Library design cherry-picking

The term hit confirmation, as we define it, involves three components reproducibility, confirmation of chemical structure, and confirmation of chemical purity. Confirmation of hit reproducibility requires that the subset of library compounds designated as hits in the primary screen be identified, that samples of each of these be obtained from the library bank (a process often referred to as cherry picking ), and that these samples be retested, at least once but preferably multiple times, to determine if they reproducibly confer an inhibition percentage of the target enzyme... 

Product-based approaches can be divided into those that take the combinatorial constraint into account such that each reactant in one pool appears in a product with every reactant from every other reactant pool, and those that merely pick product molecules without consideration of the synthetic constraint. The latter approach is often referred to as cherry-picking and is synthetically inefficient as far as combinatorial synthesis is concerned. In this chapter the emphasis is on product-based library design methods that take the combinatorial constraint into account. 

Next, we used an in-house library design software (see details in Chapter 15) to enumerate the virtual libraries and then calculated various physical properties. Products were removed from consideration if MW is > 300, number of rotatable bonds > 3, and ClogP > 3. For solubility, two in-house model calculations were applied as filters turbidimetric >10 mg/mL and thermodynamic solubility >100 xM. The resulting cherry-picked library was then reviewed by NMR spectroscopists to remove compounds with possible artifacts, likely to be insoluble, or likely to be false positive. These included some conjugated systems and compounds with likelihood of indistinct NMR spectra. 

An example of library design by multiple property optimization is shown in Figure 13.2. A pool of 83 400 molecules was subjected to property analysis, and a subset was cherry-picked to form the selected library containing 7350 members. 

Although most applications were of the cherry-picking type design, the combinatorial design of new chemical libraries should also be feasible. In this case, the scores obtained with the various models can be used to sort the virtual library, followed by building block frequency analysis cf. Focus2D) to determine which reagents should be used in chemical synthesis. Alternatively, combinatorial optimization approaches, such as those in described in ref. 4, can be applied where the model-predicted scores are used as the objective function for optimization. 

Although the objective is always to identify which reactants should be used to make the products, there are two fundamental approaches to library design reactant-based methods and product-based methods. Purely product-based methods, which select (or cherry pick) desired products without regard for the number of reactants required to form those products (as in standard similarity... 

The chapter begins with a discussion of similarity and diversity measures and how they can be applied in a virtual screening context. The various computational filters in use are also discussed. The rest of the chapter is concerned with different approaches to combinatorial library design, beginning with reagent-based methods followed by product-based approaches of cherry picking and combinatorial subset selection. Finally, approaches to designing libraries optimized on multiple properties simultaneously are discussed. 

MCRs can be considered the cradle of combinatorial chemistry [56]. Other than the stepwise library synthesis described in the library design section above, MCRs have the advantage of the very short reaction sequence. This allows for the possibility to apply extreme cherry-picking of the desired products, resulting in very low matrix coverage and high structural diversity regarding the decoration of the scaffold. 

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