Enrichment, compound libraries

Cheminformatics methods are required to design screening libraries, enrich existing libraries, select diverse or focused subsets, and pre- or post-filter undesired or reactive compounds. At present more than 10 million unique compounds are commercially available for screening (http //www.emolecules.com/, http //www.chemnavigator.com) and the theoretical number of synthetically tractable compounds even with lead-like properties is enormous (Bleicher et al., 2003 Fink, Bruggesser, and Reymond, 2005). 

However, identification of ion channel modulators by screening compound libraries enriched with ion channel privileged chemotypes offers rapid, efficient access to lead compounds. Flexible data-driven building and optimization of such an ion channel focused library will enable better and faster lead identification of ion channel modulators. 

Test a substantial number of compounds. VS methods generally offer enrichment, but most ranked hit lists contain a significant proportion of false positives. Hitlists should be scaled to 1-5% of the compounds in the virtual library screened. In many real world situations, the computational chemist is being asked to choose lists of compounds representing 0.1% or less of the compounds screened (e.g., the best 100 of 100,000 compounds). Typically, VS methods have been validated considering 1%, 5%, or 10% of the total number of compounds in the VS collection. By following up on more compounds, one increases the probability of impact from VS. 

Bradley and coworkers used the 3D pharmacophore ensemble model to filter a virtual combinatorial library of 3924 N-substituted glycine peptoids (30) containing three known a, actives down to a set of 639 products. Using a cut-down technique, a 160 compound combinatorial library was designed in which the number of compounds that passed the ensemble model filter was maximized. This library contained two of the three known actives present in the original 3924 compound virtual library. This represents a substantial enrichment [(2 actives/160 products) X 100 = 1.25% vs (3 actives/3924 products) x 100 = 0.076%]. 

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