Comparison of Three Holographic Fingerprint Descriptors and their Binary Counterparts

Abstract

Molecular fingerprint representations were compared with respect to their ability to retrieve sets of ligands by retrospective similarity searching. Twelve different biological target classes were considered, and both "holographic" fingerprint vectors containing floating-point or integer values giving the frequency of occurrence of fragment types or atom pairs, and their "binary" representations were compared. The descriptors were substructure-based or grounded on topological and spatial pharmacophore type pairs. Enrichment factors between seven and 19 were obtained for the top-scoring two percent of the reference database, depending on the target class. Although the holographic fingerprints yielded higher enrichment factors on average, only for two target classes significant differences (> 20%) between holographic and binary fingerprint representations were observed. This result demonstrates that binary fingerprint representations can be applied to rapid similarity searching for most target classes without losing significant enrichment of actives in the virtual hit lists. A comparison of the individual molecules retrieved by holographic fingerprints and their binary representation revealed that significantly different sets of actives and "inactives" were found. The intersections of two virtual hit lists ranged from zero to approximately 90% for the set of known ligands. This result demonstrates that "binarization" of continuous molecular descriptors has a significant effect on the topology of chemical space and the resulting neighborhood behavior of chemical similarity searching.

Diversity in Medicinal Chemistry Space

“…Slight modifications of similarity coefficients can be introduced to take into account the frequency of occurrence [47,70]. Similarity search comparison studies show that molecular holograms performed equally well as [68,70] and, occasionally, significantly better [68] than their binary counterparts.…”

“…Ghose and Crippen have defined a system of 120 atom types for encoding organic molecules and predict their hydrophobicity and molar refractivity [64]. The counts of these atom types have been used as molecular descriptors [66,67] or holographic fingerprint [68]. Atom type holographic fingerprints can be seen as an extended molecular formula.…”

Calculation and application of activity discriminants in lead optimization

“…The number of data points in each assay is number of compounds that have been successfully measured experimentally. The number of compounds in the test set is the number of compounds synthesized in 2006-2008, used [23][24][25][26], physical chemical properties [27], partial charge [28][29][30], topological polar surface area [31][32][33], molecular fingerprints [34][35][36][37][38][39][40], molecular connectivity indices [41][42][43][44] and E-state indices [44][45][46][47], CoMFA [48,49], GRIND [50][51][52], and Volsurf [53][54].…”

FTree query construction for virtual screening: a statistical analysis

“…In recent years, several chemoinformatic tools have provided convincing results in effectively retrieving actives from a random collection of molecules [1][2][3][4][5][6][7]. Using either fingerprinting methods coupled with similarity indexes, like Tanimoto [8], or taking advantage of 3D-search algorithms, chemoinformaticians working in drug discovery have several tools to perform one of their major tasks.…”