EXPERIMENTAL-DESIGNS FOR SELECTING MOLECULES FROM LARGE CHEMICAL DATABASES

Recent developments in high-throughput screening and combinatorial che mistry have generated interest in experimental design methods to selec t subsets of molecules from large chemical databases. In this manuscri pt three methods for selecting molecules from large databases are desc ribed: edge designs, spread designs, and coverage designs. Two algorit hms with linear time complexity that approximate spread and coverage d esigns are described. These algorithms can be threaded for multiproces sor systems, are compatible with any definition of molecular distance, and may be applied to very large chemical databases. For example, ten thousand molecules were selected using the maximum dissimilarity appr oximation to a spread design from a sixty-dimensional simulated molecu lar database of one million molecules in approximately 6 h on a UNIX w orkstation.