دانلود کتاب High-Throughput Screening in Drug Discovery, Volume 35

Backed by leading authorities, this is a professional guide to successful compound screening in pharmaceutical research and chemical biology, including the chemoinformatic tools needed for correct data evaluation. Chapter authors from leading pharmaceutical companies as well as from Harvard University discuss such factors as chemical genetics, binding, cell-based and biochemical assays, the efficient use of compound libraries and data mining using cell-based assay results. For both academics and professionals in the pharma and biotech industries working on small molecule screening.Content: Chapter 1 Chemical Genetics: Use of High?throughput Screening to Identify Small?molecule Modulators of Proteins Involved in Cellular Pathways with the Aim of Uncovering Protein Function (pages 1–13): Sa L. ChiangChapter 2 High?throughput Screening for Targeted Lead Discovery (pages 15–36): Jorg Huser, Emanuel Lohrmann, Bernd Kalthof, Nils Burkhardt, Ulf Bruggemeier and Martin BechemChapter 3 Tools and Technologies that Facilitate Automated Screening (pages 37–73): John ComleyChapter 4 Functional Cell?based Assays for Targeted Lead Discovery in High?throughput Screening (pages 75–91): Jorg Huser, Bernd Kalthof and Jochen StrayleChapter 5 Biochemical Assays for High?throughput Screening (pages 93–128): William D. Mallender, Michael Bembenek, Lawrence R. Dick, Michael Kuranda, Ping Li, Saurabh Menon, Eneida Pardo and Tom ParsonsChapter 6 Image?based High?content Screening – A View from Basic Sciences (pages 129–149): Peter Lipp and Lars KaestnerChapter 7 Methods for Statistical Analysis, Quality Assurance and Management of Primary High?throughput Screening Data (pages 151–205): Hanspeter GublerChapter 8 Chemoinformatic Tools for High?throughput Screening Data Analysis (pages 207–257): Peter G. Nell and Stefan M. MundtChapter 9 Combinatorial Chemistry and High?throughput Screening (pages 259–296): Roger A. Smith and Nils GriebenowChapter 10 High?throughput Screening and Data Analysis (pages 297–322): Jeremy S. Caldwell and Jeff Janes