Computer Aided And Machine Learning Driven Drug Design
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Computer-Aided and Machine Learning-Driven Drug Design
Author: Vinícius Gonçalves Maltarollo
language: en
Publisher: Springer Nature
Release Date: 2025-02-27
The computer-aided drug design research field comprises several different knowledge areas, and often, researchers are only familiar or experienced with a small fraction of them. Indeed, pharmaceutical industries and large academic groups rely on a broad range of professionals, including chemists, biologists, pharmacists, and computer scientists. In this sense, it is difficult to be an expert in every single CADD approach. Furthermore, there are well-established methods that are constantly revisited, and novel approaches are introduced, such as machine-learning based scoring functions for molecular docking. This book provides an organized update of the most commonly employed CADD techniques, as well as successful examples of actual applications to develop bioactive compounds/drug candidates. Also includes is a section of case studies that cover certain pharmacological/target classes, focusing on the applications of the previously described methods. This part will especially appeal to professionals who are not as interested in the theoretical aspects of CADD. This is an ideal book for students, researchers, and industry professionals in the fields of pharmacy, chemistry, biology, bioinformatics, computer sciences, and medicine who are seeking a go-to reference on drug design and medicinal chemistry.
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.
Computer-Aided Drug Discovery Methods: A Brief Introduction
Author: Manos C. Vlasiou
language: en
Publisher: Bentham Science Publishers
Release Date: 2024-10-11
Computer-Aided Drug Discovery Methods: A Brief Introduction explores the cutting-edge field at the intersection of computational science and medicinal chemistry. This comprehensive volume navigates from foundational concepts to advanced methodologies, illuminating how computational tools accelerate the discovery of new therapeutics. Beginning with an overview of drug discovery principles, the book explains topics such as pharmacophore modeling, molecular dynamics simulations, and molecular docking. It discusses the application of density functional theory and the role of artificial intelligence in therapeutic development, showcasing successful case studies and innovations in COVID-19 research. Ideal for undergraduate and graduate students, as well as researchers in academia and industry, this book serves as a vital resource in understanding the complex landscape of modern drug discovery. It emphasizes the synergy between computational methods and experimental validation, shaping the future of pharmaceutical sciences toward more effective and targeted therapies.