This detailed book collects modern and established computer-based methods aimed at addressing the drug discovery challenge from disparate perspectives by exploiting information on ligand-protein recognition. Beginning with methods that allow for the exploration of specific areas of chemical space an...

Ausführliche Beschreibung

Bibliographische Detailangaben

Weitere Verfasser:	Ballante, Flavio (HerausgeberIn)
Format:	Buch
Sprache:	English
Veröffentlicht:	New York, NY, U.S.A. HumanaPress [2021]
Schriftenreihe:	Methods in molecular biology 2266
Schlagworte:	Pharmaceutical technology Chemoinformatics Chemistry Chemistry, Physical and theoretical Medicine Chemische Technik Aufsatzsammlung
Zusammenfassung:	This detailed book collects modern and established computer-based methods aimed at addressing the drug discovery challenge from disparate perspectives by exploiting information on ligand-protein recognition. Beginning with methods that allow for the exploration of specific areas of chemical space and the designing of virtual libraries, the volume continues with sections on methods based on docking, quantitative models, and molecular dynamics simulations, which are employed for ligand discovery or development, as well as methods exploiting an ensemble of protein structures for the identification of potential protein targets. Written for the highly successful Methods in Molecular Biology series, chapters include introductions to their respective topics, lists of the necessary materials, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Protein-Ligand Interactions and Drug Design provides detailed practical procedures of solid computer-aided drug design methodologies employed to rationalize and optimize protein-ligand interactions, for experienced researchers and novices alike
Beschreibung:	Part I: Chemical Space; ; 1. Investigation of the Click-Chemical Space for Drug Design Using ZINClick; Alberto Massarotti; ; 2. Molecular Scaffold Hopping via Holistic Molecular Representation; Francesca Grisoni and Gisbert Schneider; ; Part II: Hit Identification and Hit-to-Lead Optimization; ; 3. Biased Docking for Protein-Ligand Pose Prediction; Juan Pablo Arcon, Adrián G. Turjanski, Marcelo A. Martí, and Stefano Forli; ; 4. Binding Mode Prediction and Virtual Screening Applications by Covalent Docking; Andrea Scarpino, György G. Ferenczy, and György M. Keseru; ; 5. Ligand-Receptor, Ligand-DNA Interactions and Drug Design; Aggeliki Syriopoulou, Ioannis Markopoulos, Andreas G. Tzakos, and Thomas Mavromoustakos; ; 6. Simulation of Ligand Transport in Receptors Using CaverDock; Jana Hozzová, Ondrej Vávra, David Bednár, and Jirí Filipovic; ; 7.Negative Image-Based Screening: Rigid Docking Using Cavity Information; Pekka A. Postila, Sami T. Kurkinen, and Olli T. Pentikäinen; ; 8. Negative Image-Based Rescoring: Using Cavity Information to Improve Docking Screening; Olli T. Pentikäinen and Pekka A. Postila; ; 9. Fragment-Based Drug Design of Selective HDAC6 Inhibitors; Dusan Ruzic, Nemanja Djokovic, and Katarina Nikolic; ; 10. A Protocol to Use Comparative Binding Energy Analysis to Estimate Drug-Target Residence Time; Gaurav K. Ganotra, Ariane Nunes-Alves, and Rebecca C. Wade; ; 11. Dynamic Docking Using Multicanonical Molecular Dynamics: Simulating Complex Formation at the Atomistic Level; Gert-Jan Bekker and Narutoshi Kamiya; ; 12. Free Energy Calculations for Protein-Ligand Binding Prediction; Willem Jespers, Johan Åqvist, and Hugo Gutiérrez-de-Terán; - 13. Exploiting Water Dynamics for Pharmacophore Screening; David Schaller, Szymon Pach, Marcel Bermudez, and Gerhard Wolber; ; 14. Markov State Models to Elucidate Ligand Binding Mechanism; Yunhui Ge and Vincent A. Voelz; ; Part III: Target Identification; ; 15. From Homology Modeling to the Hit Identification and Drug Repurposing: A Structure-Based Approach in the Discovery of Novel Potential Anti-Obesity Compounds; Giosuè Costa, Anna Artese, Francesco Ortuso, and Stefano Alcaro; ; 16. Multiple Target Drug Design Using LigBuilder 3; Xiaoyu Qing, Shiwei Wang, Yaxia Yuan, Jianfeng Pei, and Luhua Lai; ; 17. Bionoi: A Voronoi Diagram-Based Representation of Ligand-Binding Sites in Proteins for Machine Learning Applications; Joseph Feinstein, Wentao Shi, J. Ramanujam, and Michal Brylinski; ; 18. MDock: A Suite for Molecular Inverse Docking and Target Prediction; Zhiwei Ma and Xiaoqin Zou
Beschreibung:	xv, 327 Seiten Illustrationen, Diagramme 254 mm
ISBN:	9781071612088