# A Message Passing Approach to Biomedical Relation Classification for Drug-Drug Interactions
## 4 Conclusions  
In this paper, we propose a Knowledge Graph schema in a Graph Neural Network-based architecture for the classification of Drug-Drug Interactions from biomedical literature, which achieves state-of-the-art performance. Specifically, we presented a Graph Isomorphism Network-based architecture with message passing and non-message passing units that leverage the proposed DDI-specific graph structure that models the knowledge (drug identifiers, names, types and interactions) from the DDI corpus. Token and sentence embeddings are generated for the drug named entities and sentences, respectively, and are passed to the graph, populating the Token and Sentence nodes, taking advantage of the underlying BERT-based LM.  
Although our approach achieves state-of-the-art performance in the DDI classification task, the experimental results show that the individual class scores are greatly affected by the underlying LM, indicating that further improvements can be achieved. Based on the  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Sentence**} \\ \hline \multirow{2}{*}{**S1**} & Other drugs which may enhance the neuromuscular blocking action of **nondepolarizing** \\  & **agents** such as **MIVACRON** include certain **antibiotics** e.g., _antibiotics_group_). \\ \hline \multirow{2}{*}{**S2**} & **Thalidomide\({}_{\text{e0}}\)** has been reported to enhance the sedative activity of **b****b  
results, future work could be directed towards exploring a combination of general and domain-specific corpora to pre-train or domain-adapt a LM to further improve the performance of each positive class. Another direction for future work is to extend our approach with multi-task learning for extracting the entities in combination with interactions that could potentially improve generalization by using the domain information contained in the training signals of the related tasks as an inductive bias.  
Conceptualization, D.Z.; methodology, D.Z. and C.K.; formal analysis, D.Z. and C.K.; investigation, D.Z. and C.K.; software, D.Z. and C.K.; resources, D.Z.; validation, D.Z. and C.K.; writing--original draft preparation, D.Z.; writing--review and editing, D.Z., C.K. and I.V.; visualization, D.Z. and C.K.; supervision, D.Z. and I.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable  
The DDI Extraction 2013 corpus is available at [[https://github.com/isegura/DDICorpus](https://github.com/isegura/DDICorpus)]([https://github.com/isegura/DDICorpus](https://github.com/isegura/DDICorpus)), (accessed on 25 October 2022).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} DDI & Drug-Drug Interaction \\ NN & Neural Networks \\ GNN & Graph Neural Network \\ GCN & Graph Convolutional Network \\ RGNN & Relational Graph Neural Network \\ KG & Knowledge Graphs \\ ML & Machine Learning \\ RDF & Resource Description Framework \\ RE & Relation(ship) Extraction \\ PoS & Part-of-Speech \\ CNN & Convolutional Neural Network \\ RNN & Recurrent Neural Network \\ LSTM & Long Short-Term Memory \\ BiLSTM & Bidirectional Long Short-Term Memory \\ GRU & Gated Recurrent Unit \\ DGF & Dependency Graph Features \\ XML & Extensible Markup Language \\ LM & Language Model \\ SLDA & Sentence Level Domain Adapted \\ DLDA & Document Level Domain Adapted \\ GIN & Graph Isomorphism Network \\ MLP & Multi-Layer Perceptron \\ DNR & Drug Name Replacement \\ NIF & Negative Instance Filtering \\ CPU & Central Processing Unit \\ \end{tabular}