QSAR - How to statistically determine when variables are "working well together"

QSAR Society,

        Is anyone aware of any publications, "white" papers, or
presentations which discuss the concept of how to
judge when molecular descriptors are "working well together" in a QSAR
equation, to reduce errors and especially
improve predictive power for external prediction sets?

        For example, I am well-aware of the more trivial case of building
QSAR equations with say 2 terms, then 3
terms, then 4 terms, then 5 terms, etc. and then monitoring the R^2, Q^2,
etc.. Then, we say that if the R^2 or Q^2 or
perhaps the F statistic has "improved significantly", this justifies the
use of a higher order equation. We may then use
an Occam's Razor argument to use simpler models when a model with fewer
terms has about the same predictive power
as a set of equations with more terms, etc.

        However, I am looking for (perhaps) something more sophisticated
and thoughtful than this approach! Is there something
like examining the synergism between variables in QSAR equations that
reduces errors in a way that suggests that the
variables "work well together" e.g., some kind of cancellation of errors?
Is there a mathematical formalism for this?

        Thoughts, ideas, leads, comments, etc. are much appreciated here.

        Regards,
        Jim Metz

James T. Metz, Ph.D.
Research Investigator Chemist

GPRD R46Y AP10-2
Abbott Laboratories
100 Abbott Park Road
Abbott Park, IL 60064-6100
U.S.A.

Office (847) 936 - 0441
FAX (847) 935 - 0548

james.metz[a]abbott.com
Received on 2003-10-23 - 13:42 GMT