Biological space combinatorial chemistry

While this may in fact be the case for natural product mixtures, it is rarely the case when dealing with synthesized mixtures. Despite our attempts to create real molecular diversity in the test tube, our efforts have not even begun to anticipate the true diversity of atomic connectivity within "drug space" (estimated to be of the order of 1063 unique compounds, theory, famously in this case, greatly outpacing the amount of matter in the universe). Thus, combinatorial chemistry was never practically able to produce true chemical diversity and compounds produced in such library format ended up looking very much like one another, with the attendant similarities in biological activity profiles. 

Although combinatorial chemistry and HTS have offered medicinal chemists a much broader range of possibilities for lead discovery and optimization, the number of chemical compounds that can be reasonably synthesized, which is sometimes called virtual chemistry space , is stiU far beyond today s capability of chemical synthesis and biological assay. Therefore, medicinal chemists continue to face the same problem as before which compounds should be chosen for the next round of synthesis and testing For chemoinformaticians, the task is to develop and utilize various computer programs to evaluate a very large number of chemical compounds and recommend the most promising ones for bench medicinal chemists. This process can be called virtual... 

One may think of an iterative model for the preclinical discovery screening cycle. A large number of compounds are to be mined for compounds that are active for example, that bind to a particular target. The compounds may come from different sources such as vendor catalogues, corporate collections, or combinatorial chemistry projects. In fact, the compounds need only to exist in a virtual sense, because in silico predictions in the form of a model can be made in a virtual screen (Section 8) which can then be used to decide which compounds should be physically made and tested. A mapping from the structure space of compounds to the descriptor space or property space provides covariates or explanatory variables that can be used to build predictive models. These models can help in the selection process, where a subset of available molecules is chosen for the biological screen. The experimental results of the biological screen (actives and inactives, or numeric potency values) are then used to learn more about the structure-activity relationship (SAR) which leads to new models and a new selection of compounds as the cycle renews. 

Figure 1 Representation of some of the interfaces between biology and chemistry space (a) the continuum of chemistry space continuum with representative regions of specific biological activity highlighted (b) large combinatorial libraries seek to cover as much space as possible across several biological families but with one core scaffold (c) smaller focused libraries, shown as the small blue cubes, are designed with relevant biological targets in mind and(d) libraries that do not overlap with relevant biological space are undesirable...

Many different descriptors have been developed in an attempt to define chemistry spaces that are relevant for bioactivity. Aside from being relevant to biological activity, to be useful for combinatorial chemistry, descriptors should also be relatively easy to calculate in order that the methods can be applied to large datasets. Some descriptors commonly used in virtual screening are described below. For more detail on descriptors, see the chapter by Downs and Barnard and the earlier review by Brown [9]. The most commonly used similarity coefficients are the Tanimoto coefficient and Euclidean distance [10]. A weighting scheme may also be applied to assign relative weights to different descriptors. 

Fig. 15.1-3 Schematic visualization of the various concepts to address chemical and biological space (shaded areas) in drug discovery. Medicinal chemistry focused on compound series (red dots) that had shown activity in pharmacological assays and compound optimization was driven by a tight feedback from biological experiments, leading to a focused nonarrayed addressing of chemical space. The combinatorial promise was to systemically explore the chemical space with diverse arrays of compounds (blue dots) to find the suitable starting points. Analysis of combinatorial chemistry libraries showed their limited...

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