Virtual combinatorial library active compound

In general, the described techniques provide an effective, flexible, and relatively fast solution for library design based on analysis of bioscreening data. The quantitative relationships, based on the assessment of contribution values of various molecular descriptors, not only permit the estimation of potential biological activity of candidate compounds before synthesis but also provide information concerning the modification of the structural features necessary for this activity. Usually these techniques are applied in the form of computational filters for constraining the size of virtual combinatorial libraries and... 

Bradley and coworkers used the 3D pharmacophore ensemble model to filter a virtual combinatorial library of 3924 N-substituted glycine peptoids (30) containing three known a, actives down to a set of 639 products. Using a cut-down technique, a 160 compound combinatorial library was designed in which the number of compounds that passed the ensemble model filter was maximized. This library contained two of the three known actives present in the original 3924 compound virtual library. This represents a substantial enrichment [(2 actives/160 products) X 100 = 1.25% vs (3 actives/3924 products) x 100 = 0.076%]. 

The pharmacophore concept has proven to be extremely successful not only in rationalizing structure-activity relationships, but also by its large impact in developing the appropriate 3D tools for efficient virtual screening. Profiling of combinatorial libraries and compound classification are other often-used applications of this concept. 

Research projects in pharmaceutical industry that are in an early phase need bioactive chemotypes as potential lead structures for optimization. Hits with a medium or even weak activity can serve as leads if the overall profile looks attractive. HTS of the in-house compound libraries is the most common source of these lead structures. If information about the 3D structure of the target and/or about bioactive ligand(s) is available, virtual screening can be used to add further active chemotypes either from the existing compounds, for example, from vendor catalogues, or from the virtual chemical space, for example, from virtual combinatorial libraries. Virtual screening can also be used to select a subset from the in-house screening collection if a full HTS is not possible due to cost or time limitations. 

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 ... 

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