| bit.importance {fingerprint} | R Documentation |
Evaluate the Discriminatory Power of Individual Bits in a Binary Fingerprint
Description
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.
Usage
bit.importance(actives, background)
Arguments
actives |
A list of fingerprints for the actives |
background |
A list of fingerprints representing the background collection |
Value
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.
Author(s)
Rajarshi Guha rajarshi.guha@gmail.com
References
Nisius, B.; Bajorath, J.; ChemMedChem, 2010, 5, 859-868.