Use of recursive partitioning in the sequential screening of G-protein–coupled receptors

High-throughput screening (HTS) is changing as more compounds and better assay techniques become available. HTS is also generating a large amount of data. There is a need to rationalize the HTS process, because, in some cases, the screening of all available compounds is not economically feasible. In addition to the selection of promising compounds, there is a need to learn from the data that we collect. In this paper, we use a data-mining method, recursive partitioning, to help uncover and understand structure–activity relations and to help biology and chemistry experts make better decisions on which compounds to screen next and better characterize. The sequential-screening process is presented and the results of applying that process to 14 G-protein–coupled receptor assays are reported.