Cherry-picking

Allowed trucking companies to cherry-pick the most desirable traffic, leaving more volume of the less desirable traffic to the railroads. Repealed 1981. 

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... 

Another trick was to publish only some of the data from a clinical trial, a manoeuvre that researchers call cherry-picking the data. Some clinical trials are conducted in more than one location. These are called multi-centre studies. Multi-centre studies make it easier to find sufficient patients to conduct the trial. They also make it easier to cherry-pick the data. For example, one multi-centre study of Prozac was presented to the FDA as showing a drug-placebo difference of three points on the Hamilton scale. When data from this clinical trial was published, the difference was reported as 15 points - a five-times increase in effectiveness. How was this magical augmentation of the benefits of Prozac accomplished The full study was conducted on 245 patients. The published paper reported data from only 27 of these patients. In the published version, the data from the bulk of the patients were left out, making the drug seem much more effective than it really was. 

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.

Recently, Gasteiger et al. [59] reported several models to predict human oral bioavailability using Hou and Wang s data set. A set of ADRIANA.Code and Cerius2 descriptors were calculated, and MLR analysis was performed. The best linear model had r2 of 0.18 and RMSD of 31.15. When a set of subsets was cherry-picked so that each subset had either a common functional group or a similar pharmacological activity, the r2 values were improved and RMSD values dropped. But the performance of those models was still not satisfactory the standard errors were above 20.0 and r2 was lower than 0.6. 

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. 

After the HTS primary screening but before cherry picking (individually selecting) compounds for dose-response measurements. 

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) ... 

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... 

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