  Drug discovery is the process of identifying compounds which have potentially
meaningful biological activity. A major challenge that arises is that the
number of compounds to search over can be quite large, sometimes numbering in
the millions, making experimental testing intractable. For this reason
computational methods are employed to filter out those compounds which do not
exhibit strong biological activity. This filtering step, also called virtual
screening reduces the search space, allowing for the remaining compounds to be
experimentally tested. In this paper we propose several novel approaches to the
problem of virtual screening based on Canonical Correlation Analysis (CCA) and
on a kernel-based extension. Spectral learning ideas motivate our proposed new
method called Indefinite Kernel CCA (IKCCA). We show the strong performance of
this approach both for a toy problem as well as using real world data with
dramatic improvements in predictive accuracy of virtual screening over an
existing methodology.
