Learning to Rank Drug Combinations

Although recent evidence suggest that multicomponent therapy can be more effective in the treatment of complex diseases than single drug treatments, discovering such drug combinations remains a challenging and expensive task. We propose a machine learning approach for this problem. In our approach we use a relatively small set of drug combinations for which effectiveness has been assessed (perhaps experimentally) to train a kernel based model that can be used to rank other previously unseen drug combinations by their effectiveness. We present experimental results for the problem of selective killing of cells using synthetic data from a human apoptosis model. Our learning algorithm is able to produce very good rankings on unseen data, both in terms of producing mostly the correct ordering of drug combinations, and in terms of quality at the top of the list, that is, orderings that have most of the drug combinations with the largest scores at the top of the list. This is important in the case were the ranking algorithm is used to produce an ordered list of drug combinations that is in turn tested experimentally, as this would allow the experimental effort to concentrate on drug combinations at the top of the list.