Maximally diverse set

CONFORT performs an exhaustive conformational analysis of a molecule [71]. Two different search modes either generate a user-defined number of conformations, or output a maximally diverse set of conformations, which was used in this study. The diversity metric is based on interconformational distances that circumvent the generation of duplicate structures. The conformations are relaxed and optimized by applying only internal coordinates and analytic gradients and by the Tripos force field package. 

This entire strategy was applied to each of the four possible scaffold conformations. The top 50 components for each of the scaffold families were merged for each attachment point (i.e. Ri, R2, R3), and hierarchically clustered to maximize the diversity of the compounds chosen for library synthesis. In the end, the 10 best scoring compounds from unique clusters were chosen for each Ri, R2, R3 attachment point. Thus, our final library had the most diverse set of components that fitted our structural hypothesis. 

Alternatively, a product-based scheme can be envisaged, in which reagents are selected at all positions simultaneously, so that the score of the generated products is maximized. This type of approach has been championed by Gillet etal. [51] and by Lewis et al. [52], Finally, one may pick the most diverse set of products and then deconvolute to find the sets of reagents required to make that set. This kind of approach is sometimes called cherry-picking. 

The subset seleetion can be performed iteratively. The first compound is chosen at random and the next compound is selected to be maximally dissimilar to the first the third is then selected to be maximally dissimilar to the first two, and so on. The selection stops when a prespecified number of compounds have been selected or no more compounds can be chosen that are below a given similarity or above a certain distance to another compound in the selected set. Pearlman (llb,c) refers to such methods as "addition" algorithms because they add compounds to a diverse set of increasing size. He notes that such algorithms are quite satisfactory when the size of the desired subset is relatively modest but, given that the time required for such algorithms is proportional to the size of the total population and the square of the size of the desired diverse subset, they are far less satisfactory when, for example, selecting a subset of 10,000 from a population of 1,000,000. 

The DOFLA has advantage in the following aspects (Table 17.1). This approach benefits from the combinatorial chemistry techniques, once an efficient synthetic route can be developed for a diverse set of dyes. Thus broader chemical space could be explored and unknown/unexpected molecular interactions might be discovered. The design and preparation of the fluorescent dye library is unbiased to any specific target analyte, and the library would be evaluated with quite distinct analytes to maximize the chance of application in different fields. Taken all these together,... 

A screening library is designed as a maximally diverse subset of the virtual library in order to explore the entire chemical space and to identify compartments of hits or highly potent scaffolds. An increased hit rate is not necessary, and not even expected in this first design step, because the selected set of compounds is evenly distributed in the entire chemical space defined by the virtual library. 

Since the first description was only two decades ago, combinatorial biosynthesis has advanced from a limited set of proof-of-principle experiments into a more mature scientific discipline. To reach the maximal potential of natural product structural diversity, the combination of this approach with other established and emerging technologies will ultimately provide access to a rich variety of unnatural natural products with improved properties or new biological activities for future drug discovery and development. 

Step 1 starts with a large set of seed solutions, which may be created by heuristics or by random generation. One possible implementation then generates a diverse subset of these by choosing some initial seed solution, then selecting a second one that maximizes the distance from the initial one. The third one maximizes the distance from the nearest of the first two, and so on. 

The first application of a computational method to select structurally diverse compounds for purchase started in 1992 at the Upjohn Company, which predated the formation of Pharmacia Upjohn by about three years. The basic approach selected compounds using a method based upon maximum dissimilarity and was implemented using SAS software [11]. This later evolved into the program Dfragall, which was written in C and is described in Section 13.6.3. Basically, a set of compounds that is maximally dissimilar from the corporate compound collection is chosen from the set of available vendor compounds. Early versions of the process relied solely on diversity-based metrics but it was found that many nondrug like compounds were identified. As a result, structural exclusion criteria were developed to eliminate compounds that were considered unsuitable for... 

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