I’ve just uploaded a new version of the fingerprint package (v3.3) to CRAN that implements some ideas described in Nisius and Bajorath. First, the balance method generates “balanced code” fingerprints, which given an input fingerprint of N bits, returns a new fingerprint of 2N bits, such that the bit density is exactly 50%. Second, bit.importance is a method to evaluate the importance of each bit in a fingerprint, in terms of the Kullback-Liebler divergence between a collection of actives and background molecules. In other words, the method ranks the bits in terms of their ability to discriminate between the actives and the background molecules.