bit.importance

A list of fingerprints for the actives

actives

A list of fingerprints representing the background collection

background

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.

programming

Functions to manipulate binary fingerprints
of arbitrary length. A fingerprint is represented by an object of S4 class 'fingerprint'
which is internally represented a vector of integers, such
that each element represents the position in the fingerprint that is set to 1.
The bitwise logical functions in R are overridden so that they can be used directly
with 'fingerprint' objects. A number of distance metrics are also
available (many contributed by Michael Fadock). Fingerprints
can be converted to Euclidean vectors (i.e., points on the unit hypersphere) and
can also be folded using OR.  Arbitrary fingerprint formats can be handled via line
handlers. Currently handlers are provided for CDK, MOE and BCI fingerprint data.

bit.importance: Evaluate the Discriminatory Power of Individual Bits in a Binary Fingerprint

Description

Usage

Arguments

Value

References

See Also