Deconvolution of combinatorial libraries for drug discovery: a model system

Abstract

Iterative synthesis and screening strategies have recently been used to identify unique active molecules from complex synthetic combinatorial libraries. These techniques have many advantages over traditional screening methods, including the potential to screen large numbers of compounds to identify an active molecule while avoiding analytical separations and structural determination of unknown compounds. It is not clear, however, whether these techniques identify the most active molecular species in the mixtures and, if so, how often. Two key factors which may affect success of the selection process are the presence of many active compounds in the library with a range of activities and the chosen order of unrandomization. The importance of these factors has not been previously studied. Moreover, the impact of experimental errors in determination of subset activities or in randomization during library synthesis is not known. We describe here a model system based on oligonucleotide hybridization that addresses these questions using computer simulations. The results suggested that, within achievable experimental and library synthesis error, iterative deconvolution methods generally find either the best molecule or one with activity very close to the best. The presence of many active compounds in a library influenced the profile of subset activities, but did not preclude selection of a molecule with near optimal activity.