Computational Methods for Rational Drug Design E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 1231

Veröffentlichungsjahr: 2024

Beschreibung

Comprehensive resource covering computational tools and techniques for the development of cost-effective drugs to combat diseases, with specific disease examples

Computational Methods for Rational Drug Design covers the tools and techniques of drug design with applications to the discovery of small molecule-based therapeutics, detailing methodologies and practical applications and addressing the challenges of techniques like AI/ML and drug design for unknown receptor structures. Divided into 23 chapters, the contributors address various cutting-edge areas of therapeutic importance such as neurodegenerative disorders, cancer, multi-drug resistant bacterial infections, inflammatory diseases, and viral infections.

Edited by a highly qualified academic with significant research contributions to the field, Computational Methods for Rational Drug Design explores topics including:

• Computer-assisted methods and tools for structure- and ligand-based drug design, virtual screening and lead discovery, and ADMET and physicochemical assessments
• In silico and pharmacophore modeling, fragment-based design, de novo drug design and scaffold hopping, network-based methods and drug discovery
• Rational design of natural products, peptides, enzyme inhibitors, drugs for neurodegenerative disorders, anti-inflammatory therapeutics, antibacterials for multi-drug resistant infections, and antiviral and anticancer therapeutics
• Protac and protide strategies in drug design, intrinsically disordered proteins (IDPs) in drug discovery and lung cancer treatment through ALK receptor-targeted drug metabolism and pharmacokinetics

Helping readers seamlessly navigate the challenges of drug design, Computational Methods for Rational Drug Design is an essential reference for pharmaceutical and medicinal chemists, biochemists, pharmacologists, and phytochemists, along with molecular modeling and computational drug discovery professionals.

Leseprobe

Table of Contents

Cover

Table of Contents

Title Page

Copyright Page

List of Contributors

Preface

1 Molecular Modeling and Drug Design

1.1 Introduction

1.2 Types of Molecular Models

1.3 Computational Methods in Drug Discovery

1.4 Potential Use and Application of AI in Drug Designing

1.5 Limitations of Current Methods

1.6 Case Studies

1.7 Molecular Docking

1.8 Conclusion and Future Works

References

2 Bioactive Small Molecules and Drug Discovery

2.1 Introduction

2.2 Importance of Computational Methods in Bioactive Small‐Molecules Discovery

2.3 Natural Products in Bioactive Small‐Molecule Discovery

2.4 Role of Density Functional Theory (DFT) Studies in Bioactive Small‐Molecule Discovery

2.5 Application of DFT to Bioactive Small Molecules

2.6 Factors Affecting the Choice of Bioactive Molecules in Drug Discovery

2.7 Conclusion

References

3 Novel Drug Targets for Small Molecule‐based Drug Discovery

3.1 Introduction

3.2 Drug Target Identification

3.3 Classification of Novel Drug Targets

3.4 Small Molecules as Drugs

3.5 Conclusion

References

4 Computer‐assisted Methods and Tools for Structure‐ and Ligand‐based Drug Design

4.1 Introduction

4.2 Structure‐Based Drug Discovery Concept

4.3 Ligand‐Based Drug Discovery Concept

4.4 Structure‐ and Ligand‐Based Assisted Studies

4.5 Advancement and Challenges in SBDD and LBDD

4.6 Conclusion

References

5 Virtual Screening and Lead Discovery

5.1 Introduction to Virtual Screening and Lead Discovery

5.2 Molecular Targets and Biomolecular Structures

5.3 Virtual Screening Approaches

5.4 Databases and Compound Collections

5.5 Molecular Docking

5.6 Pharmacophore Modeling

5.7 Quantitative Structure–Activity Relationship (QSAR)

5.8 Machine Learning and AI in Virtual Screening

5.9 Hit‐to‐Lead Optimization

5.10 Case Studies and Examples

5.11 Challenges and Future Directions

5.12 Ethical and Regulatory Considerations

5.13 Conclusion

References

6 ADMET and Physicochemical Assessments in Drug Design

6.1 ADMET

6.2 Physicochemical Assessments

References

7

In Silico

Modeling and Drug Design

7.1 Introduction

7.2 Target Identification

7.3 Computer‐Aided Drug Design

7.4 ADMET Assessment

7.5 Conclusion

References

8 Pharmacophore Modeling in Drug Design

8.1 Introduction

8.2 Essential Concepts in Pharmacophore Hypothesis Generation

8.3 Diverse Approaches to Pharmacophore Modeling

8.4 Application of Pharmacophore Modeling

8.5 Emerging Trends in Pharmacophore Model Development

8.6 Case Studies

8.7 Challenges in Pharmacophore Modeling

8.8 Conclusion

Acknowledgments

References

9 Scaffold Hopping and

De Novo

Drug Design

9.1 Introduction

9.2 Scaffold Hopping

9.3

De Novo

Drug Design

9.4 Results and Discussion

9.5 Software Tools for SH (Scaffold Hopping) and

De Novo

Design Selection

9.6 Case Study

9.7 Conclusion

References

10 Fragment‐based Drug Design and Drug Discovery

10.1 Introduction

10.2 The Process of Finding Fragments

10.3 FBDD Strategies

10.4 Case Studies

10.5 Conclusion and Future Perspectives

References

11 AI/ML Approaches in Drug Design

11.1 Introduction

11.2 Traditional Drug Design Methods

11.3 AI/ML Landscape in Drug Design

11.4 Ethics, Reliability, and Regulatory Issues

11.5 Future Directions

11.6 Conclusion

References

12 Network‐based Methods in Drug Discovery

12.1 Introduction

12.2 Network Pharmacology: Practical Guide

12.3 Ayurveda and Traditional Indian Medicine

12.4 Network Pharmacology in Herbal Remedies

12.5 Conclusion and Future Prospects

References

13 Rational Design of Natural Products for Drug Discovery

13.1 Introduction

13.2 Natural Products for the Development of New Drugs

13.3 Criteria for Selecting Natural Products for Drug Design

13.4 Importance of Biodiversity in Sourcing Natural Products

13.5 Structural Elucidation of Natural Products

13.6

In Silico

Computational Tools for Rational Drug Discovery from Natural Sources

13.7 Formulation Challenges with Natural Products

13.8 Quality by Design (QbD) Approaches

13.9 Conclusion

References

14 Design of Enzyme Inhibitors in Drug Discovery

14.1 Introduction

14.2 Importance of Enzyme Inhibition as a Strategy for Modulating Enzyme Activity

14.3 Classification of Enzyme Inhibitors

14.4 Strategies Employed in the Design and Development of Enzyme Inhibitors

14.5 Limitations and Challenges

14.6 Future Directions

14.7 Conclusion

References

15 Rational Design of Peptides and Protein Molecules in Drug Discovery

15.1 Introduction

15.2 Peptides as Therapeutics

15.3 New Technologies for Peptide‐Based Drug Discovery

15.4 Computational Approaches in Peptide Drug Discovery

15.5 Conclusion

References

16 Rational Design of Drugs for Neurodegenerative Disorders

16.1 Introduction

16.2 Common Mechanism of Neurodegeneration

16.3 Brief Overview of Computational Methods in Drug Design

16.4 Parkinson’s Disease as Prevalent Neurodegenerative Disorder

16.5 Conclusion

References

17 Rational Design of Anti‐inflammatory Therapeutics

17.1 Introduction

17.2 Navigating Inflammation and its Microenvironment

17.3 The Demand for Advanced Anti‐inflammatory Medications

17.4 Natural Products Used for Anti‐inflammatory Drug Development: Systematic Approach in Use of Different Animal Models for Evaluations

17.5 Rational Design of Anti‐inflammatory Agents

17.6 Conclusion and Future Perspectives

Authors’ Contribution

References

18 Rational Design of Antibacterial Agents for Multidrug‐Resistant Infections

18.1 Introduction

18.2 Treatment

18.3 Antibacterial Resistance

18.4 Medicinal Chemistry Strategies for the Design of Antibacterials Combating Multidrug‐Resistant Bacterial Infections

18.5 Summary and Conclusion

References

19 Rational Design of Antiviral Therapeutics

19.1 Introduction to Antiviral Therapeutics

19.2 Targets for Antiviral Therapeutics and Inhibition Strategies

19.3 Rational Strategies for Antiviral Therapeutics

19.4 Conclusion

References

20 Rational Design of Anticancer Therapeutics

20.1 Introduction

20.2 Rational Design of Nanomedicine for Cancer Treatment

20.3 The CAPIR Cascade: A Nanomedicine Strategy for Administering Cancer Medications

20.4 Rational Regulation of Nanoparticle’s Physicochemical Characteristics

20.5 Some Approaches of Rational Drug Design in Anticancer Theranostics

20.6 Artificial Intelligence’s Progress in Anticancer Drug Development

20.7 Conclusion

References

21 PROTAC and ProTide Strategies in Drug Design

21.1 Introduction

21.2 Drug Design: Past to Present

21.3 PROTAC Strategy in Drug Design

21.4 Emergence of ProTide Technology in Drug Design

21.5 Approaches of ProTides in Drug Development

21.6 Implementation of ProTides as Nucleoside Analogs

21.7 Conclusion

References

22 Advancing Lung Cancer Treatment Through ALK Receptor‐targeted Drug Metabolism and Pharmacokinetics

22.1 Introduction

22.2 ALK Receptor and Its Role

22.3 Diagnostic Methods for ALK Rearranged NSCLC

22.4 ALK Inhibitors Drug Development

22.5 Drug Metabolism of Reported ALK Inhibitor

22.6 Resistance and Mutations

22.7 Conclusion

Conflict of Interest

References

23 Targeting Intrinsically Disordered Proteins (IDPs) in Drug Discovery

23.1 Introduction

23.2 Properties and Significance of IDPs

23.3 Challenges in Targeting IDPs

23.4 Computational Tools for IDP Analysis

23.5 Rational Design Approaches for IDP Inhibition

23.6 Case Studies

23.7 Future Directions

23.8 Conclusions

References

Index

End User License Agreement

List of Tables

Chapter 2

Table 2.1 Structure‐based methods for drug discovery.

Table 2.2 Essential ligand‐based methods for drug discovery.

Table 2.3 Network‐based methods for bioactive small molecules.

Table 2.4 Pharmacological effects of bioactive compounds.

Table 2.5 Summary of selected DFT functionals used in molecular modeling st...

Table 2.6 Widely used quantum chemical programs.

Table 2.7 Common factors that typically affect the choice of bioactive mole...

Table 2.8 A concise overview of the factors influencing the selection of bi...

Chapter 3

Table 3.1 Small molecule drugs targeting novel drug targets.

Table 3.2 Structure of small molecules.

Chapter 4

Table 4.1 List of associated tools for the target structure generation.

Table 4.2 List of associated tools for the active site prediction.

Table 4.3 List of available tools and databases for the docking analysis.

Table 4.4 List of available tools and databases for the MD simulation analy...

Table 4.5 List of tools and databases available in the case of LBDD.

Table 4.6  List of successful structure‐based assisted studies.

Table 4.7 List of successful ligand‐based assisted studies.

Chapter 6

Table 6.1 Programs based on fragmental methods.

Table 6.2 Programs based on atomic contributions.

Table 6.3 Solubility of Ritonavir in ethanol/water (mg/mL).

Chapter 7

Table 7.1 Protein target databases.

Chapter 8

Table 8.1 Programs and servers used in pharmacophore modeling.

Chapter 10

Table 10.1 Some common structure databases.

Table 10.2 Required and desired features of drug orally bioavailable.

Table 10.3 Some NP databases.

Chapter 11

Table 11.1 Benefits and challenges of artificial intelligence and machine l...

Chapter 12

Table 12.1 Data retrieval and analysis of network pharmacology data is faci...

Table 12.2 The table includes feature comparison tables from several databa...

Table 12.3 This table represents the features of Cytoscape StringApp with i...

Table 12.4 This table lists the many ayurvedic formulations, along with the...

Table 12.5 The several classes of phytoconstituents were listed in a table ...

Chapter 15

Table 15.1 FDA‐approved peptide drugs 2000–2022.

Table 15.2 Peptide‐based drugs in clinical trials.

Table 15.3 Chemical ligation techniques for peptide synthesis.

Table 15.4 Peptidomimetics for stabilization of β‐sheet/β strands in PPIs....

Table 15.5 rDNA‐derived therapeutic peptides.

Table 15.6 Online tools for CPP designing.

Table 15.7 Molecular modeling tools for peptide–protein docking.

Table 15.8 Online tools for AMP designing.

Chapter 16

Table 16.1 Current treatment strategies for Parkinson’s disease.

Table 16.2 Side effects of drugs used to treat motor symptoms.

Chapter 17

Table 17.1 Methods for evaluating anti‐inflammatory activity in animal mode...

Chapter 19

Table 19.1 Target inhibitors of viral structure.

Table 19.2 Antiviral therapeutics based on various drug discovery pipelines...

Chapter 21

Table 21.1 Example of ProTides that are already approved or are in a clinic...

Chapter 22

Table 22.1 The list of the pharmacokinetic parameters for ALK inhibitors....

Table 22.2 Resistance and mutations reported of the ALK TKIs.

Chapter 23

Table 23.1 List of intrinsically disordered proteins and their role in cell...

Table 23.2 Computational approaches and tools for IDP analysis.

List of Illustrations

Chapter 1

Figure 1.1 Different molecular models of carbon dioxide (CO

2

). (a) Ball and ...

Figure 1.2 AI in drug development.

Chapter 3

Figure 3.1 Canonical steps in drug target identification.

Chapter 4

Figure 4.1 Illustration of basic concepts that are involved in the SBDD.

Figure 4.2 Illustration of basic concepts that are involved in the LBDD.

Chapter 5

Figure 5.1 Depicting the drug discovery process.

Figure 5.2 Workflow for the virtual screening approaches in the drug discove...

Figure 5.3 Workflow for the docking in drug discovery process.

Figure 5.4 Workflow for the QSAR in the drug discovery process.

Figure 5.5 Refined workflow for employing virtual screening approaches in th...

Figure 5.6 Workflow for the discovery of mutant‐specific PI3K inhibitors bas...

Figure 5.7 Workflow for the discovery of selective RXR ligands based on an S...

Chapter 6

Figure 6.1 Biopharmaceutical classification system (BCS) of drugs.

Figure 6.2 Impact of substitution on a molecule’s log 

P

value.

Figure 6.3 Acid and base equations.

Figure 6.4 Acidic and basic groups of ciprofloxacin.

Figure 6.5 The impact of pH on weak bases and acids.

Figure 6.6 Chemical change of ciprofloxacin depending on pH.

Figure 6.7 Factors affecting solubility.

Figure 6.8 Olmesartan’s conversion from prodrug to active medication.

Chapter 7

Figure 7.1 An overview of target identification strategies.

Figure 7.2 Classification of computer‐aided drug design (CADD) techniques an...

Chapter 8

Figure 8.1 Flowchart illustrating the procedure of computational drug design...

Figure 8.2 An overarching procedure for partitioning the data into many data...

Figure 8.3 Pharmacophore models delineating inhibitors targeting cyclin‐depe...

Figure 8.4 (a) The pharmacophore model of XIAP protein bound to the 46781908...

Figure 8.6 A receiver operating characteristic (ROC) curve was generated to ...

Figure 8.7 A receiver operating characteristic (ROC) curve was generated to ...

Figure 8.8 Erlotinib (a) and axitinib (b) are superimposed onto the selected...

Chapter 9

Figure 9.1 Illustration of scaffold hopping.

Figure 9.2 Basic principle of

de novo

drug design.

Figure 9.3 Pictorial representation of overall classification of

de novo

dru...

Figure 9.4

De novo

drug‐design approach schematic illustration.

Figure 9.5 The basic difference between

de novo

drug design and scaffold hop...

Figure 9.6 Structure‐based

de novo

drug design.

Figure 9.7 Reference structures for ACE inhibitors.

Figure 9.8 Reference structures for angiotensin‐II receptor antagonists.

Figure 9.9 Reference structures for Aldose reductase inhibitors.

Figure 9.10 Reference structures for ACE inhibitors.

Figure 9.11 Reference structures for aldose reductase inhibitors.

Figure 9.12 Reference structures for ACE inhibitors.

Figure 9.13 Reference structures for ACE inhibitors.

Chapter 10

Figure 10.1 The FBDD process, exemplified on vemurafenib. The fragment 7‐aza...

Figure 10.2 (a) Fragment evolution. (b) Fragment linking. (c) Fragment self‐...

Figure 10.3 Examples of fragment hit scaffolds present among antimalarial HT...

Chapter 11

Figure 11.1 Drug design process with artificial intelligence and machine lea...

Figure 11.2 Methods and techniques used by artificial intelligence and machi...

Chapter 12

Figure 12.1 The representation of different drug applications discovery in v...

Figure 12.2 Various applications of network pharmacology, in contrast to tra...

Figure 12.3 Compression between one target and multitarget approach to treat...

Figure 12.4 IMPPAT database features and various features of the database is...

Figure 12.5 This image demonstrates the visualization of molecular clouds. W...

Figure 12.6 Developing the current

in silico

methods by integrating it with ...

Chapter 13

Figure 13.1 Components of the process of development of new drugs from natur...

Figure 13.2 Two approaches used by virtual screening and molecular docking....

Figure 13.3 QbD in rational product development.

Chapter 14

Figure 14.1 Competitive inhibition of the enzyme.

Figure 14.2 Strategies involved in structure‐based design.

Figure 14.3 Process involved in computer‐aided design.

Figure 14.4 Fragment‐based design.

Figure 14.5 Phases of virtual screening method.

Figure 14.6 Natural product‐based discovery.

Figure 14.7 Enzyme‐templated dynamic combinatorial chemistry.

Chapter 15

Figure 15.1 Models displaying the mechanism of membrane disruption by peptid...

Figure 15.2 Peptide‐based cancer theranostics. (A) Peptide vaccine; (B) nano...

Figure 15.3 Peptide diversification strategies.

Figure 15.4 Strategies for peptide cyclization.

Figure 15.5 Biopanning and phage display method for peptide expression.

Chapter 16

Figure 16.1 Different computational approaches commonly used in drug design ...

Figure 16.2 Pathogenesis of Parkinson’s disease.

Figure 16.3 Enzymatic targets for Parkinson’s disease and their mechanism.

Chapter 17

Figure 17.1 Inflammation and environment of inflammation with acute and chro...

Chapter 18

Figure 18.1 Multidrug resistance mechanisms.

Figure 18.2 Biotransformation of prodrug at the site of action.

Chapter 19

Figure 19.1 Molecular knockouts of viruses.

Figure 19.2 Rational pipeline for the development of antiviral therapeutics....

Chapter 20

Figure 20.1 A 5‐step CAPIR cascade of nanomedicines.

Figure 20.2 Application of AI in anticancer drug design.

Chapter 21

Figure 21.1 A systematic diagram stating the mechanism of action of PROTACs....

Chapter 22

Figure 22.1 Structure of the anaplastic lymphoma kinase (ALK) gene.

Figure 22.2 Mechanism of EML4‐ALK rearrangement.

Figure 22.3 ALK timeline development process.

Chapter 23

Figure 23.1 Intrinsically disordered proteins drug discovery approaches.

Guide

Cover Page

Table of Contents

Title Page

Copyright Page

List of Contributors

Preface

Begin Reading

Index

WILEY END USER LICENSE AGREEMENT

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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

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Library of Congress Cataloging‐in‐Publication DataNames: 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, MolecularClassification: LCC RM301.25 (print) | LCC RM301.25 (ebook) | NLM QV 745 | DDC 615.1/9–dc23/eng/20241009LC record available at https://lccn.loc.gov/2024045117LC ebook record available at https://lccn.loc.gov/2024045118

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List of Contributors

Raghu Ram AcharDivision of BiochemistrySchool of Life SciencesJSS Academy of Higher Education & ResearchMysuru, KarnatakaIndiaDepartment of BiotechnologyJSS Science and Technology UniversityMysuru, KarnatakaIndia

Richie Rashmin BhandareDepartment of Pharmaceutical SciencesCollege of Pharmacy and Health SciencesAjman University, AjmanUnited Arab EmiratesCentre of Medical and Bio‐allied HealthSciences ResearchAjman University, AjmanUnited Arab Emirates

Abanish BiswasDepartment of Pharmaceutical Sciencesand TechnologyBirla Institute of TechnologyRanchi, JharkhandIndia

Diptanil BiswasDepartment of Statistical GenomicsNational Institute of Biomedical GenomicsKalyani, West BengalIndia

Ulviye Acar ÇevikDepartment of Pharmaceutical ChemistryFaculty of PharmacyAnadolu University, EskişehirTurkey

Debarupa Dutta ChakrabortyRoyal School of PharmacyThe Assam Royal Global UniversityGuwahati, AssamIndia

Prithviraj ChakrabortyRoyal School of PharmacyThe Assam Royal Global UniversityGuwahati, AssamIndia

Shrimanti ChakrabortyDepartment of Pharmaceutical Sciencesand TechnologyBirla Institute of TechnologyRanchi, JharkhandIndia

Soumi ChakrabortyDepartment of Pharmaceutical Sciencesand TechnologyBirla Institute of TechnologyRanchi, JharkhandIndia

N. ChandanaDivision of Biochemistry, School of LifeSciencesJSS Academy of Higher Education & ResearchMysuru, KarnatakaIndiaDepartment of BiotechnologyJSS Science and Technology UniversityMysuru, KarnatakaIndia

Tabsum ChhetriDepartment of BioinformaticsUniversity of North BengalDarjeeling, West BengalIndia

Sohan S. ChitlangeDepartment of Pharmaceutical ChemistryDr. D. Y. Patil Institute of PharmaceuticalSciences and ResearchPune, MaharashtraIndia

Jay Mukesh ChudasamaDepartment of PharmacySumandeep Vidyapeeth Deemed to beUniversityVadodara, GujaratIndia

Bhrigu Kumar DasPharmacology and Toxicology LaboratorySchool of Pharmaceutical SciencesGirijananda Chowdhury University, GuwahatiIndia

Riddhi DaveGujarat Arts and Science CollegeAhmedabad, GujaratIndia

Shine DevarajanSchool of Biotechnology and BioinformaticsD Y Patil Deemed to be UniversityNavi Mumbai, MaharashtraIndia

Sneha DokhaleDepartment of BiotechnologyB. K. Birla College of Arts, Science &CommerceKalyan, MaharashtraIndia

Samiksha GarseSchool of Biotechnology and BioinformaticsD Y Patil Deemed to be UniversityNavi Mumbai, MaharashtraIndia

John J. GeorrgeDepartment of BioinformaticsUniversity of North BengalDarjeeling, West BengalIndia

Abhirup GhoshSchool of Biosciences and TechnologyVellore Institute of TechnologyVellore, Tamil NaduIndia

Rahul GhoshDepartment of Pharmaceutical Sciences andTechnologyBirla Institute of TechnologyRanchi, JharkhandIndia

Ashis Kumar GoswamiDepartment of Pharmaceutical SciencesFaculty of Science and EngineeringDibrugarh UniversityAssamIndia

Shikha GoswamiDepartment of PharmacologyDelhi Pharmaceutical Sciences and ResearchUniversityNew DelhiIndia

Anmol GuptaDepartment of BiosciencesIntegral University, LucknowUttar Pradesh, India

Ayşen IşikDepartment of BiochemistryFaculty of Science, Selçuk University, KonyaTurkey

Vanesa JamesDepartment of Regulatory Affairs and QualityAssuranceLJ Institute of PharmacyAhmedabad, GujaratIndia

Risy Namratha JamullamudiDepartment of PharmacyKoneru Lakshmaiah Education FoundationVaddeswaram, APIndia

Priyanka KamariaDepartment of Pharmaceutical ChemistryKLE College of PharmacyBangaloreIndia

Koyel KarDepartment of Pharmaceutical ChemistryBCDA College of Pharmacy and TechnologyKolkata, West BengalIndia

Abdüllatif KarakayaDepartment of Pharmaceutical ChemistryFaculty of Pharmacy, Zonguldak Bulent EcevitUniversity, ZonguldakTurkey

Priya KashavDepartment of PharmacySumandeep Vidyapeeth Deemed to beUniversityVadodara, GujaratIndia

Ankita KashyapInstitute of PharmacyAssam Medical College and HospitalDibrugarhIndia

Monalisa KeshCentre for Digital HealthIndian Institute of Technology BombayMumbaiIndia

Kevser Kübra KırboğaFaculty of Engineering, BioengineeringDepartmentBilecik Seyh Edebali UniversityBilecikTürkiye

Shaunak KolhapureSchool of Biotechnology and BioinformaticsD Y Patil Deemed to be UniversityNavi Mumbai, MaharashtraIndia

Sathish Kumar KonidalaDepartment of Pharmaceutical SciencesSchool of Biotechnology and PharmaceuticalSciencesVignan’s Foundation for Science, Technologyand ResearchGuntur, APIndia

Nigam Jyoti MaitiDepartment of Pharmaceutical Sciences andTechnologyBirla Institute of TechnologyRanchi, JharkhandIndia

Manshi MishraDepartment of Pharmaceutical Sciences andTechnologyBirla Institute of TechnologyRanchi, JharkhandIndia

Saurav Kumar MishraDepartment of BioinformaticsUniversity of North BengalDarjeeling, West BengalIndia

Maitreyee MukherjeeDepartment of Pharmaceutical TechnologyNSHM Knowledge Campus – Group ofInstitutionsKolkata 700053,West BengalIndia

Podila NareshDepartment of Pharmaceutical SciencesSchool of Biotechnology and PharmaceuticalSciencesVignan’s Foundation for Science, Technologyand ResearchGuntur, APIndia

Chandan NayakSchool of Pharmaceutical Education andResearchBerhampur UniversityBerhampur, OdishaIndia

André M. OliveiraDepartment of Environment StudiesFederal Centre of Technological Education ofMinas GeraisContagem, Minas GeraisBrazil

Ipsa PadhyDepartment of Pharmaceutical ChemistrySchool of Pharmaceutical SciencesSiksha ‘O’ Anusandhan (Deemed to be University)Bhubaneswar, OdishaIndia

Ipsita PanigrahiDivision of BiochemistrySchool of Life SciencesJSS Academy of Higher Education & ResearchMysuru, KarnatakaIndiaDepartment of BiotechnologyJSS Science and Technology UniversityMysuru, KarnatakaIndia

Ghanshyam ParmarDepartment of PharmacySumandeep Vidyapeeth (Deemed to beUniversity)Vadodara, GujaratIndia

Vaishali PatelDepartment of PharmaceuticsLaxminarayandev College of PharmacyBharuch, GujaratIndia

Mukesh Kumar PatwaDepartment of MicrobiologyKing George Medical UniversityLucknow, Uttar PradeshIndia

Sathiaseelan PerumalDepartment of ChemistryBishop Heber CollegeTiruchirappalli, Tamil NaduIndia

Shantanu RajDepartment of Pharmaceutical Sciences andTechnologyBirla Institute of TechnologyRanchi, JharkhandIndia

Gourav RakshitDepartment of Pharmaceutical Sciences &TechnologyBirla Institute of TechnologyRanchi, JharkhandIndia

Sanket S. RathodDepartment of Pharmaceutical ChemistryBharati Vidyapeeth College of PharmacyKolhapur, MaharashtraIndia

Sharanya RoyDepartment of Pharmaceutical Sciences andTechnologyBirla Institute of TechnologyRanchi, JharkhandIndia

Sneha RoyDepartment of BioinformaticsUniversity of North BengalDarjeeling, West BengalIndia

Mithun RudrapalDepartment of Pharmaceutical SciencesSchool of Biotechnology and PharmaceuticalSciencesVignan’s Foundation for Science, Technology& ResearchGuntur, Andhra PradeshIndia

Biprajit SarkarDepartment of Pharmaceutical Sciences andTechnologyBirla Institute of TechnologyRanchi, JharkhandIndia

Anupam SarmaAdvanced Drug Delivery LaboratorySchool of Pharmaceutical SciencesGirijananda Chowdhury UniversityGuwahatiIndia

Ashish ShahDepartment of PharmacySumandeep Vidyapeeth Deemed to be UniversityVadodara, GujaratIndia

Afzal Basha ShaikDepartment of Pharmaceutical SciencesSchool of Biotechnology and PharmaceuticalSciencesVignan’s Foundation for Science, Technologyand ResearchGuntur, APIndia

Tripti SharmaDepartment of Pharmaceutical ChemistrySchool of Pharmaceutical SciencesSiksha ‘O’ Anusandhan (Deemed to be University)Bhubaneswar, Odisha, IndiaSchool of Pharmaceutical Sciences and ResearchChhatrapati Shivaji Maharaj UniversityNavi Mumbai, MaharashtraIndia

Sonali S. ShindeDepartment of Pharmaceutical ChemistryDr. D. Y. Patil Institute of PharmaceuticalSciences and ResearchPune, MaharashtraIndia

Irum SiddiquiIIRC‐1, Department of BioengineeringIntegral UniversityLucknow, Uttar PradeshIndia

Aditi SinghDivision of BiochemistrySchool of Life SciencesJSS Academy of Higher Education & ResearchMysuru, KarnatakaIndiaDepartment of BiotechnologyJSS Science and Technology UniversityMysuru, KarnatakaIndia

Kratika SinghDepartment of MicrobiologyKing George Medical UniversityLucknow, Uttar PradeshIndia

Nisha Kumari SinghDepartment of Pharmaceutical Sciences andTechnologyBirla Institute of TechnologyRanchi, JharkhandIndia

Urmila SinghDepartment of MicrobiologyKing George Medical UniversityLucknow, Uttar PradeshIndia

Ashapurna SinhaDepartment of BiosciencesIntegral UniversityLucknow, Uttar PradeshIndia

Kamma Harsha SriDepartment of Pharmaceutical SciencesVignan Pharmacy CollegeGuntur, APIndia

Shivananju Nanjunda SwamyDepartment of BiotechnologyJSS Science and Technology UniversityMysuru, KarnatakaIndia

Vaishnavi ThakurSchool of Biotechnology and BioinformaticsD Y Patil Deemed to be UniversityNavi Mumbai, MaharashtraIndia

Rajiv Kumar TonkDepartment of Pharmaceutical ChemistryDelhi Pharmaceutical Sciences and ResearchUniversityNew DelhiIndia

Sridhar VemulapalliDepartment of Pharmaceutical SciencesUniversity of Nebraska Medical CenterOmaha, NEUnited States

Vivek YadavDepartment of Pharmaceutical ChemistryDelhi Pharmaceutical Sciences and ResearchUniversityNew DelhiIndia

Neha ZachariahSwami Shraddhanand CollegeUniversity of Delhi, New DelhiIndia

Preface

The growing incidence of diseases has created a dire need for proper medications, which are readily available, potent and, cost‐effective. To meet up this demand, the discovery of novel drug molecules that would be clinically effective and safe is need of the hour. Traditionally, drug discovery requires many years of development and a huge expenditure. However, using modern computational approaches a drug molecule can be identified by investing less time at reduced cost of discovery. It facilitates scientists to find potent and safe therapeutic molecules in more rational way as compared to conventional approaches. In computational drug discovery, various tools, methods/techniques, and software are used in designing better drug molecules with predictive modeling and biophysical studies ranging from physicochemical/ADMET assessments, understanding the molecular mechanisms and toxicity screening. The computational approaches and methods in drug designing include structure‐based methods, ligand‐based methods, virtual screening, predictive analytics, informatics tools, artificial intelligence (AI) approaches, machine learning methods, and multi‐database methods.

This book mainly delves into recent advances in drug designing tools and techniques and their applications pertaining to the discovery of novel therapeutic molecules. It includes armamentarium of latest approaches and advanced methodologies available for drug design and their practical applications in diverse cutting‐edge therapeutic areas including cancer, multidrug‐resistant bacterial infections, neurodegenerative disorders, inflammatory diseases, and viral infections. It comprises twenty‐three chapters in unique topics contributed from experts all around the globe. Some of the key features of the book are as follows:

Computer‐assisted methods and tools for structure‐ and ligand‐based drug design, virtual screening and lead discovery, artificial intelligence and machine learning approaches for drug design and ADMET and physicochemical assessments

In silico

and pharmacophore modeling, fragment‐based design,

de novo

drug design and scaffold hopping, network‐based methods and drug discovery

Rational design of natural products, peptides, enzyme inhibitors, drugs for neurodegenerative disorders, anti‐inflammatory therapeutics, antibacterials for multidrug‐resistant infections, and antiviral and anticancer therapeutics

The content of the book has been crafted in such a way that it would serve as useful resource materials for postgraduate and doctoral courses in multidisciplinary academic and/or research disciplines such as Pharmaceutical Sciences, Medicinal Chemistry, Pharmacology, Biomedical Sciences, Biochemistry, Microbiology, Biotechnology, Drug Discovery, Computational Drug Design, and Allied Sciences. This book will be particularly useful to drug developers, pharmaceutical scientists (R&D), discovery scientists, biomedical scientists, healthcare professionals, biochemists, medicinal chemists, pharmacologists, research students, professors, and other researchers working in the field of drug design and discovery. In addition, scientists involved in diverse areas of medicinal chemistry, molecular modeling, drug design and discovery, computational drug discovery (CADD), in silico drug discovery. and related areas are also expected to be the wider audience, users, or readers of this book, regardless whether they are engaged in basic (chemistry, computational chemistry, biophysical techniques, molecular modeling, and drug design) or applied (such as pharmaceutical, medicinal chemistry, and drug discovery) research.

I extend my heartfelt gratitude to the contributors and publication team of Wiley for their invaluable contributions and tireless efforts, supports, and dedication, which have enabled the successful compilation of the book volume.

October, 2024

Mithun RudrapalGuntur, India

1Molecular Modeling and Drug Design

Monalisa Kesh1, Abhirup Ghosh2, and Diptanil Biswas3

1 Centre for Digital Health, Indian Institute of Technology Bombay, Mumbai, India

2 School of Biosciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India

3 Department of Statistical Genomics, National Institute of Biomedical Genomics, Kalyani, West Bengal, India

1.1 Introduction

1.1.1 What Is Molecular Modeling?

Molecular modeling is at the cutting edge of scientific innovation and discovery, providing a paradigm‐shifting method for comprehending the microscopic world of atoms and molecules. Researchers in various disciplines, including chemistry, biology, materials science, and pharmacology, are given newfound strength by this computational technique that enables them to precisely model, examine, and forecast the behavior of molecules and molecular systems. At its core, molecular modeling uses computers’ computational capacity to reveal the nanoscale world’s well‐kept secrets, providing knowledge that is essential for expanding our understanding of the natural world and fostering technological advancements [1]. The attraction of molecular modeling is its capacity to connect theory and experiment. It offers a comprehensive understanding to the researchers to examine the dynamics of large molecular assemblies, the creation of chemical bonds, and the delicate description of atoms. Researchers can now examine issues that are frequently difficult, expensive, or even impractical to solve using only conventional experimental methods but much easier by this computational playground [2].

The significance of molecular modeling is most notable in drug development, where it has completely changed how pharmaceutical molecules are created and optimized. Researchers can quickly screen and prioritize prospective treatments by modeling the interactions between drug candidates and their target proteins, considerably speeding up the drug development process. This has contributed to personalized medicine, in which treatments are matched to specific genetic profiles as well as the development of novel therapeutics. The design of novel materials with specific features is aided in materials research by molecular modeling. Computational modeling directs the development of materials for various applications, from electronics to aerospace, whether it is optimizing the structure of innovative polymers, investigating the behavior of sophisticated composites, or comprehending the properties of nanomaterials [3, 4].

Beyond these areas, molecular modeling provides a flexible tool for understanding chemical processes, researching the principles of protein folding, and evaluating environmental effects. It is essential to education because it aids in the visualization of intricate molecular relationships and structures, which deepens comprehension of the underlying ideas that control the natural world. By enabling researchers to solve the riddles of molecules and molecular systems, molecular modeling acts as a light of scientific discovery in this era of computational inquiry, advancing us to new horizons of knowledge and innovation [5].

1.1.2 Software Used for Molecular Modeling

In the discipline of molecular modeling, software is crucial because it allows researchers to carry out intricate simulations, see molecular structures, and quickly process data. There are numerous software programs accessible, each suited to particular modeling methodologies and study goals [6]. These famous applications are frequently used for molecular modeling.

1.1.2.1 Schrodinger

For molecular modeling and drug development, Schrodinger provides a complete range of software tools. A user‐friendly interface is offered by Maestro, one of its flagship products, for various modeling activities, including molecular dynamics (MD) simulations, virtual screening, and structure‐based drug creation [7].

1.1.2.2 GROMACS

GROMACS is a potent simulation tool for MD that is typically employed to examine the behavior of biomolecules like proteins and lipids. It is a well‐liked option in both academia and business because of its speed and scalability [8].

1.1.2.3 Amber

Amber is a second extensively used program for modeling MD, with a significant emphasis on biomolecular systems. It is appropriate for various research applications since it has tools for modeling proteins, nucleic acids, and tiny compounds [9].

1.1.2.4 CHARMM

Known for its prowess in simulating intricate biomolecular systems and researching protein–ligand interactions, CHARMM (Chemistry at HARvard Molecular Mechanics) is a leading name in the field. Drug discovery and structural biology both make substantial use of it [10].

1.1.2.5 AutoDock

A well‐liked molecular docking program, AutoDock, forecasts how tiny compounds will interact with protein receptors. It is useful for identifying prospective medication candidates during virtual screening [11].

1.1.2.6 VMD

The versatile molecular visualization program VMD (visual molecular dynamics) is used to examine and display molecular structures, trajectories, and data from numerous simulation programs. It is especially helpful for producing gorgeous molecular graphics [12].

1.1.2.7 PyMOL

PyMOL is a popular molecular visualization program that provides an easy‐to‐use interface for developing professional‐grade 3D molecular images and animations. It is helpful for both academic and research endeavors [13].

1.1.2.8 Open Babel

A useful tool for data preparation and software package compatibility, Open Babel, is an open‐source chemical toolkit that enables users to convert between multiple chemical file formats [14].

1.1.2.9 Avogadro

Avogadro is a user‐friendly, open‐source tool for building and analyzing molecular structures. It is a molecular editor and visualization tool [15].

1.1.2.10 Discovery Studio

Developed by BIOVIA (formerly Accelrys), Discovery Studio is a complete set of molecular modeling and simulation tools used in materials science and drug development [16].

To conduct cutting‐edge molecular modeling studies, advance our understanding of molecular systems, and hasten discoveries in areas like drug development, materials science, and structural biology, these software tools are indispensable aids for researchers in various scientific disciplines. The unique study objectives, available computing power, and user skills all play a role in the software selection process.

1.1.3 Molecular Mechanics

Due to the invaluable insights of molecular mechanics into the interactions between medications and their biological targets, typically proteins, molecular mechanics plays a significant role in the development of new drugs. This computational method helps researchers identify and optimize prospective medication candidates by assisting in the study of the structural and energetic elements of molecular interactions. Here is how molecular mechanics assists in the search for new medicines [17].

1.1.3.1 Prediction of Binding Affinity

Molecular mechanics can calculate the binding affinity between a potential medication and its intended protein target. Researchers can forecast how tightly the medicine will bind to the target by estimating the potential energy of the drug–receptor combination. When choosing or creating molecules with higher affinity, which increases medicinal efficacy, this information is essential.

1.1.3.2 Conformational Analysis

Molecular mechanics that aids in the investigation of various conformations of a drug molecule may take up in the binding site of its target. This is important because proteins are dynamic structures, and knowing how a medicine interacts with different conformations might help formulators create more potent drugs.

1.1.3.3 Virtual Screening

To quickly assess the binding of hundreds of chemicals to a target protein, high‐throughput virtual screening uses molecular mechanics. To find possible drug candidates, virtual screening makes use of molecular mechanics simulations, which optimize molecular interactions for effective drug discovery. The prioritization of compounds with favorable binding energies speeds up the identification of prospective therapeutic candidates [18].

1.1.3.4 Lead Discovery

Molecular mechanics helps refine lead compounds during drug development. It aids in the decision‐making process for scientists when deciding whether chemical alterations will improve a drug’s binding affinity, selectivity, and overall pharmacological qualities [19].

1.1.3.5 Mechanism of Action

Computer simulations of molecular mechanics shed light on the ways in which medicines affect their intended protein targets. By modeling molecular interactions, molecular mechanics sheds light on drug processes and the finer points of a drug’s mode of action. Researchers can use this information to create medications with more on‐target effects and precise mechanisms of action. Absorption, distribution, metabolism, and excretion (ADME) and possible toxicity of drug candidates can also be predicted using molecular mechanics. Evaluation of a drug’s safety and bioavailability is aided by knowledge of how it interacts with cellular and physiological elements [20].

In drug development, molecular mechanics is a crucial tool for logically designing and perfecting therapeutic medicines. Reducing the number of prospective candidates saves time and money for researchers while increasing the likelihood that successful medications with focused effects and few adverse effects will be developed.

1.2 Types of Molecular Models

Molecular models are critical in clarifying complicated structures at the molecular level, assisting both scientists and students in picturing three‐dimensional (3D) atom configurations. Three important models stand out among the numerous types: the ball‐and‐spoke model, the space‐filling model, and the crystal lattice model (Figure 1.1).

The ball‐and‐spoke model represents atoms as spheres and bonds as sticks, simplifying chemical structures. This basic method explains connectedness and geometry. In contrast, the space‐filling model depicts atoms as spheres that occupy available space, displaying relative sizes and molecular packing. Crystal lattice models play an important role in crystallography because they depict the recurring atomic configurations in crystals while stressing symmetry and periodicity. These models, each with its own set of strengths, contribute to a full understanding of molecular structures by providing different viewpoints on the microscopic world [21, 22].

Figure 1.1 Different molecular models of carbon dioxide (CO2). (a) Ball and stick model, (b) space‐filling model, and (c) crystal lattice model.

1.2.1 Ball‐and‐Spoke Model

In molecular modeling, ball‐and‐spoke models are crucial visual representations that simplify the complex 3D structures of molecules for analysis and comprehension. This paradigm, which depicts atoms as spheres and bonds as sticks, has found widespread applicability in various scientific disciplines. The ball‐and‐spoke model, often known as the stick model, is a molecular depiction in which atoms are represented as spheres and the bonds that connect them as sticks. This straightforward approach visualizes molecular geometry in a practical and simple manner, assisting researchers in comprehending the spatial arrangement of atoms within a molecule [23].

Applications of this model:

Structural analysis

: The ball‐and‐spoke model is extensively used in structural elucidation because it allows researchers to easily determine bond angles, bond lengths, and overall molecular shapes.

Teaching tool

: The model is an effective teaching tool in educational settings, reducing complex chemical structures for pupils and facilitating a better understanding of key chemistry principles.

Communication

: The model is a universal language in scientific communication, commonly used to transmit molecular structures to varied audiences in research publications, presentations, and textbooks.

Pros of this model:

Simplicity

: The model’s simplicity makes it approachable to both academics and students, promoting a clear understanding of molecular geometry.

Intuition

: The visual depiction aids in the rapid understanding of molecular structures, allowing for instant insights into atom connectivity and arrangement.

Communication effectiveness

: The ball‐and‐spoke paradigm promotes clarity in research publications and presentations, making it an effective means of communication in the scientific community.

Cons of this model:

Oversimplification

: In some circumstances, the model may oversimplify molecular activity, ignoring nuances that more advanced models may capture.

Electron density representation is limited

: The model does not directly express information regarding electron density or orbital features, which can be critical for a thorough understanding of molecular properties [

23

,

24

].

1.2.1.1 Future Directions

As molecular modeling techniques advance, researchers are exploring ways to integrate the strengths of the ball‐and‐spoke model with more advanced computational methods, aiming for a more holistic representation of molecular structures.

In conclusion, the ball‐and‐spoke model remains a cornerstone in molecular modeling, offering a balance between simplicity and effectiveness. While it has its limitations, its widespread applications and continued use underscore its significance in the scientific community’s pursuit of understanding molecular complexities.

1.2.2 Space‐filling Models

Space‐filling models, often known as CPK models (after the initials of the creators Corey, Pauling, and Koltun), describe molecules by portraying atoms as spheres with radii proportional to the van der Waals radii of the respective elements. These models try to provide a realistic representation of molecular structures by demonstrating how atoms pack together efficiently in 3D space [25, 26].

Applications of this model:

Steric considerations

: Space‐filling models are very useful for comprehending steric hindrance and molecule packing. They explain how atoms inhabit space and impact the orientations of one another.

Drug design

: Space‐filling models in pharmaceutical research help in drug design by visualizing how compounds can interact within active areas of biological macromolecules, allowing researchers to optimize binding interactions.

Molecular size visualization

: The models excel in conveying the relative sizes of atoms within a molecule, allowing for a better understanding of molecular dimensions and overall structure.

Pros of this model:

Steric considerations

: Space‐filling models can help you understand steric hindrance and molecular packing. They describe how atoms inhabit space and influence one another’s orientations.

Drug design

: In pharmaceutical research, space‐filling models aid in drug design by visualizing how chemicals interact within active areas of biological macromolecules, allowing researchers to optimize binding interactions.

Visualization of molecular size

: The models excel in communicating the relative sizes of atoms within a molecule, allowing for a better comprehension of molecular dimensions and overall structure.

Cons of this model:

Complexity for large molecules

: Space‐filling models for large and complicated molecules may appear cluttered, making it difficult to detect individual characteristics.

Inadequate bond information

: Space‐filling models, unlike ball‐and‐spoke models, do not directly depict bond information, instead focus on atomic configurations

[27]

.

1.2.2.1 Future Directions

Advances in processing power and visualization techniques provide opportunities to improve space‐filling models. The incorporation of interactive and dynamic aspects may alleviate some of the difficulties involved with seeing complicated chemical structures.

Finally, space‐filling models provide a detailed and realistic description of molecular structures, revealing important information about molecular size, steric effects, and overall packing. Despite significant limitations, their applications in various scientific disciplines highlight their importance as indispensable tools for both molecular modelers and researchers.

1.2.3 Crystal Lattice Models

In molecular modeling, crystal lattice models describe the repeated arrangement of atoms within a crystal structure. These representations highlight crystals’ 3D periodicity, emphasizing the regular arrangement of atoms and molecules inside the lattice. These models help researchers grasp the relationship between microscopic arrangements and macroscopic features by offering a macroscopic picture of the crystalline structure [28].

Applications of this model:

Material science

: Crystal lattice models are important in materials science because they guide researchers in the discovery and design of novel materials. Predicting material qualities requires an understanding of the crystal structure.

Drug development

: Crystallography aids in the elucidation of the crystal structures of medicinal molecules in pharmaceutical research, providing critical information for optimizing medication formulation and increasing bioavailability.

Electronic properties

: In order to understand the electronic properties of semiconductors and other electronic materials, crystal lattice models are required [

29

,

30

].

Pros of this model:

Periodic insight

: Crystal lattice models depict the periodic arrangement of atoms, allowing researchers to identify patterns and symmetries within the crystal lattice.

Macroscopic understanding

: These models bridge the gap between microscopic and macroscopic scales, providing a comprehensive view of the crystal structure and assisting with experimental data interpretation.

Predictive capability

: Understanding the crystal lattice is critical for forecasting material properties and driving the creation of novel materials with specific functions

[31]

.

Cons of this model:

Oversimplification

: While crystal lattice models are excellent at depicting periodicity, they may oversimplify certain aspects of molecular activity, ignoring dynamic and nonperiodic processes.

Noncrystalline material complexity

: Because crystal lattice models are inherently intended for crystalline materials, their application for examining noncrystalline or amorphous structures is limited

[31]

.

1.2.3.1 Future Directions

Crystal lattice models are becoming more useful as computational tools and experimental procedures advance. Integrating real‐time simulations and dynamic elements could improve the accuracy and application of these models, allowing them to represent a wider range of materials.

Finally, crystal lattice models continue to be vital in molecular modeling, providing a powerful tool for understanding the periodicity and structure of crystalline materials. Their applications in materials science, drug development, and electronics highlight their value as valuable tools for researchers attempting to unravel the mysteries of molecular configurations in the solid state.

1.3 Computational Methods in Drug Discovery

1.3.1 What Is Drug Discovery?

By combining computational, mathematical, experimental, translational, and clinical models, possible novel medicinal entities can be identified through the process of drug development. It is the technique of locating and analyzing compounds that have the ability to govern illness in a secure way, with the aim of developing medications that can prolong patients’ lives. Drug development is still an arduous, expensive, time‐consuming, and ineffective process with a high dropout rate of novel therapeutic discovery, despite advancements in biotechnology and physiological system understanding. Knowing a biological target while employing creativity to find novel drugs can be referred to as drug designing. Creating molecules that complement the molecular target they interact and bind to in terms of charge and shape is the fundamental aspect of drug design. In the big data era, drug design typically, but not always, depends on computer modeling tools and bioinformatics methodologies [32].

Apart from small molecules, biopharmaceuticals, particularly therapeutic antibodies, have emerged as a significant class of medications [33]. Significant progress has also been made in computational methods for enhancing the stability, selectivity, and affinity of these protein‐based treatments. Preclinical research on animal and cell models as well as human clinical trials are all part of the process of developing and discovering new drugs. Afterward, the drug must receive regulatory approval before being put on the market. In contemporary drug discovery, screening hits are identified, medicinal chemistry is applied, and those hits are optimized to improve the hit’s affinity, specificity, efficacy, metabolic stability, and oral bioavailability. Before conducting clinical trials, a molecule that satisfies all of these criteria will be found, and the drug development process will start [34].

An illness or clinical condition for which there are not any appropriate pharmaceuticals on the market leads to the start of a drug discovery program, and this unmet clinical need serves as the project’s primary source of motivation. The preliminary study, which is frequently conducted in academic settings, produces information to support a hypothesis that a protein’s or pathway’s activation or inhibition will have a beneficial impact in the context of a medical condition. The result of this action is the identification of a target that, in order to support a drug development endeavor, may need additional validation before moving forward into the lead discovery phase. It is a drawn‐out, resource‐intensive procedure that calls for close collaboration among various disciplines. The pharmaceutical industry is extremely interested in optimizing the drug discovery process since identifying and selecting appropriate drug candidates promptly can have a significant impact on the price and profitability of new medications [35, 36].

1.3.2 Computational Platforms for Drug Discovery

In the field of drug development, computational platforms and algorithms are invaluable resources that have revolutionized the conventional and tedious approaches. These tools utilize artificial intelligence (AI), chemical simulations, and advanced algorithms to accelerate multiple phases of the drug development process. Computational models aid in the identification of pharmaceuticals with the best pharmacokinetic profiles by forecasting important pharmacological aspects including absorption, distribution, metabolism, excretion, and toxicity. By combining distinct datasets, these technologies not only expedite decision‐making processes but also promote collaboration. This leads to the simplification of drug development pipelines and a notable increase in the efficiency of generating innovative and effective medicines. Some most important computational tools and platforms for drug development are enumerated below. These tools aid in the analysis of enormous volumes of biological data, the prediction of drug–target interactions, and the optimization of drug candidates.

1.3.2.1 NCBI

The National Center for Biotechnology Information or NCBI, a division of the National Library of Medicine (NLM) of the National Institutes of Health (NIH), which was founded in 1988, is a comprehensive molecular biology information repository. It offers access to many biological databases, including PubMed, BLAST, GenBank, NCBI Gene, Genome Resources, and NCBI Bookshelf [37].

1.3.2.2 Chemical Databases

Comprehensive chemical data and compound databases are available through platforms such as PubChem and ChemSpider. Both databases provide a wealth of chemical data, but PubChem is especially useful for biological data, such as bioassays, because it is part of the larger NCBI resources. In contrast, ChemSpider places a strong emphasis on using a community‐driven methodology to create an extensive library of chemical structures. ChemSpider fosters a collaborative atmosphere by encouraging user contributions and the curation of chemical data. Community data entry is also permitted through PubChem. Chemical information can be accessed and contributed to by academics, researchers, and professionals working in the field of chemistry pharmacology and allied sectors using PubChem and ChemSpider. When used in tandem, they can offer a more thorough comprehension of chemical molecules and their biological functions [38, 39].

1.3.2.3 PDB

Protein Data Bank or PDB acts as the hub for 3D structural data of biological macromolecules and is a cornerstone of structural biology. The PDB is a massive collection of structures that have been determined through experimentation. It captures the complex architecture found in nucleic acids, proteins, and complex molecular organizations. These structures are explored by techniques such as crystallographic X‐rays, NMR spectroscopy, and cryo‐EM, providing crucial insights into the spatial arrangement of atoms within those biomolecules. Researchers can study, examine, and understand the structural nuances of biomolecules with the help of this publicly available resource. The PDB continues to be an essential tool in expanding our knowledge of the connections between structure and function, making a substantial contribution to areas like drug development, molecular biology, and bioinformatics. This is made possible by its cooperative efforts and interaction with other databases [40].

1.3.2.4 AutoDock, AutoDock Vina, DOCK, PatchDock, HADDOCK, SwissDock, Glide, Gold, FlexX, UCSF Chimera, and DockThor

These programs help uncover possible therapeutic candidates by predicting the binding modes of small compounds with the target proteins [41].

1.3.2.5 UniProt

The Universal Protein Resource, or UniProt, is a comprehensive and open‐access resource that offers details on the functional annotations of proteins. UniProt is a centralized platform that unifies data from many protein databases. It contains references to scholarly publications as well as data on post‐translational modifications, structural details, functional annotations, and protein sequences. It is a priceless tool for scientists studying molecular biology, computational biology, and related subjects. It provides a standardized, carefully curated repository that makes it easier to find and examine data about proteins. In order to guarantee that the database continues to be an extensive and trustworthy resource for the international scientific community, the UniProt Consortium is dedicated to continuously updating and growing it [42].

1.3.2.6 QSAR

Quantitative structure–activity relationship or QSAR uses a compound’s chemical structure to predict its biological activity. The foundation of QSAR techniques is the statistical correlation between target drug interactions and other molecular descriptors. The QSAR approach is based on the observation that compounds with similar structures typically exhibit comparable biological activities. These models provide a mathematical explanation of how the structural characteristics of a ligand affect the activity responses of a target that binds it. The association between various features of tiny ligand binders and biological activity acquired through experimentation is used to calculate QSAR. It is possible to forecast the activity of novel drug molecule analogs using QSAR relationships [43].

1.3.2.7 GROMACS, AMBER, NAMD, PLUMED, LAMMPS, CHARMM, GROMOS, OpenMM, Orac, XMD, YASARA, Ms2, MacroModel, and Avizo

These software packages replicate the motions of atoms and molecules over a period of time, providing an understanding of the dynamic behavior of biological systems [44].

1.3.2.8 Desmond

Specifically created for mimicking biomolecular systems. This cutting‐edge MD modeling program Desmond, created by DE Shaw Research, has been developed for examining the shifting dynamics of biological systems in great detail down to the atomic level. Desmond distinguishes out for its effectiveness and flexibility, making it especially appropriate for simulating large and complicated biomolecular systems over longer scales. Desmond facilitates the investigation of the conformation dynamics, interactions, and the thermodynamic laws of macromolecules including nucleic acids, proteins, and lipids by means of advanced algorithms and parallel processing capabilities. Advanced capabilities in the software, such as parallel processing and flexible integration time steps, enable faster and more accurate simulations. Desmond has contributed significantly to the advancement of our knowledge of biological processes, aiding in the search for new drugs, and offering insightful information on the structural dynamics underlying various physiological and pathological occurrences [45].

1.3.2.9 OpenBabel