Discussion

The talk will describe two complementary data mining approaches used to characterize the GSK Natural Products Set (NPS) based on information from high-throughput screening databases. One of them is an established method based on the data-driven clustering of compounds using a wide range of descriptors , while the other method partitions and hierarchically clusters the data to identify chemical cores. Both methods successfully find structural scaffolds that significantly hit different groups of discrete drug targets, compared with their relative frequency of demonstrating inhibitory activity in a large number of screens. I will describe how these methods can be applied to unveil hidden information in large single-shot HTS data sets. Applied prospectively, this type of information could contribute to the design of new chemical templates for drug-target classes, and guide synthetic efforts for lead optimization based on natural product chemical motifs. Relevant findings for 7TM receptors (7TMRs), ion channels, class-7 transferases (protein kinases), hydrolases and oxidoreductases will be discussed.