Designing and developing new drugs is an expensive and time-consuming process, and there is a need to discover new tools or approaches that can optimize this process. Applied Computer-Aided Drug Design: Models and Methods compiles information about the main advances in computational tools for discovering new drugs in a simple and accessible language for academic students to early career researchers. The book aims to help readers understand how to discover molecules with therapeutic potential by bringing essential information about the subject into one volume.
Key Features
- Presents the concepts and evolution of classical techniques, up to the use of modern methods based on computational chemistry in accessible format.
- Gives a primer on structure- and ligand-based drug design and their predictive capacity to discover new drugs.
- Explains theoretical fundamentals and applications of computer-aided drug design.
- Focuses on a range of applications of the computations tools, such as molecular docking; molecular dynamics simulations; homology modeling, pharmacophore modeling, quantitative structure-activity relationships (QSAR), density functional theory (DFT), fragment-based drug design (FBDD), and free energy perturbation (FEP).
- Includes scientific reference for advanced readers
Readership
Students, teachers and early career researchers.Table of Contents
- Contents
- Preface
- References
- List of Contributors
- Promising Approaches to Discover New Drugs
- Igor José Dos Santos Nascimento and Ricardo Olimpio De Moura
- Introduction
- Drug Design and Discovery: Past and Today Methods and Other
- Approaches
- Natural Compounds (Nc)
- Synthetic Drugs: Classical Approaches
- Bioisosterism
- Molecular Simplification
- Molecular Hybridization
- Combinatorial Chemistry
- High Throughput Screening (Hts)
- Target-Based Drug Discovery (Tbdd)
- Phenotypic-Based Drug Discovery (Pbdd)
- Multitarget Drug Design (Mdd)
- Computer-Aided Drug Design (Cadd)
- Sbdd and Lbdd Methods in Drug Design
- Structure-Based Drug Design (Sbdd)
- Homology Modeling
- Molecular Docking and Molecular Dynamics Simulations
- Fragment-Based Drug Design (Fbdd) or De Novo Drug Design
- Density Function Theory (Dft)
- Ligand-Based Drug Design (Lbdd)
- Quantitative Structure-Activity Relationship (Qsar)
- Pharmacophore Modeling
- Machine and Deep Learning and Artificial Methods
- Challenges and Opportunities in Lbdd and Sbdd Approaches To
- Design and Discover New Drugs
- Conclusion
- Acknowledgments
- References
- Studying the Biologically Active Molecules
- Serap Çetinkaya, Burak Tüzün and Emin Saripinar
- Introduction
- Qsar's Use
- Qsar Model Development
- 2D-Qsar Analysis
- Fragment-Based 2D-Qsar Methods
- 3D-Qsar
- 4D-Qsar
- 5D- and 6D-Qsars
- Molecular Modelling and Qsar
- Importance of the Validation of Qsar Models
- Means of Proof for Qsar Models
- Internal Validation
- External Validation
- Easily Reproducible Qsar Protocol
- Conclusion
- References
- Drug Design and Discovery
- Dharmraj V. Pathak, Abha Vyas, Sneha R. Sagar, Hardik G. Bhatt and Paresh K.
- Patel
- Introduction
- Definitions of Pharmacophore
- Pharmacophore: History
- Pharmacophoric Features
- Ligand Based Pharmacophore
- Ligand-Based Pharmacophore Modeling
- Selection of the Right Set of Compounds and Their Initial Structure
- Conformational Search
- Feature Representation and Extraction
- Pattern Identification/Molecular Alignment
- Scoring the Common Pharmacophore
- Pharmacophore Tools and Their Algorithms
- Pharmacophore Validation
- Cost Analysis
- Fisher’S Randomization Test
- Test Set Prediction
- Leave-One-Out Method
- 3D-Qsar
- Pharmacophore Based 3D Qsar
- Structure Based Pharmacophore
- Structure Based Pharmacophore Model Generation
- Active Site Identification
- Complementary Image Construction
- Query Generation, Searching and Hit Analysis
- Validation
- Virtual Screening
- Prefiltering
- Application of Pharmacophore Mapping
- A Successful Example of Pharmacophore-Based Drug Design: An Example of How
- Anthranilamide Derivatives Were Successfully Shown to Be Promising Factor Xa Inhibitors
- [163]
- Applications of Artificial Intelligence in Pharmacophore Mapping
- Limitations of Pharmacophore Modeling
- Conclusion
- Acknowledgements
- References
- Muhammed Tilahun Muhammed and Esin Aki-Yalcin
- Introduction
- Brief History of Homology Modeling
- Homology Modeling Procedure
- Identification and Selection of Templates
- Sequence Alignments and Alignment Correction
- Model Building
- Loop Modeling
- Side-Chain Modeling
- Model Optimization
- Model Evaluation and Validation
- Overview of Homology Modeling Tools
- Modeller
- I-Tasser
- Swiss-Model
- Prime
- Phyre2
- Hhpred
- Rosettacm
- Alpha Fold
- Case Study
- Applications of Homology Modeling in Drug Discovery
- Conclusion
- References
- Molecular Docking Studies
- Serap Çetinkaya and Burak Tüzün
- Introduction
- Computer Aided Drug Design (Cadd)
- Ligand-Based Approach
- Structure (Receptor)-Based Approach
- Covalent Interactions in Biological Systems
- Molecular Docking: Non-Covalent and Covalent Docking
- Docking Methods in Software
- Fixed Docking
- Flexible-Fixed Docking
- Flexible Docking
- Types of Docking Calculations Algorithms
- Stepwise Structure Algorithm
- Monte Carlo Sampling Algorithm
- Genetic Algorithm
- Lamarckian Genetic Algorithm
- Biplane Space Sampling
- Shape Matching Algorithm
- Molecular Docking Software
- Artemisia Sieversiana
- Rosmarinus Officinalis
- Allium Sativum
- Zingiber Officinale
- Conclusion
- References
- Anwesha Das, Arijit Nandi, Vijeta Kumari and Mallika Alvala
Author
- Igor José dos Santos Nascimento
