Relation extraction is frequently and successfully addressed by machine learning methods. The downside of this approach is the need

for annotated training data, typically generated in tedious manual, cost intensive work. Distantly supervised approaches make use of

weakly annotated data, which can be derived automatically. Recent work in the biomedical domain has applied distant supervision for

protein-protein interaction (PPI) with reasonable results, by employing the IntAct database. Training from distantly labeled corpora is

more challenging than from manually curated ones, as such data is inherently noisy. With this paper, we make two corpora publicly

available to the community to allow for comparison of different methods that deal with the noise in a uniform setting. The first corpus is

addressing protein-protein interaction (PPI), based on named entity recognition and the use of IntAct and KUPS databases, the second

is concerned with drug-drug interaction (DDI), making use of the database DrugBank. Both corpora are in addition labeled with 5

state-of-the-art classifiers trained on annotated data, to allow for development of filter methods. Furthermore, we present in short our

approach and results for distant supervision on these corpora as a strong baseline for future research.