Library Design and Compound Selection

The foundation of any collection enhancement approach [30] is to break down the strategy into single libraries or at least sets of libraries. 

A library is a set of compounds made in parallel fashion that bear a common feature [31]. Their largest common denominator is the scaffold. In extreme cases, the scaffold could be a single functional moiety, such as an amide in a library made from a set of commercial amines and a set of commercial carboxylic acids. The concept of diversity-oriented synthesis usually generates subsets of scaffolds (vide infra). 

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Menard, P.R., Lewis, R.A., and Mason, J.S. Chemistry space metrics in diversity analysis, library design, and compound selection. J. Comput. Chem. Inf. Sci. 1998, 38, 1204-1213. 

Chemistry space metrics have been used at Rhone-Poulenc Rorer for diversity analysis, library design, and compound selection (59,80) using DiverseSolutionsto generate a "universal" chemistry space for use as a standard for profiling structural sets of interest. The complementarity of three different diversity measures for comparing and profiling compound collections (a corporate database, com-... 

Menard, RR., Mason, J.S., Morize, I. and Bauerschmidt, S. (1998b). Chemistry Space metrics in Diversity Analysis, Library Design, and Compound Selection. J.Chem.Inf.Comput.Sci., 38, 1204-1213. 

In the case of hit identification, various techniques of library design and compound selection are applied to maximize the likelihood of discovery. The relation-... 

Filters and drug-likeness models can be extremely valuable in library design and compound selection. They typically require just a 2D representation (such as a SMILES string) and so can be used to rapidly eliminate molecules of no interest and to score or rank the remainder (an example of virtual screening). However, it should always be remembered that such approaches are usually very general in nature and that any specific target may require molecules that violate one or more of these more general criteria. 

The genesis of in silico oral bioavailability predictions can be traced back to Lip-inski s Rule of Five and others qualitative attempts to describe drug-like molecules [13-15]. These processes are useful primarily as a qualitative tool in the early stage library design and in the candidate selection. Despite its large number of falsepositive results, Lipinski s Rule of Five has come into wide use as a qualitative tool to help the chemist design bioavailable compounds. It was concluded that compounds are most likely to have poor absorption when the molecular weight is >500, the calculated octan-l-ol/water partition coefficient (c log P) is >5, the number of H-bond donors is >5, and the number of H-bond acceptors is >10. Computation of these properties is now available as an ADME (absorption, distribution, metabolism, excretion) screen in commercial software such as Tsar (from Accelrys). The rule-of-5 should be seen as a qualitative, rather than quantitative, predictor of absorption and permeability [16, 17]. 

Enumeration is the computational equivalent of carrying out a combinatorial synthesis. The result is a virtual library of product molecules that can then be analyzed using a library design program to select compounds of interest. Two different approaches to library enumeration have been developed fragment marking and the reaction-transform approach (14). 

Gillet, V. J. and Willett, P. (2001) Dissimilarity-based compound selection for library design. In Combinatorial library design and evaluation. Principles, software tools and applications in drug discovery, Ghose, A. K. and Viswanadhan, A. N. (eds.), Marcel Dekker, New York, pp. 379-398. 

In the context of general screening, computational library design involves the selection of a subset of compounds that are optimally diverse and representative of available classes of compounds, leading to a non-redundant chemical library for biological testing. Methods reported in the literature include... 

Scaffold proposals were collected and reviewed according to privileged ion channel motifs, chemical feasibility, and fit to our multiple pharmacophores. Building block selection, virtual library design, and filtering yielded small virtual libraries suitable for automated solution-phase synthesis. All synthesized compounds were finally purified and characterized prior to addition to our focused library. 

Compound selection is a core process of library design, and three main methods can be mentioned. Dissimilarity-based methods select compounds in terms of similar-ity/distance between individuals in chemical space. Clustering methods first group compounds into clusters based on similarity/distance and then choose representative compounds from different clusters. Partitioning methods first create a uniform cell space that subdivides the chemical space, then assign all virtual compounds to the relative cells according to their properties, and finally choose representative compounds from different cells. 

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