Deconvolution of combinatorial libraries for Drug discovery: theoretical comparison of pooling strategies.

Synthesis and testing of mixtures of compounds in a combinatorial library allow much greater throughput than synthesis and testing of individual compounds. When mixtures of compounds are screened, however, the possibility exists that the most active compound will not be identified. The specific strategies employed for pooling and deconvolution will affect the likelihood of success. We have used a nucleic acid hybridization example to develop a theoretical model of library deconvolution for a library of more than 250,000 compounds. This model was used to compare various strategies for pooling and deconvolution. Simulations were performed in the absence and presence of experimental error. We found iterative deconvolution to be most reliable when active molecules were assigned to the same subset in early rounds. Reliability was reduced only slightly when active molecules were assigned randomly to all subsets. Iterative deconvolution with as many as 65,536 compounds per subset did not drastically reduce the reliability compared to one-at-a-time testing. Pooling strategies compared using this theoretical model are compared experimentally in an accompanying paper.