This method evaluates the Kullback-Leibler (KL) divergence to rank the individual bits in a binary fingerprint in their ability to discriminate between database and active compounds. This method is implemented based on Nisius and Bajorath and includes an m-estimate correction.
bit.importance(actives, background)
A list of fingerprints for the actives
A list of fingerprints representing the background collection
A numeric vector of length equal to the size of the fingerprints. Each element
of the vector is the KL divergence for the corresponding bit. If a bit position
is never set to 1 in any of the compounds from the actives and the background, then
the KL divergence for that position is undefined and NA
is returned.
Nisius, B.; Bajorath, J.; ChemMedChem, 2010, 5, 859-868.