Analysis of a Large Structure/Biological Activity Data Set Using Recursive Partitioning and Simulated Annealing

Large quantities of structure and biological activity data are quickly accumulated with the development of high-throughput screening (HTS) and combinatorial chemistry. Analysis of structure-activity relationships (SAR) from such large data sets is becoming challenging. Recursive partitioning (RP) is a statistical method that can identify SAR rules for classes of compounds that are acting through different mechanisms in the same data set. We use a newly proposed method called Recursive Partitioning and Simulated Annealing (RP/SA) to produce stochastic regression trees for biological activity. In the new algorithm a set of structural descriptors is extracted at each splitting node by using SA as a stochastic optimization tool. For one data set, results show that RP/SA is advantageous in analyzing the SAR information.