  Predicting interactions between small molecules and proteins is a crucial
ingredient of the drug discovery process. In particular, accurate predictive
models are increasingly used to preselect potential lead compounds from large
molecule databases, or to screen for side-effects. While classical in silico
approaches focus on predicting interactions with a given specific target, new
chemogenomics approaches adopt cross-target views. Building on recent
developments in the use of kernel methods in bio- and chemoinformatics, we
present a systematic framework to screen the chemical space of small molecules
for interaction with the biological space of proteins. We show that this
framework allows information sharing across the targets, resulting in a
dramatic improvement of ligand prediction accuracy for three important classes
of drug targets: enzymes, GPCR and ion channels.
