Deciding when to stop: Efficient stopping of active learning guided drug-target prediction
This work addresses the challenge of efficiently saving time and costs in experimental drug discovery processes, though it is incremental as it builds on existing active learning methods.
The paper tackles the problem of determining when to stop active learning in drug-target prediction to save experiments, and demonstrates that applying learned stopping criteria can reduce total experiments by up to 40% while maintaining high accuracy.
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.