Retrospective analysis of an experimental high-throughput screening data set by recursive partitioning

With the emergence of combinatorial chemistry, whether based on parallel, m ixture, solution, or solid phase chemistry, it is now possible to generate large numbers of diverse or focused compound libraries. In this paper we ai m to demonstrate that it is possible to design targeted libraries by applyi ng nonparametric statistical methods, recursive partitioning in particular, to large data sets containing thousands of compounds and their associated biological data. Moreover, when applied to an experimental high-throughput screening (HTS) data set, our data strongly suggest that this method can im prove the hit rate of our primary screens (about 4- to 5-fold) while increa sing screening efficiency: less than one-fifth of the complete selection ne eds to be screened in order to identify about 75% of all actives present.