New molecular shape descriptors: Application in database screening

Abstract

Geometric descriptors are becoming popular tools for encoding molecular shape, for use in database screening and clustering calculations. They provide condensed representations of complex objects and, as a consequence, can usually be compared quite rapidly. Here we present a number of new descriptors and methods for the quantification of molecular shape similarity. The techniques are tested using two different biological systems, with particular emphasis on their potential utility as methods for prescreening shape-based database searches. Results are compared with data sets produced using the DOCK program. We find that such similarity evaluations are useful for finding molecules with complementary shape, and that they contain an enriched number of potential DOCK hits when compared to the original databases. Significant limitations in the utility of such DOCK prescreens are discussed, and potential solutions are considered.

Ultrafast shape recognition to search compound databases for similar molecular shapes

“…However, histogram calculation has a number of well-known drawbacks, specially for very large databases, such as the difficulty of selecting a bin size suitable for all molecules in the database along with the requirement of relatively large storage and computing power. [16][17][18] These difficulties are circumvented by calculating instead the first moments of each of the four distributions of atomic distances in order to characterize them as a way to encode the molecular shape. Such approach is based on a theorem 30 from statistics, which proves that a distribution is completely determined by its moments.…”

“…19), which are therefore not intended to describe exclusively molecular shape and thus are outside the scope of this article. These techniques are fast (in the region of 500-2 000 comparisons per second 18 on a 1995 PC), but they are known to be less effective than superposition methods, 20 and thus they are normally used for database prescreening as suggested by their authors 18 (i.e. quickly filtering molecules with very different shapes) instead of stand-alone molecular shape comparison.…”

“…This increases the efficiency of the method at the risk of missing similar molecules because of the inaccuracies in the signature representation. Good et al 18 devised a series of molecular descriptors based on triangles of atom triplets. Such descriptors were encoded as bit strings and histograms, while including an extension to compare molecular surfaces.…”

Chemoinformatics Research at the University of Sheffield: A History and Citation Analysis

“…An example of the nearest neighbours obtained in a Tanimoto-based similarity search is shown in Figure 5, where the close relationship to the target structure is clearly evident. Later work in Sheffield [64] evaluated the inter-atomic distance screens that are used for 3D substructure searching when applied to the calculation of 3D structural similarities, and this occasioned several subsequent discussions of the use of both distance-based and angle-based methods for 3D similarity searching (see, e.g., [65][66][67]). However, most of the research, both in Sheffield and elsewhere, of 3D similarity measures has focused on two alternative approaches: the use of the maximum common substructure (MCS) and of molecular field overlaps.…”

Investigating the extension of pairwise distance pharmacophore measures to triplet-based descriptors

“…Many examples of 3D geometric descriptors have been given in the literature [1][2][3][5][6][7][8][9]11]. Pepperell and Willett considered a number of 3D similarity measures involving interatomic distance measures for the purpose of database clustering [3,5].…”