  Active learning has shown to reduce the number of experiments needed to
obtain high-confidence drug-target predictions. However, in order to actually
save experiments using active learning, it is crucial to have a method to
evaluate the quality of the current prediction and decide when to stop the
experimentation process. Only by applying reliable stoping criteria to active
learning, time and costs in the experimental process can be actually saved. We
compute active learning traces on simulated drug-target matrices in order to
learn a regression model for the accuracy of the active learner. By analyzing
the performance of the regression model on simulated data, we design stopping
criteria for previously unseen experimental matrices. We demonstrate on four
previously characterized drug effect data sets that applying the stopping
criteria can result in upto 40% savings of the total experiments for highly
accurate predictions.
