Adaptive experimental design for drug combinations
Drug cocktails formed by mixing multiple drugs at various doses provide more effective cures than single-drug treatments. However, drugs interact in highly nonlinear ways making the determination of the optimal combination a difficult task. The response surface of the drug cocktail has to be estimated through expensive and time-consuming experimentation. Previous research focused on the use of space-exploratory heuristics such as genetic algorithms to guide the search for optimal combinations. While being more efficient than random sampling, these methods require a considerable amount of experiments to converge to good solutions. In this paper, we propose to use an information-theoretic active learning approach under the Bayesian framework of Gaussian processes to adaptively choose what experiments to perform based on current data points. We show that our approach is able to reduce the number of required data points significantly.