Deciding When to Stop: Efficient Experimentation to Learn to Predict Drug-Target Interactions (Extended Abstract)

Abstract

An active learning method for identifying drug-target interactions is presented which considers the interaction between multiple drugs and multiple targets at the same time. The goal of the proposed method is not simply to predict such interactions from experiments that have already been conducted, but to iteratively choose as few new experiments as possible to improve the accuracy of the predictive model. Kernelized Bayesian matrix factorization (KBMF) is used to model the interactions. We demonstrate on four previously characterized drug effect data sets that active learning driven experimentation using KBMF can result in highly accurate models while performing as few as 14% of the possible experiments, and more accurately than random sampling of an equivalent number. We also provide a method for estimating the accuracy of the current model based on the learning curve; and show how it can be used in practice to decide when to stop an active learning process.