Virtual chemistry space

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... 

Scope and nature of chemical space. For Algodign, the entire chemical space is constructed based on chemistry from literature (7). For Tripos, Tripos Discovery Research (TDR), the former contract research division, provided most of the chemistry foundation for the virtual chemistry space... 

Therefore the question remains how should a chemist decide what to synthesize Put another way, how can we filter our enormous virtual chemistry space Computational chemists would like to develop methods capable of automatically evaluating very large libraries of compounds. This... 

Our reasoning is as follows if there are 10 molecules in our virtual chemistry space but we can synthesize only 10 compounds, we have essentially no chance to cover diversity space uniformly. It is foolish even to try. Instead, we should limit our attention to libraries that are more practical ... 

Software tools for virtual screening can be best classified by the input data available for screening. On the one side, there is always a collection of compounds to be screened, which differs in size (from a few tens to several millions) and in structure (from structurally unrelated compounds via combinatorial libraries to chemistry spaces). On the other side, there is the data that is used to create the screening query, which can be a protein structure, a known active compound or a pharmacophore created from several known actives (see Figure 4.1). In summary, we are ending up with four classes of screening tools ... 

Fig. 4.1 Virtual screening tools can be categorized by the compound data to be screened (compound collection, combinatorial library, chemistry space) and the query type (structure-based, ligand-based, descriptor-based, pharmacophore-based). The output is always a list of compounds together with a score quantifying the fit to the query.

In both the second and third approaches, the use of activity scores based on the positions in chemistry space of the virtual library products relative to the positions of known ligands of each individual target incorporates all of the information about known ligands, rather than just the common pharmacophoric features. The structural elements that convey specificity are not excluded in the design, as they may be in the first approach. 

The virtual library was then characterized using the Cerius2 default topological descriptors and physicochemical properties (35). The 50 default descriptors were reduced to three principal components using principal components analysis, and this defined a 3D chemistry space into which the virtual library could be plotted. The chemistry space consisted of 1134 cells and, when the virtual library was mapped into the space, it was found to occupy 364 of the cells thus, this represents the maximum cell coverage that is achievable. 

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