Product-based selection

In general, reagent-based selection is much faster and more convenient to execute in the laboratory as compared with the product-based selection. On the other hand, the latter strategy usually provides more accurate results. There exists a potential to combine both approaches to achieve more optimal results, particularly in the case of large exploratory virtual combinatorial libraries, for which mass random synthesis and screening are not economically feasible. In this article, we demonstrated the usefulness of property-based approach for selection of optimal GPCR ligands. 

The compound selection methods described thus far can be used to select compounds for screening from an in-house collection, or to select which compounds to purchase from an external supplier. In combinatorial library design, however, it is necessary to select subsets of reactants for actual synthesis. The two main strategies for combinatorial library design are reactant-based selection and product-based selection. In reactant-based selection, optimized subsets of reactants are selected without consideration of the products that will result and any of the compound selection methods already identified can be used. An early example of reactant-based design is that already described by Martin and colleagues which is based on experimental design and where diverse subsets of reactants were selected for the synthesis of peptoid libraries [1]. 

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... 

Product-based selection is much more computationally demanding than reactant-based selection, however it has been shown that better optimized libraries can result [60, 61], especially when the aim is to optimize the properties of a library as a whole, such as diversity or the distribution of physicochemical properties. In addition, product-based selection is usually more appropriate for focused libraries which require consideration of the properties of the resulting products. 

Many different methods have been developed both to measure diversity and to select diverse sets of compounds, however, currently there is no clear picture of which methods are best. To date, some work has been done on comparing the various methods however, there is a great need for more validation studies to be performed both on the structural descriptors used and on the different compound selection strategies that have been devised. In some cases, the characteristics of the library itself might determine the choice of descriptors and the compound selection methods that can be applied. For example, computationally expensive methods such as 3D pharmacophore methods are limited in the size of libraries that can be handled. Thus for product-based selection, they are currently restricted to handling libraries of tens of thousands of compounds rather than the millions that can be handled using 2D based descriptors. 

PRODUCT-BASED SELECTION 1,000,000,000 generated and characterized structures REAGENT-BASED SELECTION 3500 characterized structures... 

Fig. 1 Computer-assisted design strategy. At best, building blocks are selected by optimizing the enumerated virtual library (product-based selection).

Product-based selection is much more computationally demanding than reagent-based selection. Typically, it requires the computational enumeration of the full virtual combinatorial library and calculation of the descriptors for all possible products, prior to the application of a subset selection method. Consider a three-component reaction with 100 reagents available at each substituent position and assume that the aim is to build a 10 x 10 x 10 combinatorial library. In reagent-based selection, this requires the calculation of descriptors for 300 compounds (100 + 100 + 100). In product-based design, however, the full library of 1 million compounds (100 x 100 x 100) must be enumerated and descriptors must be calculated for each product molecule. 

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