Selective library design

Key Words Chemical database compound selection library design molecular diversity molecular similarity neighborhood behavior similar property principle similarity searching. 

Fig. 8. CDK4 selective library design process of Honma et al. (64). (A) Align sequences of 390 kinases. Dark circles denote residues with <40% conservation or subject to replacement in CDK1/2/6. (B) Darker residues in ATP binding site pinpoint the least conserved residues highlighted in (A). (C) Map lead structure onto difference residues. Arrows denote direction and distance to said amino acids. (D) Design library according to these constraints. Resulting compounds show up to 180-fold selectivity for CDK4 with respect to CDK2. Adapted from ref. 64.

J. (2004) REALISIS a medicinal chemistry-oriented reagent selection, library design, and profiling platform. J Chem Inf Comput Sci 44,2199-2206. 

In this chapter we will exemplify this method with selected library design problems and also demonstrate how to apply ProSAR designs with concurrent optimisation of product property profile to design libraries that will not only help to derive a SAR, but also have an attractive property profile. 

Yasri, A., Berthelot, D., Gijsen, H., Thielemans, T., Marichal, P., Engles, M., Hoflack, J. (2004) REALISIS a medicinal chemistry-oriented reagent selection, library design, and profiling platform. / Chem Inf Comput Sci 44, 2199-2206. 

In research, many quantitative and graphical methods are used in selecting between individual compounds, either as potential library of collection members or in filtering hits. Multicriteria approaches to library design typically seek to balance diversity and likelihood of favorable properties [16]. In early screening, the rules for choices between hits are part of a research process typically applied to diverse projects, whereas at the end of the discovery process compound choice commits to starting a single development project. 

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]. 

They are, as currently practiced, nearly always directed by chemoinformatics, i.e., the application of drug-like product information in library design and selection of monomers. 

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. 

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