  Combination therapies have become the standard of care for diseases such as
cancer, tuberculosis, malaria and HIV. However, the combinatorial set of
available multi-drug treatments creates a challenge in identifying effective
combination therapies available in a situation. To assist medical professionals
in identifying beneficial drug-combinations, we construct an expert-annotated
dataset for extracting information about the efficacy of drug combinations from
the scientific literature. Beyond its practical utility, the dataset also
presents a unique NLP challenge, as the first relation extraction dataset
consisting of variable-length relations. Furthermore, the relations in this
dataset predominantly require language understanding beyond the sentence level,
adding to the challenge of this task. We provide a promising baseline model and
identify clear areas for further improvement. We release our dataset, code, and
baseline models publicly to encourage the NLP community to participate in this
task.
