  Background: The problem of predicting whether a drug combination of arbitrary
orders is likely to induce adverse drug reactions is considered in this
manuscript. Methods: Novel kernels over drug combinations of arbitrary orders
are developed within support vector machines for the prediction. Graph matching
methods are used in the novel kernels to measure the similarities among drug
combinations, in which drug co-medication patterns are leveraged to measure
single drug similarities. Results: The experimental results on a real-world
dataset demonstrated that the new kernels achieve an area under the curve (AUC)
value 0.912 for the prediction problem. Conclusions: The new methods with drug
co-medication based single drug similarities can accurately predict whether a
drug combination is likely to induce adverse drug reactions of interest.
  Keywords: drug-drug interaction prediction; drug combination similarity;
co-medication; graph matching
