bitimp: Evaluate the Discriminatory Power of Individual Bits in a...

In fingerprint: Functions to Operate on Binary Fingerprint Data

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 [e-mail]

References

Nisius, B.; Bajorath, J.; ChemMedChem, 2010, 5, 859-868.

See Also

bit.spectrum

fingerprint documentation built on May 2, 2019, 2:42 a.m.