Revolutionizing Drug Discovery: Cutting-Edge Computational Techniques

1st Edition, Volume 103 - April 3, 2025
Imprint: Academic Press
Editors: Swati Verma, Ajmer Singh Grewal, Chaitanay Vinayak Narayan, Neelam Singh, Hemlata Nimesh
Language: English
Hardback ISBN:
9 7 8 - 0 - 4 4 3 - 3 4 6 4 9 - 1
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 3 4 6 5 0 - 7

Revolutionizing Drug Discovery: Cutting-Edge Computational Techniques is an essential guide for professionals, researchers, and students in the pharmaceutical and biotech indust… Read more

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Revolutionizing Drug Discovery: Cutting-Edge Computational Techniques is an essential guide for professionals, researchers, and students in the pharmaceutical and biotech industries, providing an in-depth look at how computational methods transform drug development. This book explores advanced tools like molecular modeling, machine learning, and AI-driven design, which accelerate drug discovery, enhance target identification, and improve clinical outcome predictions. Through real-world applications and case studies, readers gain practical insights into the benefits of computational approaches in managing data, optimizing leads, and predicting drug efficacy and safety. Emphasizing a multidisciplinary approach, it bridges chemistry, biology, and informatics, addressing both the technical and ethical dimensions of these innovations. This book is a roadmap to the future of medicine, revealing how computational advancements are reshaping the landscape of drug discovery.

Cover image
Title page
Table of Contents
Copyright
Contributors
Preface
Chapter One: Innovative computational approaches in drug discovery and design
Abstract
1 Introduction
2 Artificial intelligence and machine learning
3 Molecular dynamics stimulation
4 Quantum mechanics/molecular mechanics
5 Structured based drug design
6 Docking studies
7 Big data
8 Chemical informatics and computational toxicology
9 Network pharmacology
10 Limitations
11 Conclusion and future perspectives
Acknowledgment
References
Chapter Two: Advanced molecular modeling of proteins: Methods, breakthroughs, and future prospects
Abstract
1 Introduction
2 Homology modeling
3 Protein structure prediction
4 Protein–ligand interactions
5 Breakthroughs
6 Future prospects
7 Conclusion
References
Chapter Three: Predictive cavity and binding site identification: Techniques and applications
Abstract
1 Introduction
2 Techniques for the identification and characterization of attachment sites
3 Methods used to detect cavities and binding sites
4 Multifaceted applications of identification of the cavities in drug discovery
5 Future of drug discovery and recent advancements
6 Conclusion
References
Chapter Four: ADMET tools in the digital era: Applications and limitations
Abstract
1 Introduction
2 ADMET prediction
3 Methodologies for ADMET model creation
4 Available ADMET predictive tools
5 Limitations of ADMET prediction models
6 Conclusion and future directions
Acknowledgment
References
Chapter Five: Essential database resources for modern drug discovery
Abstract
1 Introduction
2 Database resources
3 Conclusion
Acknowledgments
References
Chapter Six: Deep learning: A game changer in drug design and development
Abstract
1 Introduction
2 Traditional drug discovery process
3 Traditional drug discovery process and transformative potential of deep learning
4 Deep learning leading to enhance drug characteristics
5 Challenges and future directions
6 Conclusion
References
Chapter Seven: Molecular and structure-based drug design: From theory to practice
Abstract
1 Introduction
2 Fundamentals of structure-based drug design (SBDD)
3 Key techniques in structural biology
4 Molecular docking
5 Basic features of molecular docking
6 Types of molecular docking
7 Approaches employed in molecular docking
8 Computational tools and software
9 Virtual screening
10 Analysis of docking results
11 Major developments in docking
12 Conclusion
References
Chapter Eight: Molecular dynamics simulations: Insights into protein and protein ligand interactions
Abstract
1 Introduction
2 Methodologies and techniques
3 Applications in protein dynamics
4 Advanced topics and techniques
5 Current tools for MD simulations
6 Applications in drug discovery
7 Challenges and limitations
8 Future directions and perspectives
9 Conclusion
Acknowledgment
References
Chapter Nine: Targeting disease: Computational approaches for drug target identification
Abstract
1 Introduction
2 Data mining in drug discovery
3 Network pharmacology in drug discovery
4 Molecular docking
5 Uses of molecular docking
6 Molecular docking scoring methods
7 Analysis in molecular docking
8 Commonly used molecular docking software
9 Reverse docking
10 Scoring in reverse docking
11 Applications of computational approaches for drug target identification
12 Future perspectives
13 Conclusion
Acknowledgment
References
Chapter Ten: High-throughput computational screening for lead discovery and development
Abstract
1 Introduction
2 Conclusion
Acknowledgment
References
Chapter Eleven: Harnessing machine learning for rational drug design
Abstract
1 Introduction
2 Protein pocket identification and representation
3 Docking of protein-ligand in the era of machine learning
4 Using deep learning techniques in molecular representation learning
5 Challenges in traditional drug discovery
6 Machine learning’s functions in drug discovery
7 Drug discovery using machine learning method
8 Examples and case studies
9 Future directions and challenges
10 Conclusion
References
Chapter Twelve: Identifying novel drug targets with computational precision
Abstract
1 Introduction
2 Fundamentals of computational drug discovery
3 Genomic and proteomic approaches
4 In silico screening techniques
5 Structure-based drug design
6 Ligand based drug design
7 Machine learning and artificial intelligence in drug discovery
8 Systems biology and network pharmacology
9 Case studies in computational drug target identification
10 Challenges and future directions
11 Ethical and regulatory considerations
12 Conclusion
Acknowledgment
References
Further reading
Chapter Thirteen: Computational exploration of viral cell membrane structures for identifying novel therapeutic target
Abstract
1 Introduction
2 Comparative analysis
3 Computational approaches for drug target identification in Zika Virus, SARS-CoV-2, Nipah Virus, and HeV
4 Currently available treatment
5 Computationally predicted ligands and vaccine candidates for Zika, SARS-CoV-2, Nipah virus and HeV
6 Conclusion
Acknowledgment
References
Chapter Fourteen: The translational impact of bioinformatics on traditional wet lab techniques
Abstract
1 Introduction
2 Enhancing experimental design through bioinformatics
3 Enhancing efficiency and reducing costs through bioinformatics
4 Ensuring reproducibility, precision, and integration with wet lab procedures
5 Hypothesis generation and sophisticated data analysis in bioinformatics
6 Conclusion
References
Chapter Fifteen: Pharmacophore modeling in drug design
Abstract
1 Introduction
2 Pharmacophore fingerprints
3 Structure-based pharmacophore design
4 Ligand based pharmacophore design
5 Virtual screening
6 Molecular dynamics pharmacophore
7 Pharmacophore model validation
8 Conclusion
Acknowledgment
References
Chapter Sixteen: Emerging horizons of AI in pharmaceutical research
Abstract
1 Introduction
2 Conventional process of clinical drug development
3 Deficiencies in the clinical drug development process and the potential for AI enhancement
4 Comprehensive AI-based methodology for pharmaceutical testing
5 Automated AI in pharmaceutical research processes
6 Pharmacological research databases
7 Utilization of AI in drug discovery by industrial companies
8 AI-generated pharmacological drugs
9 Future trends and prospects
10 Conclusion
Acknowledgment
References
Chapter Seventeen: Integrative computational approaches in pharmaceuticals: Driving innovation in discovery and delivery
Abstract
1 Introduction
2 Ligand-based drug design (LBDD)
3 Structure-based drug design (SBDD)
4 Computational approaches
5 Big data and omics approaches
6 Case studies in drug discovery
7 Case studies in drug repurposing
8 Challenges and limitations in drug discovery
9 Computational approaches in drug delivery
10 Quality by design (QbD) implementation
11 Case studies in drug delivery
12 Computational approach in the quality assurance and control
13 Computational approach in the clinical trial design
14 Computational approach in the nanorobots for drug delivery
15 Computational approach in the distribution of combination drugs and prediction of synergism/antagonism
16 Computational approach in the lipid based DDS
17 Future directions
18 Conclusion
Acknowledgment
References
Chapter Eighteen: Innovations in vaccine design: Computational tools and techniques
Abstract
1 Introduction
2 Evolution of vaccine development
3 Advancements in bioinformatics for vaccine design
4 Designing vaccines based on protein structures
5 Utilizing structure-based vaccine design principles in recent times
6 Tools for analyzing and designing antibodies
7 Gene optimization for vaccine production
8 Using computational tools throughout the vaccine development process
9 Real-world examples: from computer to clinical trials
10 Conclusion
Acknowledgment
References
Chapter Nineteen: Real-world application of molecular docking in drug discovery
Abstract
1 Introduction
2 Overview of molecular docking
3 Mechanism of action
4 Tools and techniques in molecular docking
5 Real world applications of molecular docking
6 Conclusion
Acknowledgment
References
Chapter Twenty: Challenges and limitations of computer-aided drug design
Abstract
1 Introduction
2 Key challenges in CADD
3 Addressing issues in future directions
4 Conclusion
Acknowledgement
References
Chapter Twenty One: Future prospective of AI in drug discovery
Abstract
1 Introduction
2 Using AI to find targets
3 Virtual screening and modeling
4 Better drug design
5 AI and customized medicine
6 Uses of AI in clinical trials
7 Challenges and moral importance of AI
8 Future prospectives of AI
9 Conclusion
Acknowledgment
References

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Revolutionizing Drug Discovery: Cutting-Edge Computational Techniques

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