In the context of pharmaceuticals, drug-drug interactions (DDIs) occur when two or more drugs interact, potentially altering the intended effects of the drugs and resulting in adverse patient health outcomes. Therefore, it is essential to identify and comprehend these interactions. In recent years, an increasing number of novel compounds have been discovered, resulting in the discovery of numerous new DDIs. There is a need for effective methods to extract and analyze DDIs, as the majority of this information is still predominantly located in biomedical articles and sources. Despite the development of various techniques, accurately predicting DDIs remains a significant challenge. This paper proposes a novel solution to this problem by leveraging the power of Relation BioBERT (R-BioBERT) to detect and classify DDIs and the Bidirectional Long Short-Term Memory (BLSTM) to improve the accuracy of predictions. In addition to determining whether two drugs interact, the proposed method also identifies the specific types of interactions between them. Results show that the use of BLSTM leads to significantly higher F-scores compared to our baseline model, as demonstrated on three well-known DDI extraction datasets that includes SemEval 2013, TAC 2018, and TAC 2019.

Drug-drug interactions (DDIs) happen when two or more drugs interact. DDIs may change the effect of drugs in the body which can induce adverse effects and severe diseases for patients. As a result, detecting the interaction between drugs is essential. In the last years, many new DDIs have been found and added to the medical datasets with the rise in the number of discovered drugs. On the other hand, since a lot of this information is still in Biomedical articles and sources, there is a need for a method to extract DDIs information. However, despite the development of many techniques, attaining good prediction accuracy is the main issue. This paper proposes a deep learning approach that: 1) uses the power of Relation BioBERT (R-BioBERT) to detect and classify the DDIs and 2) employs the Bidirectional Long-Short Term Memory (BLSTM) to further increase the prediction quality. Not only does this paper study whether the two drugs have an interaction or not, but it also studies specific types of interactions between drugs. The paper also provides that using BLSTM can significantly increase the F-scores compared to the baseline model on the famous SemEval 2013, TAC 2018, and TAC 2019 DDI Extraction datasets.