One of the major goals of rational design of combinatorial libraries is to design libraries with maximum diversity to enhance the potential of finding active compounds in the initial rounds of high-throughput screening programs. We present strategies to visualize and optimize the structural diversity of sets of molecules, which can be either potential substituents to be attached at specific positions of the library scaffold, or entire molecules corresponding to enumerated libraries. The selection of highly diverse subsets of molecules from the library is based on the stochastic optimization of 'Diversity' functions using a single-point-mutation Monte Carlo technique. The Diversity functions are defined in terms of the distances among molecules in multidimensional property space resulting from the calculation of 2D and 3D molecular descriptors. Several Diversity functions, including an implementation of D-Optimal design, are applied to select diverse subsets and the results are compared. The diversity of the selected subsets of molecules is visualized by embedding the intermolecular distances, defined by the molecules in multidimensional property space, into a three-dimensional space.
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