In a relatively short period, Dynamic Combinatorial Chemistry (DCC) has grown from proof-of-concept experiments in a few isolated labs to a broad conceptual framework with applications to an exceptional range of problems in molecular recognition, lead compound identification, catalyst design, nanotechnology, polymer science, and others. Bringing together a group of respected experts, this overview explains how chemists can apply DCC and fragment-based library methods to lead generation for drug discovery and molecular recognition in bioorganic chemistry and materials science.
Approaches to binding in proteins and nucleic acids
Analytical chemistry challenges
A comprehensive, single-source reference about DCC methods and applications including aspects of fragment-based drug discovery, this is a core reference that will spark the development of new solutions and strategies for chemists building structure libraries and designing compounds and materials.
Chapter 1: Dynamic Combinatorial Chemistry: An Introduction (Benjamin L. Miller).
Chapter 2: Protein-Directed Dynamic Combinatorial Chemistry (Michael F. Greaney and Venugopal T. Bhat).
Chapter 3: Nucleic Acid-Targeted Dynamic Combinatorial Chemistry (Peter C. Gareiss and Benjamin L. Miller).
Chapter 4: Complex Self-Sorting Systems (Soumyadip Ghosh and Lyle Isaacs).
Chapter 5: Chiral Selection in DCC (Jennifer J. Becker and Michel R. Gagné).
Chapter 6: Dynamic Combinatorial Resolution (Marcus Angelin, Rikard Larsson, Pornrapee Vongvilai, Morakot Sakulsombat, and Olof Ramström).
Chapter 7: Dynamic Combinatorial Chemistry and Mass Spectrometry: A Combined Strategy for High Performance Lead Discovery (Sally-Ann Poulsen and Hoan Vu).
Chapter 8: Dynamic Combinatorial Methods in Materials Science (Takeshi Maeda, Hideyuki Otsuka, and Atsushi Takahara).