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Active learning has shown to reduce the number of experiments needed toobtain high-confidence drug-target predictions. However, in order to actuallysave experiments using active learning, it is crucial to have a method toevaluate the quality of the current prediction and decide when to stop theexperimentation process. Only by applying reliable stoping criteria to activelearning, time and costs in the experimental process can be actually saved. Wecompute active learning traces on simulated drug-target matrices in order tolearn a regression model for the accuracy of the active learner. By analyzingthe performance of the regression model on simulated data, we design stoppingcriteria for previously unseen experimental matrices. We demonstrate on fourpreviously characterized drug effect data sets that applying the stoppingcriteria can result in upto 40% savings of the total experiments for highlyaccurate predictions.

Deciding when to stop: Efficient stopping of active learning guided drug-target prediction