R-BERT-CNN: Drug-target interactions extraction from biomedical literature

• URL: http://arxiv.org/abs/2111.00611v1
• Date: Sun, 31 Oct 2021 22:50:33 GMT
• Title: R-BERT-CNN: Drug-target interactions extraction from biomedical literature
• Authors: Jehad Aldahdooh, Ziaurrehman Tanoli, Jing Tang
• Abstract summary: We present our participation for the DrugProt task BioCreative VII challenge. Drug-target interactions (DTIs) are critical for drug discovery and repurposing. There are >32M biomedical articles on PubMed and manually extracting DTIs from such a huge knowledge base is challenging.
• Score: 1.8814209805277506
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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