Automated experiment design for drug development

Abstract

Testing drugs in discovery is time consuming and expensive. An idea is then to eliminate unpromising compounds from the testing phase by using online learning methods to predict properties of yet to be tested compounds and determining which drugs to test. This is done by comparing substructures in the graph representation of compounds, transformed into a compressed high dimensional space where a Gaussian process bandit and a linear bandit is used to predict properties of new compounds. Results show that the bandits perform signi cantly better than random selection and that the feature compression probably does not decrease the overall accuracy of the predictions.