Measuring Diversity: Experimental Design of Combinatorial Libraries for Drug Discovery

Abstract

Screening synthetic combinatorial libraries, such as mixtures of oligo(N-substituted)glycines, facilitates rapid drug lead discovery and optimization by vastly increasing the number of candidate molecules made and tested. Discovery efficiency and productivity can be further improved by using experimental design to maximize molecular diversity for a given library size or to bias the library with key features for a specific receptor. We describe new methods to quantify molecular diversity using descriptors that characterize lipophilicity, shape and branching, chemical functionality, and specific binding features. Experimental design methods select sets of side chains that are diverse in these properties, and "flower plots" allow the diversity to be graphically compared. We also quantify the overall diversity accessible to different families of combinatorial chemistry.

Dissimilarity-Based Algorithms for Selecting Structurally Diverse Sets of Compounds

“…The initial approach, first described in detail by Martin et al (1995), takes as its basis the assumption that if it is possible to identify maximally diverse (or, more realistically, near maximally diverse) sets of reactants, then their use will result in the generation of a maximally diverse combinatorial library of products when the reactants are combined in a combinatorial synthesis. This reagentbased approach is computationally attractive, as it means that the selection algorithm need only be applied to the individual sets of reagents, and it rapidly established itself as the method of choice for designing combinatorial libraries.…”

“…This has led to the development of several diversity indices, which provide a single-number quantification of the degree of structural variation within a dataset. Examples of such approaches include a count of the number ofbits that are set in the union of all of the fingerprints for a dataset (Martin et al, 1995), the number of distinct substructures that can be generated from all of the molecules in a dataset (Bone and Villar, 1997), the fraction of the bins in a partition that contain some minimal number of molecules (Pickett et al, 1996), and the sum of the pairwise inter-molecular dissimilarities for a dataset (Turner et al, 1997).…”

Rapid Quantification of Molecular Diversity for Selective Database Acquisition

“…There have already been several descriptions of measures of structural diversity; [2][3][4][5][6] this communication reports a further such measure and an algorithm for its calculation that allows it to be applied to even the largest datasets at minimal computational cost.…”

A novel approach for screening discrete variations in organic synthesis

“…We previously presented another method [4] for solving the problem, based on Fedorov's iterative algorithm [5]. The algorithm was based on the D-optimality criterion [1] A similar approach to the design problem has been given by Martin et al [6] in the context of designing combinatorial libraries.…”