  In this research, we present our work participation for the DrugProt task of
BioCreative VII challenge. Drug-target interactions (DTIs) are critical for
drug discovery and repurposing, which are often manually extracted from the
experimental articles. There are >32M biomedical articles on PubMed and
manually extracting DTIs from such a huge knowledge base is challenging. To
solve this issue, we provide a solution for Track 1, which aims to extract 10
types of interactions between drug and protein entities. We applied an Ensemble
Classifier model that combines BioMed-RoBERTa, a state of art language model,
with Convolutional Neural Networks (CNN) to extract these relations. Despite
the class imbalances in the BioCreative VII DrugProt test corpus, our model
achieves a good performance compared to the average of other submissions in the
challenge, with the micro F1 score of 55.67% (and 63% on BioCreative VI
ChemProt test corpus). The results show the potential of deep learning in
extracting various types of DTIs.
