# A Message Passing Approach to Biomedical Relation Classification for Drug-Drug Interactions
###### Abstract  
The task of extracting drug entities and possible interactions between drug pairings is known as Drug-Drug Interaction (DDI) extraction. Computer-assisted DDI extraction with Machine Learning techniques can help streamline this expensive and time-consuming process during the drug development cycle. Over the years, a variety of both traditional and Neural Network-based techniques for the extraction of DDIs have been proposed. Despite the introduction of several successful strategies, obtaining high classification accuracy is still an area where further progress can be made. In this work, we present a novel Knowledge Graph (KG) based approach that utilizes a unique graph structure in combination with a Transformer-based Language Model and Graph Neural Networks to classify DDIs from biomedical literature. The KG is constructed to model the knowledge of the DDI Extraction 2013 benchmark dataset, without the inclusion of additional external information sources. Each drug pair is classified based on the context of the sentence it was found in, by utilizing transfer knowledge in the form of semantic representations from domain-adapted BioBERT weights that serve as the initial KG states. The proposed approach was evaluated on the DDI classification task of the same dataset and achieved a F1-score of 79.14% on the four positive classes, outperforming the current state-of-the-art approach. Drug-Drug Interactions; transformers; graph neural networks; language models; relation classification; domain-adaption +
Footnote †: 2022: _Applied_
information that is very diverse in terms of type, format, level of detail and differ in terms of expressiveness and semantics. Additionally, the possibility of conflicting or outdated information presents among the various sources adds to the overall complexity regarding the collection, storage and analysis of the data and consequently the extraction and exploitation of the hidden wealth of information. DDI extraction from textual corpora is a traditional Relationship Extraction (RE) task in Machine Learning (ML) that aims to classify the interaction between drug entities [4] into specific predefined categories. Related DDI extraction studies vary based on the underlying task they aim to tackle and could be divided into pattern-based, traditional machine learning-based and deep learning-based [5]. The DDI classification task focuses on classifying the interactions between drug pairs by using gold entities with Relationship Classification (RC) techniques and are evaluated on the DDI Extraction 2013 corpus, which is considered as the benchmark dataset [6]. Similar to all underlying extraction tasks, the Deep Learning-based (DL) methods achieve the best performance and advance the state-of-the-art research in this field. Early DL-based approaches mainly utilized Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) as their base architectures to learn better task-specific representations with the use of contextualized information incorporated into their Neural Network-based architectures. [PERSON] et al. [7] introduced the first CNN-based approach for the DDI task, focusing on local sentence information without defining additional features generated by Natural Language Processing (NLP) toolkits. They applied a convolutional layer that takes the input from a look-up table constructed from word and position embeddings, leveraging the neural networks ability to automatically learn features. A max pooling layer then extracts the most important feature from each feature vector before finally classifying the interactions into one of the five classes using a softmax layer. The reported results show that the position embeddings improve the classification performance but face challenges due to the different position distribution on the test set. Similarly, [PERSON] et al. [8] integrated multiple word embeddings in their proposed MCCNN model, to tackle the vocabulary gap, the integration of semantic information and the manual feature selection in the DDI extraction task.