The Effectiveness of Reactant Pools for Generating Structurally-Diverse Combinatorial Libraries

Abstract

Current approaches to the design of combinatorial libraries assume that structural diversity in the reactant pools corresponds to structural diversity in the combinatorial libraries that result from reacting these pools together. In experiments with three different published libraries, dissimilarity-based compound selection (DBCS) is applied at two levels. First, the DBCS algorithm is applied at the reactant level, a library is built, and its diversity is measured. Second, the DBCS algorithm is applied to the full set of products generated by enumeration of all the reactants and the diversity of the subset is measured. Results show that reactant-based selection, which attempts to maximize diversity in the pools, results in noticeably less diverse libraries than if the selection is performed at the product level. Experiments are reported to estimate the upperbound to diversity achievable using DBCS, and it appears that DBCS is very effective at finding maximally diverse subsets. However, applying DBCS selection at the product level is synthetically inefficient since it does not result in a combinatorial library. We thus describe a genetic algorithm for selecting combinatorial libraries from the fully enumerated products and demonstrate that these libraries are significantly more diverse than those generated using reactant-based selection.