Virtual library, size

The nature of combinatorial chemistry can present a considerable challenge because these libraries are generally produced as arrays of compounds and it is often inconvenient to synthesize individual compounds in order to achieve an optimal design. Two methods have been described that attempt to select optimal subset of reagents from a virtual library that has been partitioned into favorable and unfavorable compounds by some method of filtering. The PLUMS algorithm [97] was designed to simultaneously optimize the size of the library based on effectiveness and efEciency . 

In the early stages of the project when leads do not exist, computational methods can be used to select a diverse set of compounds from a large virtual library. If a compound shows activity then other similar compounds from the library are synthesised and tested. If a lead already exists at the start of the programme, the size of the virtual library can be reduced by selecting a subset of compounds that are similar to the lead. 

Protein targets, which are even too costly for the 5000 subset, can be prescreened by virtual screening (VS) or a biochemical assay and the NMR assay restricted to an economical library size of 100-1000 hit compounds. Taken together, we believe that it pays off to screen experimentally as many fragments as economical for a target. This notion appears to have been adopted by other proponents in NMR screening, such as Abbott with an NMR library of approximately 10 000 compounds (Table 3.2). 

The estimated size of a virtual library that contains all the initially selected building blocks (fragments) is prohibitive, even for a computational analysis ... 

The strategy we employed to reduce the size of the virtual library was based on independent analysis of the properties of individual building blocks, in order to select subsets of suitable and diverse reactants from a pool of available chemicals. 

Equation (3), which is an application of Bayes theorem, is referred to as the Positive Predictive Value. The parameter p is unknown but believed to be very small (<0.01) for large virtual libraries. 1 - p is the power (or 1 - type II error, where ft is the false negative error rate) and a is the type I error, also called the size of a test in the hypothesis testing context, or the false positive error rate. The last equation defines the probability that a molecule is determined to be a hit in a biochemical assay given that the virtual screen predicts the molecule to be a hit. This probability is of great interest because it is valuable to have an estimate of the hit rate one can expect for a subset of molecules that are selected by a virtual screen. 

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