This contributed volume offers a comprehensive discussion on how to design and discover pharmaceuticals using computational modeling techniques. The different chapters deal with the classical and most advanced techniques, theories, protocols, databases, and tools employed in computer-aided drug design (CADD) covering diverse therapeutic classes. Multiple components of Structure-Based Drug Discovery (SBDD) along with its workflow and associated challenges are presented while potential leads for Alzheimer’s disease (AD), antiviral agents, anti-human immunodeficiency virus (HIV) drugs, and leads for Severe Fever with Thrombocytopenia Syndrome Virus (SFTSV) disease are discussed in detail. Computational toxicological aspects in drug design and discovery, screening adverse effects, and existing or future in silico tools are highlighted, while a novel in silico tool, RASAR, which can be a major technique for small to big datasets when not much experimental data are present, is presented. The book also introduces the reader to the major drug databases covering drug molecules, chemicals, therapeutic targets, metabolomics, and peptides, which are great resources for drug discovery employing drug repurposing, high throughput, and virtual screening. This volume is a great tool for graduates, researchers, academics, and industrial scientists working in the fields of cheminformatics, bioinformatics, computational biology, and chemistry.
Author(s): Supratik Kar, Jerzy Leszczynski
Series: Challenges and Advances in Computational Chemistry and Physics, 35
Publisher: Springer
Year: 2023
Language: English
Pages: 310
City: Cham
Preface
Contents
Contributors
1 SBDD and Its Challenges
1.1 Introduction
1.2 Overview on Structure-Based Drug Design (SBDD)
1.2.1 Protein Structure
1.2.2 Ligands
1.2.3 Molecular Docking Simulations
1.3 Crucial Components and Challenges in Computational SBDD
1.3.1 Target Structure Selection
1.3.2 Target and Ligand 3D Structure Preparation
1.3.3 Binding Affinity and Mode Prediction
1.3.4 Contribution of Water
1.3.5 Effect of Dynamics
1.4 Conclusion
References
2 In Silico Discovery of Class IIb HDAC Inhibitors: The State of Art
2.1 Introduction
2.2 Structural Biology of HDAC6
2.2.1 Insight into HDAC6 Crystal Structures
2.2.2 Insight into HDAC10 Crystal Structures
2.3 Different Tools of in Silico Drug Discovery and Its Applications
2.3.1 Design Strategies for HDAC6 Inhibitors
2.4 Design Strategies for HDAC 10 Inhibitors
2.5 Conclusion
References
3 Role of Computational Modeling in Drug Discovery for Alzheimer’s Disease
3.1 Introduction
3.1.1 The Cholinergic Hypothesis
3.1.2 The Amyloid Hypothesis
3.1.3 Tau Protein Hypothesis
3.2 Role of Computational Studies in the Designing of Anti-Alzheimer’s Agents
3.2.1 Tacrine-Based Scaffolds as Anti-AD Agents
3.2.2 Indole-Based Anti-AD Agents
3.2.3 Pyridine and Pyrimidine-Based Scaffolds as Anti-AD Agents
3.2.4 Quinoline-Based Scaffolds as Anti-AD Agents
3.2.5 Coumarin and Chromene-Based Scaffolds as Anti-AD Agents
3.2.6 Pyrazole-Based Scaffolds as Anti-AD Agents
3.2.7 Benzimidazole and Benzodiazepine Derivatives as Anti-AD Agents
3.2.8 Thiazole Containing Compounds as Anti-AD Agents
3.2.9 Alkylamine Linked Derivatives as Anti-AD Agents
3.3 Conclusion
References
4 Computational Modeling in the Development of Antiviral Agents
4.1 Introduction
4.2 Brief History and Structure of Viruses
4.3 Mechanism of Viral Infections
4.4 Computational Modeling in Viral Infections
4.4.1 Virtual Screening (VS)
4.4.2 Molecular Docking
4.4.3 Molecular Dynamics (MD)
4.5 Virus-Surface Proteins and Receptor Interaction
4.6 Antivirals Targeting Viral Surface Proteins
4.7 Applications of Computational Modeling in Antiviral Drug Discovery
4.8 Conclusion
References
5 Targeted Computational Approaches to Identify Potential Inhibitors for Nipah Virus
5.1 Introduction
5.2 Experimentally Tested Repurposed Drugs or Novel Molecules Against NiV
5.3 Computational Approaches for the Identification of Antiviral Drugs for NiV
5.4 Machine Learning and QSAR-Based Prediction Approach
5.5 Molecular Docking
5.6 Molecular Dynamics
5.7 Integrated Structure- and Network-Based Approach
5.8 Drug–Target–Drug Network-Based Approach
References
6 Role of Computational Modelling in Drug Discovery for HIV
6.1 Background
6.2 HIV Replication Cycle
6.3 The Resistance Problem
6.4 Structure-Based Methods
6.4.1 Molecular Docking
6.4.2 Molecular Dynamics and Free Energy Calculations
6.5 Quantitative Structure–Activity Relationships (QSARs)
6.6 Pharmacophore Modelling
6.7 The Emergence of Machine Learning in Drug Discovery for HIV
6.7.1 Multiple Linear Regression
6.7.2 Logistic Regression
6.7.3 Naïve Bayes
6.7.4 Support Vector Machines
6.7.5 Tree-Based Methods
6.7.6 Artificial Neural Networks
6.8 Conclusion
References
7 Recent Insight of the Emerging Severe Fever with Thrombocytopenia Syndrome Virus: Drug Discovery, Therapeutic Options, and Limitations
7.1 Introduction
7.2 Geographical Distribution and Its Genetic Diversity
7.3 Mechanism and Pathogenesis of SFTSV
7.4 Clinical Symptoms
7.5 Diagnosis
7.6 SFTS Therapeutic Options
7.7 Structure-Based Drug Design Approach Guided Identification of Potential Binders
7.8 Conclusion
References
8 Computational Toxicological Aspects in Drug Design and Discovery, Screening Adverse Effects
8.1 Introduction
8.2 Tools for Individual Endpoints
8.3 Tools for Read-Across
8.4 Weight-of-Evidence
8.5 Tools for Integrating Multiple Endpoints
8.6 Tools for Integrating Hazard and Exposure
8.7 Innovation and Caution in Safe-by-Design Drug Production
8.8 Tools for Building New Models
8.8.1 aiQSAR
8.8.2 DTC LAB Tools
8.8.3 SARpy
8.8.4 QSARpy
8.8.5 CORAL
8.8.6 SOM Tool
8.8.7 OCHEM
8.8.8 AMBIT
8.9 Conclusions
References
9 Read-Across and RASAR Tools from the DTC Laboratory
9.1 Introduction
9.2 The Theory Behind the Read-Across Approach
9.3 Read-Across Tool from the Drug Theoretics and Cheminformatics Laboratory
9.3.1 Pre-requisites for Using This Tool
9.3.2 Downloading and Execution of the Software
9.3.3 Analysis of the Output Files
9.3.4 Application of the Read-Across Tool Developed in the DTC Laboratory
9.4 Read-Across Structure–Activity Relationship—A Novel Concept
9.5 The RASAR Descriptor Calculator Tool from the DTC Laboratory
9.5.1 Pre-Requisites for Using This Tool
9.5.2 Downloading and Execution of the Tool
9.5.3 Analysis of the Output Files
9.5.4 Application of the RASAR Descriptor Calculator Tool Developed by the DTC Laboratory
9.6 Conclusion
References
10 Databases for Drug Discovery and Development
10.1 Introduction
10.2 Types of Databases for Drug Discovery
10.3 Databases
10.3.1 Chemical Molecules Database
10.3.2 Drug Molecules Database
10.3.3 Therapeutic Target Database
10.3.4 Peptide Database
10.3.5 Metabolomic Database
10.4 How to Select the Database for the Research?
10.5 Overview and Conclusion
References
Index
