Selecting combinatorial libraries to optimize diversity and physical properties

The program SELECT is presented for the design of combinatorial libraries. SELECT is based on a genetic algorithm with a multi-objective fitness function. Any number of objectives can be included, provided that they can be readily calculated. Typically, the objectives would be to maximize structural diversity while ensuring that the compounds in the library have "drug-like" properties. In the examples given, structural diversity is measured using Daylight fingerprints as descriptors and either the normalized sum of pairwise dissimilarities, calculated with the cosine coefficient, or the average nearest neighbor distance, calculated with the Tanimoto coefficient, as the measure of diversity. The objectives are specified at run time. Combinatorial libraries are selected by analyzing product space, which gives significant advantages over methods that are based on analyzing reactant space. SELECT can also be used to choose an optimal configuration for a multicomponent library. The performance of SELECT is demonstrated by its application to the design of a two-component amide library and to the design of a three-component thiazoline-2-imine library.