  An important experimental design problem in early-stage drug discovery is how
to prioritize available compounds for testing when very little is known about
the target protein. Informer based ranking (IBR) methods address the
prioritization problem when the compounds have provided bioactivity data on
other potentially relevant targets. An IBR method selects an informer set of
compounds, and then prioritizes the remaining compounds on the basis of new
bioactivity experiments performed with the informer set on the target. We
formalize the problem as a two-stage decision problem and introduce the Bayes
Optimal Informer SEt (BOISE) method for its solution. BOISE leverages a
flexible model of the initial bioactivity data, a relevant loss function, and
effective computational schemes to resolve the two-step design problem. We
evaluate BOISE and compare it to other IBR strategies in two retrospective
studies, one on protein-kinase inhibition and the other on anti-cancer drug
sensitivity. In both empirical settings BOISE exhibits better predictive
performance than available methods. It also behaves well with missing data,
where methods that use matrix completion show worse predictive performance. We
provide an R implementation of BOISE at
https://github.com/wiscstatman/esdd/BOISE
