Maximum diversity library

A screening library is designed as a maximally diverse subset of the virtual library in order to explore the entire chemical space and to identify compartments of hits or highly potent scaffolds. An increased hit rate is not necessary, and not even expected in this first design step, because the selected set of compounds is evenly distributed in the entire chemical space defined by the virtual library. 

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Talking about selectivity in the context of a combinatorial library seems odd, and indeed, from the perspective of generating maximum diversity, it is critical that the reaction is nonstereoselective (stereorandom) and nonsubstrate selective (general). However, it is important that reaction occurs only with desired functional groups on library constituents rather than with target functionality, or library functionality, leading to irreversible formation of a product. 

Typically, SELECT would be configured to design libraries that simultaneously have maximum diversity, minimum cost and druglike physicochemical properties. The physicochemical property profiles are optimised by minimising the difference between the distribution of a property in the library and some reference distribution, for example, the distribution of the property in a collection of known drugs. Each of the properties is standardised and then relative weights are defined by the user at run time. 

Library for repository expansion and high-through-put screening (HTS) (lead finding) Maximum diversity of single library. Maximum diversity within a set of libraries. Find gaps in the represented chemical space. 

Fig. 13 Selection of compounds from a virtual combinatorial library, a) First six steps of a maximum dissimilarity selection, b) Selection by excluding similar compounds, c) Maximum diversity selection as a result of clustering, d) Grouping by partitioning of descriptor space.

The first screening library can be designed in terms of maximum diversity and high drug-likeness (drug-like educts are no guarantee for drug-like products ). 

Cell-based methods, as well as clustering or distance-based methods, aim at extracting representative structurally diverse subsets of compounds from large chemical databases [Cummins, Andrews et al, 1996 Mason and Pickett, 1997 Pearlman and Smith, 1999 Earnum, Desjarlais et al, 2003]. They are mainly used in design and optimization of combinatorial libraries the most important aspect being here to ensure maximum diversity within and between libraries before they are produced. Moreover, cell-based methods are used for lead discovery purposes allowing the selection of the compounds most similar to the active reference target. 

The initial design phase for combinatorial compound collections focuses on computer-generation of a virtual library and selecting a subset of compounds for chemical synthesis on the basis of specific characteristics, such as maximum diversity, desired lipophilicity, and lack of toxic and reactive functionality [13]. When combinatorial compounds are used for lead optimization, collections can be designed on the basis of a reference structure with the collection chosen to represent the desired degree of diversity. Software for the design of such combinatorial reactions is commercially available. An alternative ap-... 

A number of different approaches have been used for selecting diverse sets of compounds. One of the simplest is maximum dissimilarity, in which each new compound is chosen to be as dissimilar as possible to those already selected [60]. This can drastically reduce the library size without significantly reducing the likelihood of discovering classes... 

However, efficient directed evolution is not a matter of generating huge libraries which then require considerable efforts in screening for the desired property. The goal is to create a maximum in structural diversity while minimizing the size of the libraries (17,23,24,37). 

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