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Chapter 19 - Basic Principles and Practices of Computer-Aided Drug Design
- from Section Four - Chemical Genomics Assays and Screens
- Edited by Haian Fu, Emory University, Atlanta
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- Book:
- Chemical Genomics
- Published online:
- 05 June 2012
- Print publication:
- 13 February 2012, pp 259-278
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- Chapter
- Export citation
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Summary
Technological advances in pharmaceutical research during the past few decades have transformed drug discovery and development from empirical trial-and-error methods to development of mechanism-based compounds, often referred to as targeted therapy. In the targeted therapy approach, the scientific endeavor is to find drugs that can act on specific targets, the majority of which are proteins. Functions, or more typically dysfunctions, of these targeted cellular proteins typically underlie diseases. The therapeutic concept assumes that binding of drugs to the target proteins can alter the proteins’ function in the pathological states of cells. A favorable outcome of drug administration is to nullify or at least mitigate the disease. Development of a drug for treatment of a disease requires many resources as well as much time and collaborative effort of scientists from different disciplines, including chemistry, biology, and pharmacology. In the past decade, the computer has emerged as a powerful tool that can facilitate drug discovery and development. The initial step in the process calls for a correlation of the structures of known compounds with their activities in order to begin the search for new classes of molecules with the requisite activity. In recent years, more sophisticated computational molecular modeling methods have been developed and applied to the development of drugs, from initial discovery of hits and lead optimization to prediction of absorption, distribution, metabolism, and excretion (ADME) properties to toxicity (TOX) evaluation. In this chapter, we focus on the well-established computational methods applied to the identification and optimization of lead compounds. We first discuss the computational methodologies currently used in drug discovery. The strategies used for discovery and optimization of novel ligands by combining different computational tools are described next, and we then present case studies in which computational methods have been employed in drug design. We conclude by highlighting the current challenges and future perspectives of computer-aided drug design (CADD).