Applications Of Computational Tools In Drug Design And Development
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Computational Drug Discovery and Design
This second edition provides new and updated methods and techniques for identification of drug target, binding sites prediction, high- throughput virtual screening, lead discovery and optimization, conformational sampling, prediction of pharmacokinetic properties using computer-based methodologies. Chapters also focus on the application of the latest artificial intelligence technologies for computer aided drug discovery. Written in the format of the highly successful Methods in Molecular Biology series, each chapter includes an introduction to the topic, lists necessary methods, includes tips on troubleshooting and known pitfalls, and step-by-step, readily reproducible protocols.
Applications of Computational Tools in Drug Design and Development
Author: S.N. Koteswara Rao G.
language: en
Publisher: Springer Nature
Release Date: 2025-05-20
This book provides a comprehensive overview of the role of computers and computational tools at different stages of drug discovery and development. Designed to meet the needs of a beginner to advanced learner, the book provides the information on the tools, how they work, with the latest reports on applications in drug design, drug delivery and building network pharmacology models. Part I explores the pharmacological aspects, covering computational simulation of drug delivery at the molecular level, modeling for formulation design, and the revolutionary use of computational fluid dynamics in pharmaceutical processes. Specific applications such as pharmaceutical die filling processes, inhalation aerosol-based targeted drug delivery, and the development of inhalation compounds using in silico modeling tools are discussed. The use of computational tools in cheminformatics and their application in preformulation perspectives for drug delivery are also included. Part II expands the scope to include solubility prediction, absorption prediction, protein binding prediction, bio-permeability prediction, toxicity prediction, and metabolism prediction. It covers the identification of potential sites of metabolism in lead molecules and computer-assisted simulation studies to understand drug-polymer interactions. Recent advances in drug likeness screening using software and online tools are also reviewed. Part III focuses on specific therapeutic areas. The chapters examine the mechanistic understanding of anti-Alzheimer's agents, the design of novel antidiabetic agents, and the exploration of drug design for atherosclerosis. It also covers modern computational intelligence-based drug repurposing for cancer therapeutics, computational analyses of the mechanism of action of antiepileptic agents, and rational approaches for designing antihypertensive agents. The final chapters explore drug discovery and computational strategies in the context of multi-drug-resistant tuberculosis and the network pharmacology approach to uncover the pharmacological mechanisms of natural products. The book will be a useful reference for researchers, students and professionals in the field of life sciences, chemistry, pharmaceutics and bioinformatics.
Computer-Aided Drug Design
This book provides up-to-date information on bioinformatics tools for the discovery and development of new drug molecules. It discusses a range of computational applications, including three-dimensional modeling of protein structures, protein-ligand docking, and molecular dynamics simulation of protein-ligand complexes for identifying desirable drug candidates. It also explores computational approaches for identifying potential drug targets and for pharmacophore modeling. Moreover, it presents structure- and ligand-based drug design tools to optimize known drugs and guide the design of new molecules. The book also describes methods for identifying small-molecule binding pockets in proteins, and summarizes the databases used to explore the essential properties of drugs, drug-like small molecules and their targets. In addition, the book highlights various tools to predict the absorption, distribution, metabolism, excretion (ADME) and toxicity (T) of potential drug candidates. Lastly, it reviews in silico tools that can facilitate vaccine design and discusses their limitations.