Drug Discovery
selected. At this point in the library design, specific groups of compounds may be included, such as those incorporating privileged structures of known drug compounds23, scaffolds found in natural products24 or target focused fragments, derived from pharmacophore screens25.
Size and diversity
The next step in the design process is to reduce the number of members to a workable library size while maintaining maximum diversity. There is some debate about the optimum number of frag- ments that should be included in a library. A cen- tral concept behind fragment screening is that a small number of fragments can probe a much greater proportion of available ‘chemical space’ than an HTS screen of larger molecules26. A recent analysis indicates that screening a 1,000-member fragment library which averages 14 heavy atoms (MW 190) is equivalent to screening a library of more than 1,018 molecules that averages 32 heavy atoms (MW 450)27. This is exemplified by Novartis which observes that hit rates from its NMR-based fragment screens are 10-1,000 times higher than for HTS assays28, and by Vernalis that reports it has achieved hit rates of around 0.5% for challenging protein-protein interaction targets through to 7% for kinases from a fragment library of around 1200 fragments27,29. To some extent, the choice of library size depends on the assay; x-ray crystallography is a relatively low throughput technique and libraries of <1,000 are typically employed; 30 NMR screens
can accommodate 1,000-3,000 member libraries; higher throughput is obtained with SPR, where libraries of several thousand may be screened. Chemical diversity is generally accessed by some type of clustering algorithm, with 2D fingerprints as molecular descriptors to compare similarity31,32. For example, clustering for the Maybridge Ro3 library was accomplished with the dbclus algo- rithm33 which uses standard Daylight Fingerprints to identify dense clusters, where similarity within each cluster reflects the Tanimoto value used and the cluster centroid is similar to every other mole- cule within the cluster in a consistent and automat- ed manner. AstraZeneca reports development of a similar in-house clustering method13, while Vernalis uses 2D-pharmacophore graph triangle fingerprints in the second and subsequent iterations of their SeeDs library29. To achieve the required number of diverse molecules for a particular library, the Tanimoto co-efficient may be tailored to give a suitable number of clusters and singletons. For the Maybridge Ro3 library of 1,500 compounds, a Tanimoto level of 0.66 was chosen, as this resulted in 819 clusters and 690 singletons from a starting pool of 8,000 pre-filtered structures.
Quality control and solubility Finally, quality control and solubility of the frag- ment library are important, both initially and on storage. Fragments are tested to confirm identity, purity (typically >95%) and solubility. If the com- pounds are stored in DMSO, regular examination, both visually checking for precipitation13 and
Solubility Study of 4000 Ro3 Compliant Compounds
100 150 200 250 300 350 400 450
50 0
MW range Number tested % Insoluble (1mM) % Insoluble (5mM) 100
10 20 30 40 50 60 70 80 90
0
Figure 2: Solubility study of 4,000 Ro3 compliant compounds at concentrations of 5mM and 1mM in aqueous buffer
Drug Discovery World Winter 2011/12
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Number of compounds tested
80 90
100 110 120 130 140 150 160 170 180 190 200 210 220 230 240 250 260 270 280 290
% Failed solubility test
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