery 25
Ashish Shah, Vaishali Patel, Sathiaseelan Perumal, Riddhi Dave, Neha Zachariah,
Ghanshyam Parmar, and Jay Mukesh Chudasama
2.1 Introduction 25
2.1.1 Introduction to Drug Design and Discovery 25
2.1.2 Brief History of Small-Molecule Drug Discovery 26
2.1.3 Importance of Bioactive Small Molecules in Drug Discovery 26
2.2 Importance of Computational Methods in Bioactive Small-Molecules Discovery 26
2.2.1 Structure-Based Methods 27
2.2.2 Ligand-Based Methods 27
2.2.3 Network-Based Methods 29
2.3 Natural Products in Bioactive Small-Molecule Discovery 30
2.3.1 Plant Primary and Secondary Molecules as Bioactive Molecules 30
2.3.2 Anticancer Agents as Bioactive Molecules 31
2.3.3 Antiviral Agents as Bioactive Molecules 32
2.3.4 Antimalarial Agents as Bioactive Molecules 32
2.3.5 Marine Bioactive Products 33
2.4 Role of Density Functional Theory (DFT) Studies in Bioactive
Small-Molecule Discovery 33
2.4.1 Importance of DFT in Small-Molecule Drug Discovery 33
2.5 Application of DFT to Bioactive Small Molecules 34
2.5.1 HOMO–LUMO Calculation 34
2.5.1.1 Molecular Electrostatic Potential (MEP) Map 35
2.5.1.2 The Two Main Methods Used in Population Statistics Are the Mulliken and Natural
Population Analyses 36
2.5.1.3 Natural Bond Orbital (NBO) Analysis 36
2.5.1.4 Implementations and Tools 36
2.6 Factors Affecting the Choice of Bioactive Molecules in Drug Discovery 36
2.6.1 Target Identification and Validation 37
2.6.2 Target Specificity 39
2.6.3 Bioavailability and Pharmacokinetics 39
2.6.4 Chemical Structure and Drug-likeness 40
2.6.5 Safety and Toxicity 41
2.6.6 Toxicity and Side Effects 41
2.6.7 Cost-Effectiveness, Synthetic Feasibility, and Scalability 41
2.6.8 Structural Diversity and Novelty 42
2.6.9 Patentability and Intellectual Property 42
2.7 Conclusion 43
References 43
3 Novel Drug Targets for Small Molecule-based Drug Discovery 49
Raghu Ram Achar, Ipsita Panigrahi, Aditi Singh, N. Chandana,
and Shivananju Nanjunda Swamy
3.1 Introduction 49
Contentsviii
3.2 Drug Target Identification 51
3.3 Classification of Novel Drug Targets 53
3.3.1 Transcription Factors 53
3.3.2 Cytokines 53
3.3.3 Chaperones 54
3.3.4 Viral Targets 55
3.3.5 G Protein-coupled Receptors 55
3.3.6 Transporters 56
3.3.7 Enzymes 56
3.3.8 RNA Targets 57
3.4 Small Molecules as Drugs 57
3.5 Conclusion 60
References 65
4 Computer-assisted Methods and Tools for Structure- and Ligand-based
Drug Design 69
Saurav Kumar Mishra, Sneha Roy, Tabsum Chhetri, and John J. Georrge
4.1 Introduction 69
4.2 Structure-Based Drug Discovery Concept 69
4.2.1 Structure Generation of the Target 70
4.2.1.1 The Detailed Description of Each Tool 70
4.2.2 Active Binding Site Within the Target 75
4.2.2.1 The Detailed Description of Each Tool 75
4.2.2.2 Molecular Docking Analysis 77
4.2.2.3 The Detailed Description of Each Tool 78
4.2.3 Molecular Dynamic Simulations 79
4.2.3.1 The Detailed Description of Each Tool 80
4.3 Ligand-Based Drug Discovery Concept 81
4.3.1.1 The Detailed Description of Each Tool 82
4.4 Structure- and Ligand-Based Assisted Studies 84
4.4.1 The Detailed Description of Each Tool 85
4.4.2 The Detailed Description of Each Tool 88
4.5 Advancement and Challenges in SBDD and LBDD 90
4.6 Conclusion 90
References 91
5 Virtual Screening and Lead Discovery 97
Nisha Kumari Singh, Nigam Jyoti Maiti, Manshi Mishra, Shantanu Raj,
Gourav Rakshit, Rahul Ghosh, and Sharanya Roy
5.1 Introduction to Virtual Screening and Lead Discovery 97
5.1.1 Overview of Drug Discovery Process 97
5.1.2 Role of Virtual Screening 98
5.1.3 Importance of Lead Discovery 99
5.2 Molecular Targets and Biomolecular Structures 99
5.3 Virtual Screening Approaches 99
5.3.1 Structure-based Virtual Screening 99
Contents ix
5.3.2 Ligand-based Virtual Screening 100
5.3.3 Hybrid Approaches 100
5.4 Databases and Compound Collections 101
5.4.1 Overview of Chemical Databases 101
5.4.2 Compound Filtering and Preparation 102
5.4.3 Diversity and Size of Compound Collections 102
5.5 Molecular Docking 102
5.5.1 Principles of Molecular Docking 102
5.5.2 Docking Algorithms and Scoring Functions 103
5.5.3 Validation of Docking Results 103
5.6 Pharmacophore Modeling 104
5.6.1 Concept of Pharmacophores 104
5.6.2 Generating Pharmacophore Models 104
5.6.3 Applications in Lead Discovery 105
5.7 Quantitative Structure–Activity Relationship (QSAR) 105
5.7.1 Basics of QSAR 105
5.7.2 Model Development and Validation 106
5.7.3 QSAR in Virtual Screening 107
5.8 Machine Learning and AI in Virtual Screening 107
5.8.1 Introduction to Machine Learning and AI 107
5.8.2 Feature Selection and Model Training 108
5.8.3 Applications in Virtual Screening 108
5.9 Hit-to-Lead Optimization 109
5.9.1 Prioritizing Hits from Virtual Screening 109
5.9.2 SAR Analysis and Iterative Design 109
5.9.2.1 SAR Analysis (Structure–Activity Relationship) 109
5.9.2.2 Iterative Design 109
5.9.3 ADME/Tox Considerations 110
5.9.3.1 ADME (Absorption, Distribution, Metabolism, Excretion) 110
5.9.3.2 Toxicity Considerations 110
5.10 Case Studies and Examples 112
5.10.1 Exploration Protocol for Mutant-targeted PI3K Inhibitors 112
5.10.2 Enhancing Virtual Screening Hit Rate: Implementation on the RXRα
Nuclear Receptor 112
5.11 Challenges and Future Directions 114
5.11.1 Limitations of Virtual Screening 114
5.11.2 Emerging Technologies and Trends 115
5.11.3 Integration with High-throughput Experimentation 115
5.12 Ethical and Regulatory Considerations 116
5.12.1 Intellectual Property and Patents 116
5.12.2 Ethical Use of Computational Tools 116
5.12.3 Regulatory Approval Process 116
5.13 Conclusion 116
5.13.1 Future Prospects in Virtual Screening and Lead Discovery 116
5.13.2 Summary of Key Points 117
References 117
Contentsx
6 ADMET and Physicochemical Assessments in Drug Design 123
Ulviye Acar Çevik, Ayşen Işik, and Abdüllatif Karakaya
6.1 ADMET 123
6.1.1 Absorption 123
6.1.1.1 Solubility and Dissolution 125
6.1.1.2 Lipophilicity 126
6.1.1.3 Permeability 128
6.1.2 Distribution 129
6.1.3 Metabolism 131
6.1.4 Excretion 133
6.1.5 Toxicity 134
6.2 Physicochemical Assessments 135
6.2.1 Partition Coefficient 135
6.2.2 Log D: Ionizable Compound Lipophilicity 136
6.2.2.1 Methods for Calculating Lipophilicity 136
6.2.2.2 Direct Experimental Determination of Lipophilicity 137
6.2.2.3 Indirect Experimental Determination of Lipophilicity 137
6.2.3 Acid–Base Properties and Ionization 138
6.2.4 Solubility 140
6.2.5 Polymorphism 142
6.2.6 Molecular Weight 143
6.2.7 Number of Hydrogen Bond Donors (HDB) and Acceptors (HDA) 144
References 144
7 In Silico Modeling and Drug Design 153
Sonali S. Shinde, Sanket S. Rathod, and Sohan S. Chitlange
7.1 Introduction 153
7.2 Target Identification 154
7.2.1 Experimental Approaches 154
7.2.2 Computational Target Identification 155
7.2.3 Target Validation 155
7.3 Computer-Aided Drug Design 156
7.3.1 Ligand-based CADD 157
7.3.2 Structure-Based CADD 158
7.4 ADMET Assessment 160
7.5 Conclusion 160
References 161
8 Pharmacophore Modeling in Drug Design 167
Rahul Ghosh, Sharanya Roy, Gourav Rakshit, Nisha Kumari Singh, and Nigam Jyoti Maiti
8.1 Introduction 167
8.1.1 The Role of Pharmacophore Modeling in Drug Design 167
8.1.2 Historical Perspective and Evolution of Pharmacophore Concepts 170
8.2 Essential Concepts in Pharmacophore Hypothesis Generation 170
8.2.1.1 Partitioning Initial Data into Distinctive Datasets 172
8.3 Diverse Approaches to Pharmacophore Modeling 173
8.3.1 Ligand-Based Pharmacophore Modeling 174
8.3.2 Structure-Based Pharmacophore Modeling 175