Prediction of synergistic drug combinations

Given the large number of possible drug combinations, computational methods to prioritize the most effective treatments are critical. Here, we present four methodologies for predicting synergistic drug interactions. Mechanism based synergy prediction utilizes well-characterized biological data to predict drug interactions based on drug target interactions. Guilt by association methods predict novel interactions based on similarity to compounds with known interactions. A frequentist approach uses a drug's known tendency to exhibit drug interactions. Compound descriptor array based methods use machine learning approaches to relate compound interactions with arrays of observations regarding a compound. The increasing success of drug synergy prediction methods offer a means toward designing rational drug combinations.