Structure Based Design Of Combinatorial Libraries
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Combinatorial Library
Author: Lisa B. English
language: en
Publisher: Springer Science & Business Media
Release Date: 2008-02-04
The continued successes of large- and small-scale genome sequencing projects are increasing the number of genomic targets available for drug d- covery at an exponential rate. In addition, a better understanding of molecular mechanisms—such as apoptosis, signal transduction, telomere control of ch- mosomes, cytoskeletal development, modulation of stress-related proteins, and cell surface display of antigens by the major histocompatibility complex m- ecules—has improved the probability of identifying the most promising genomic targets to counteract disease. As a result, developing and optimizing lead candidates for these targets and rapidly moving them into clinical trials is now a critical juncture in pharmaceutical research. Recent advances in com- natorial library synthesis, purification, and analysis techniques are not only increasing the numbers of compounds that can be tested against each specific genomic target, but are also speeding and improving the overall processes of lead discovery and optimization. There are two main approaches to combinatorial library production: p- allel chemical synthesis and split-and-mix chemical synthesis. These approaches can utilize solid- or solution-based synthetic methods, alone or in combination, although the majority of combinatorial library synthesis is still done on solid support. In a parallel synthesis, all the products are assembled separately in their own reaction vessels or microtiter plates. The array of rows and columns enables researchers to organize the building blocks to be c- bined, and provides an easy way to identify compounds in a particular well.
Chemoinformatics
Author: Jürgen Bajorath
language: en
Publisher: Springer Science & Business Media
Release Date: 2008-02-04
In the literature, several terms are used synonymously to name the topic of this book: chem-, chemi-, or chemo-informatics. A widely recognized de- nition of this discipline is the one by Frank Brown from 1998 (1) who defined chemoinformatics as the combination of “all the information resources that a scientist needs to optimize the properties of a ligand to become a drug. ” In Brown’s definition, two aspects play a fundamentally important role: de- sion support by computational means and drug discovery, which distinguishes it from the term “chemical informatics” that was introduced at least ten years earlier and described as the application of information technology to ch- istry (not with a specific focus on drug discovery). In addition, there is of course “chemometrics,” which is generally understood as the application of statistical methods to chemical data and the derivation of relevant statistical models and descriptors (2). The pharmaceutical focus of many developments and efforts in this area—and the current popularity of gene-to-drug or si- lar paradigms—is further reflected by the recent introduction of such terms as “discovery informatics” (3), which takes into account that gaining kno- edge from chemical data alone is not sufficient to be ultimately successful in drug discovery. Such insights are well in accord with other views that the boundaries between bio- and chemoinformatics are fluid and that these d- ciplines should be closely combined or merged to significantly impact b- technology or pharmaceutical research (4).
Molecular Pathomechanisms and New Trends in Drug Research
Knowledge of the basic mechanisms of human disease is essential for any student or professional engaged in drug research and development. Functional gene analysis (genomics), protein analysis (proteomics), and other molecular biological techniques have made it possible to understand these cellular processes, opening up exciting opportunities for no