Analysis of a large structure/biological activity data set using recursive partitioning and simulated annealing
Year: 2003
Type: report
Abstract: 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.
Authors: Ke Zhang, Jacqueline M. Hughes‐Oliver
Language: English
Subfield: Drug Discovery
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2520924638