Docking Screens for Drug Discovery

1. Auflage 2019
ISBN: 978-1-4939-9754-1
Verlag: Springer

Buch, Englisch, Band 2053, 286 Seiten, Format (B × H): 178 mm x 254 mm, Gewicht: 576 g

Reihe: Methods in Molecular Biology

This book focuses on recent developments in docking simulations for target proteins with chapters on specific techniques or applications for docking simulations, including the major docking programs. Additionally, the volume explores the scoring functions developed for the analysis of docking results and to predict ligand-binding affinity as well as the importance of docking simulations for the initial stages of drug discovery. Written for the highly successful Methods in Molecular Biology series, this collection presents the kind of detail and key implementation advice to ensure successful results. 
Authoritative and practical, Docking Screens for Drug Discovery aims to serve those interested in molecular docking simulation and also in the application of these methodologies for drug discovery.

Zielgruppe

Professional/practitioner

Autoren/Hrsg.

de Azevedo Jr., Walter Filgueira

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizinische Fachgebiete Pharmakologie, Toxikologie
• Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
• Naturwissenschaften Biowissenschaften Proteinforschung

Inhaltsverzeichnis

Building Machine-Learning Scoring Functions for Structure-Based Prediction of Intermolecular Binding Affinity.- Integrating Molecular Docking and Molecular Dynamics Simulations.- How Docking Programs Work.- SAnDReS: A Computational Tool for Docking.- Electrostatic Energy in Protein-Ligand Complexes.- Van der Waals Potential in Protein Complexes.- Hydrogen-Bonds in Protein-Ligand Complexes.- Molecular Dynamics Simulations with NAMD2.- Docking with AutoDock4.- Molegro Virtual Docker for Docking.- Docking with GemDock.- Docking with SwissDock.- Molecular Docking Simulations with ArgusLab.- Web Services for Molecular Docking Simulations.- Homology Modeling of Protein Targets with MODELLER.- Machine Learning to Predict Binding Affinity.- Exploring the Scoring Function Space.