  Traditional biomedical version of embeddings obtained from pre-trained
language models have recently shown state-of-the-art results for relation
extraction (RE) tasks in the medical domain. In this paper, we explore how to
incorporate domain knowledge, available in the form of molecular structure of
drugs, for predicting Drug-Drug Interaction from textual corpus. We propose a
method, BERTChem-DDI, to efficiently combine drug embeddings obtained from the
rich chemical structure of drugs along with off-the-shelf domain-specific
BioBERT embedding-based RE architecture. Experiments conducted on the
DDIExtraction 2013 corpus clearly indicate that this strategy improves other
strong baselines architectures by 3.4\% macro F1-score.
