DECONVOLUTION OF COMBINATORIAL LIBRARIES FOR DRUG DISCOVERY - A MODELSYSTEM

Iterative synthesis and screening strategies have recently been used t o identify unique active molecules from complex synthetic combinatoria l libraries. These techniques have many advantages over traditional sc reening methods, including the potential to screen large numbers of co mpounds to identify an active molecule while avoiding analytical separ ations and structural determination of unknown compounds. It is not cl ear, however, whether these techniques identify the most active molecu lar species in the mixtures and, if so, how often. Two key factors whi ch may affect success of the selection process are the presence of man y active compounds in the Library with a range of activities and the c hosen order of unrandomization. The importance of these factors has no t been previously studied. Moreover, the impact of experimental errors in determination of subset activities or in randomization during libr ary synthesis is not known. We describe here a model system based on o ligonucleotide hybridization that addresses these questions using comp uter simulations. The results suggested that, within achievable experi mental and library synthesis error, iterative deconvolution methods ge nerally find either the best molecule or one with activity very close to the best. The presence of many active compounds in a library influe nced the profile of subset activities, but did not preclude selection of a molecule with near optimal activity.