The idea of comparing datasets using fingerprints was described in Guha \& Schurer (2008). The idea is that one can summarize the dataset by counting the frequency of occurrence of each bit position. The frequency is normalized by the number of fingerprints considered. Thus a collection of N fingerprints can be converted to a single vector of numbers highlighting the most frequent bits with respect to a given dataset. A plot of this vector looks like a traditional spectrum and hence the name.
The bit spectra for two datasets (assuming that the same types of fingerprints have been used) allows one to compare the similarity of the datasets, without having to do a full pairwise similarity calculation. The difference between the structural features of the datasets can be quantified by evaluating the distance between the two bit spectra.
bit.spectrum(fplist)A list structure with each element being an object of class
fingerprint. These will can be constructed by hand or
read from disk via fp.read.
All fingerprints in the list should be of the same length.
A numeric vector of length equal to the size of the fingerprints.
Guha, R.; Schurer, S.; J. Comp. Aid. Molec. Des., 2008, 22, 367-384.