Libraries cherry-picking

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. 

Make decision directly on individual product which yields a cherry-picking library (best product property profile but lower library production efficiency) ... 

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... 

Fig. 15.24 RADDAR a nearly infinite number of chemical structures can be generated by computationally enumerating combinatorial library proposals. Cherry picking of interesting candidates (proposed structures) based on defined computational algorithms allows the chemist to synthesize the relevant subsets of theoretically accessible molecules.

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. 

In product-based selection, the properties of the resulting product molecules are taken into account when selecting the reactants. Typically this is done by enumerating the entire virtual library that could potentially be made. Any of the subset selection methods described previously could be used to select a diverse subset of products, however the resulting subset is very unlikely to represent a combinatorial subset. This process is known as cherry-picking and is synthetically inefficient as far as combinatorial synthesis is concerned. Synthetic efficiency is maximized by taking the combinatorial... 

Any of the compound selection methods that have been developed for reactant selection can also be applied to the product library in a process known as cherry picking. A subset library selected in this way is shown by the shaded elements of the matrix in figure 3. However, a subset of products selected in this way is very unlikely to be a combinatorial library (the compounds in a combinatorial library are the result of combining all of the reactants available in one pool with all of the reactants in all the other pools). Hence, cherry picking is combinatorially inefficient as shown in figure 3 where 7 reactants are required to make the 4 products shown. 

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. 

The shared-pool suppliers, as seen in Table 2 below, are capable of providing 70,000-450,000+ compounds each from stock, generally as dry powder/dry film or as solutions in DMSO (from cherry-picked selections or pre-plated sets). The cost of these libraries is relatively low, allowing discovery scientists the opportunity to screen a large number of structurally and pharmacophorically diverse compounds easily and cheaply. By screening a relevant subset from the compounds offered, the user often can use the hits as the basis for structure queries in sourcing analogues for... 

Figure 2 The 384-microtube plate developed for high-throughput retrieval of compound subsets from large compound collections. At Roche, the 384-microtube plate is used for high-throughput cherry picking of compound samples from the HTS library for hit validation and secondary screening...

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... 

---